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  • 1.
    Almlof, Erik
    et al.
    KTH Royal Inst Technol.
    Zhao, Xiaoyun
    Dalarna University, School of Information and Engineering, Microdata Analysis. KTH Royal Inst Technol.
    Pernestal, Anna
    KTH Royal Inst Technol.
    Jenelius, Erik
    KTH Royal Inst Technol, Div Transport Planning, Stockholm, Sweden..
    Nybacka, Mikael
    KTH Royal Inst Technol.
    Frameworks for assessing societal impacts of automated driving technology2022In: Transportation planning and technology (Print), ISSN 0308-1060, E-ISSN 1029-0354, Vol. 45, no 7, p. 545-572Article in journal (Refereed)
    Abstract [en]

    Numerous studies have studied the impacts of automated driving (AD) technology on e.g. accident rates or CO2 emissions using various frameworks. In this paper we present an overview of previous frameworks used for societal impacts and review their advantages and limitations. Additionally, we introduce the Total Impact Assessment (TIA) framework developed by the Swedish Transport Administration and use this framework to evaluate three scenarios for AD bus services in Stockholm. We conclude that the reviewed frameworks cover different aspects of AD technology, and that e.g. cybersecurity and biodiversity are areas largely neglected. Furthermore, most frameworks assume effects to be homogenous, when there may be large variation in e.g. perceived security. The TIA framework does not manage to include all societal aspects of AD technology, but has great benefits and manages to provide important insights of the societal impacts of AD technology, especially how effects may wary for different actors.

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  • 2. Ardestani, Seyed Faraz Mahdavi
    et al.
    Adibi, Sasan
    Golshan, Arman
    Dalarna University.
    Sadeghian, Paria
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Factors Influencing the Effectiveness of E-Learning in Healthcare: A Fuzzy ANP Study2023In: Healthcare, E-ISSN 2227-9032, Vol. 11, no 14, article id 2035Article in journal (Refereed)
    Abstract [en]

    E-learning has transformed the healthcare education system by providing healthcare professionals with training and development opportunities, regardless of their location. However, healthcare professionals in remote or rural areas face challenges such as limited access to educational resources, lack of reliable internet connectivity, geographical isolation, and limited availability of specialized training programs and instructors. These challenges hinder their access to e-learning opportunities and impede their professional development. To address this issue, a study was conducted to identify the factors that influence the effectiveness of e-learning in healthcare. A literature review was conducted, and two questionnaires were distributed to e-learning experts to assess primary variables and identify the most significant factor. The Fuzzy Analytic Network Process (Fuzzy ANP) was used to identify the importance of selected factors. The study found that success, satisfaction, availability, effectiveness, readability, and engagement are the main components ranked in order of importance. Success was identified as the most significant factor. The study results highlight the benefits of e-learning in healthcare, including increased accessibility, interactivity, flexibility, knowledge management, and cost efficiency. E-learning offers a solution to the challenges of professional development faced by healthcare professionals in remote or rural areas. The study provides insights into the factors that influence the effectiveness of e-learning in healthcare and can guide the development of future e-learning programs.

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  • 3. Bai, W.
    et al.
    Johanson, Martin
    Dalarna University, School of Culture and Society, Business Administration and Management. Uppsala University.
    Oliveira, Luis
    Dalarna University, School of Culture and Society, Business Administration and Management. Dalarna University, School of Information and Engineering, Microdata Analysis. Center for International Business Studies, Fundação Getúlio Vargas, Brazil.
    Ratajczak-Mrozek, M.
    Francioni, B.
    Where business networks and institutions meet: Internationalization decision-making under uncertainty2022In: Journal of International Management, ISSN 1075-4253, E-ISSN 1873-0620, Vol. 28, no 1, article id 100904Article in journal (Refereed)
    Abstract [en]

    Both business networks and institutional forces are relevant to firm internationalization but they have seldom been studied together. We investigate under what circumstances firms are more likely to adopt non-predictive strategy in light of the influence of the business networks, the institutional forces, and the home market background affecting their internationalization. Based on survey data from 758 small and medium-sized enterprises (SMEs) from Brazil, China, Poland, Italy, and Sweden, our results support the effects of formal institutional distance and cultural differences on the use of non-predictive strategies by SMEs in internationalization decisions, as well as the contingency effects of business network stability and of having an emerging market background. We integrate research on the liability of foreignness and the liability of outsidership and find that business network stability is critical. It does not moderate the relation between cultural difference and NPS adoption but attenuates the negative relation between institutional distance and NPS adoption, indicating that the liabilities of foreignness and outsidership play different roles in internationalization. © 2021 The Author(s)

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  • 4.
    Bai, Wensong
    et al.
    Dalarna University, School of Culture and Society, Business Administration and Management. Zhejiang University of Technology, Hangzhou, China; Uppsala University.
    Hilmersson, M.
    Johanson, Martin
    Dalarna University, School of Culture and Society, Business Administration and Management. Uppsala University.
    Oliveira, Luis
    Dalarna University, School of Culture and Society, Business Administration and Management. Dalarna University, School of Information and Engineering, Microdata Analysis.
    SMEs' regional diversification: dynamic patterns and home market institutional determinants2023In: International Marketing Review, ISSN 0265-1335, E-ISSN 1758-6763Article in journal (Refereed)
    Abstract [en]

    Purpose: The authors seek to advance the understanding of small- and medium-sized enterprise (SME) internationalization at the regional level and examine the role of home market institutions in this process. Design/methodology/approach: The authors analyze hypotheses with data from SMEs in five country markets and from the Global Entrepreneurship Monitor. A cluster analysis establishes the regional diversification patterns (based on regional diversification scope, speed and rhythm) and a multinomial regression tests the effect of home market institutions on their adoption. Findings: The results offer a refined picture of SME regional diversification by revealing three patterns: intra-regionally focused firms, late inter-region diversifiers and early inter-region diversifiers. They also suggest that the adoption of these patterns is determined by SMEs' home market institutions. Originality/value: The authors develop a nuanced understanding of SME internationalization by building upon and expanding the regionalization rationale in the internationalization patterns literature. Additionally, the authors address the acknowledged, yet rarely investigated, country-level determinants of internationalization patterns. © 2023, Emerald Publishing Limited.

  • 5.
    Bai, Wensong
    et al.
    Dalarna University, School of Culture and Society, Business Administration and Management. Zhejiang University of Technology, China; Uppsala University.
    Johanson, Martin
    Dalarna University, School of Culture and Society, Business Administration and Management. Uppsala University; University of Huddersfield, UK.
    Oliveira, Luis
    Dalarna University, School of Culture and Society, Business Administration and Management. Dalarna University, School of Information and Engineering, Microdata Analysis. Center for International Business Studies, Fundação Getúlio Vargas, Brazil.
    Ratajczak-Mrozek, Milena
    Poznan University.
    The Role of Business and Social Networks in the Effectual Internationalization: Insights from Emerging Market SMEs2021In: Journal of Business Research, ISSN 0148-2963, E-ISSN 1873-7978, Vol. 129, p. 96-109Article in journal (Refereed)
    Abstract [en]

    This study investigates the performance implications of the distinct mechanisms represented by business and social networks in the effectual internationalization. Our hypotheses consider the influence of both network types on firms’ decision-making during internationalization, including the use of effectuation’s overarching principle of non-predictive strategy and the analysis of affordable losses as preferred criterion for selecting between action paths. We test our structural model on a sample of 469 SMEs from Brazil, China, and Poland. The analysis demonstrates that the knowledge circulating in the firms’ business networks negatively moderates the relationship between non-predictive strategy and affordable losses, while social networking mediates the relationships between both non-predictive strategy and affordable losses, on the one hand, and international performance, on the other.

  • 6.
    Bhat, Adhyapadi Apoorva
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Koothenparambil Joy, Jomin
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Fault Detection in PV System using Machine Learning Technique2023Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    With the steady and rapid reliance on solar power as a viable alternative to traditional fuel-based energy, maintenance of solar panels is becoming an unavoidable issue for both producers and consumers. Machine learning techniques are useful in detecting solar panel faults and their life span. In recent years, Machine learning technology has emerged that helps to extract meaningful information and detect the fault in PV Systems. This paper reviews and involves identifying faulty features and predicting the fault in residential PV Systems that causes power degradation. We have built a linear regression model and performed hierarchical clustering to identify the faulty group of data, and from that faulty group, we identified that the features such as Radiation, Module Temperature, and IS values play an important role in the degradation of the power generation in the solar panels. Additionally, in this study fault prediction in a PV system has also been attempted. We evaluated the performance using 6 different models SVM, KNN, Naive Bayes Random Forest, Decision Tree and Logistic Regression. Finally, we concluded that the Random Forest, KNN and Decision Tree performed better in predicting with an accuracy of 99 %.

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  • 7. Bodell, Victor
    et al.
    Ekstrom, Lukas
    Aghanavesi, Somayeh
    Dalarna University, School of Information and Engineering, Microdata Analysis. KTH, Royal Institute of Technology, Stockholm.
    Comparing Machine Learning Estimation of Fuel Consumption of Heavy-Duty Vehicles2021In: World Academy of Science, Engineering and Technology: An International Journal of Science, Engineering and Technology, ISSN 2010-376X, Vol. 15, no 2, p. 97-101, article id 10011850Article in journal (Refereed)
    Abstract [en]

    Fuel consumption (FC) is one of the key factors indetermining expenses of operating a heavy-duty vehicle. A customermay therefore request an estimate of the FC of a desired vehicle.The modular design of heavy-duty vehicles allows their constructionby specifying the building blocks, such as gear box, engine andchassis type. If the combination of building blocks is unprecedented,it is unfeasible to measure the FC, since this would first r equire theconstruction of the vehicle. This paper proposes a machine learningapproach to predict FC. This study uses around 40,000 vehiclesspecific a nd o perational e nvironmental c onditions i nformation, suchas road slopes and driver profiles. A ll v ehicles h ave d iesel enginesand a mileage of more than 20,000 km. The data is used to investigatethe accuracy of machine learning algorithms Linear regression (LR),K-nearest neighbor (KNN) and Artificial n eural n etworks ( ANN) inpredicting fuel consumption for heavy-duty vehicles. Performance ofthe algorithms is evaluated by reporting the prediction error on bothsimulated data and operational measurements. The performance of thealgorithms is compared using nested cross-validation and statisticalhypothesis testing. The statistical evaluation procedure finds thatANNs have the lowest prediction error compared to LR and KNNin estimating fuel consumption on both simulated and operationaldata. The models have a mean relative prediction error of 0.3% onsimulated data, and 4.2% on operational data.

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  • 8. Bohm, Clifford
    et al.
    Hintze, Arend
    Dalarna University, School of Information and Engineering, Microdata Analysis. Michigan State University, USA.
    Schossau, Jory
    A Simple Sparsity Function to Promote Evolutionary Search2023In: ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference, 2023, p. 368-376Conference paper (Refereed)
  • 9.
    Bohm, Clifford
    et al.
    Michigan State University, Department of Integrative Biology and BEACON Center for the Study of Evolution in Action, East Lansing, U.S.A..
    Kirkpatrick, Douglas
    Michigan State University, BEACON Center for the Study of Evolution in Action and Department of Computer Science and Engineering, East Lansing, U.S.A.
    Hintze, Arend
    Dalarna University, School of Information and Engineering, Microdata Analysis. Michigan State University, BEACON Center for the Study of Evolution in Action, East Lansing, U.S.A..
    Understanding Memories of the Past in the Context of Different Complex Neural Network Architectures.2022In: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 34, no 3, p. 754-780Article in journal (Refereed)
    Abstract [en]

    Deep learning (primarily using backpropagation) and neuroevolution are the preeminent methods of optimizing artificial neural networks. However, they often create black boxes that are as hard to understand as the natural brains they seek to mimic. Previous work has identified an information-theoretic tool, referred to as R, which allows us to quantify and identify mental representations in artificial cognitive systems. The use of such measures has allowed us to make previous black boxes more transparent. Here we extend R to not only identify where complex computational systems store memory about their environment but also to differentiate between different time points in the past. We show how this extended measure can identify the location of memory related to past experiences in neural networks optimized by deep learning as well as a genetic algorithm.

  • 10. Cajas, V.
    et al.
    Urbieta, M.
    Rossi, G.
    Rybarczyk, Yves
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Migrating legacy Web applications2021In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543, Vol. 24, no 2, p. 1033-1049Article in journal (Refereed)
  • 11.
    Carling, Kenneth
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    The emergence of Microdata Analysis and its intellectual history over the past two decades2021Report (Other academic)
    Abstract [en]

    By 2020, students of Dalarna University had produced some 100 Bachelor and Master’s theses and 25 Licentiate or Doctoral theses in the academic discipline of Microdata Analysis guided by the university’s faculty. While firmly rooted in the tradition and the format of the formal sciences Computer Science and Mathematics, the theses are disparate with regard to area of investigation, research method, and epistemology. The research carried out in these theses is recognized internationally by learned societies and their journals and conferences, yet Dalarna University remains globally unique in labelling an academic discipline Microdata Analysis. This paper attempts to narrate the history of the forming process of Microdata Analysis at the university and grasp its nature.

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  • 12.
    Carling, Kenneth
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Privacy and data: Some research venues2021In: A Research Agenda for Knowledge Management and Analytics, Edward Elgar Publishing, 2021, p. 191-203Chapter in book (Refereed)
    Abstract [en]

    The issue of data privacy is often reduced to secure data transactions by cryptographic techniques. However, in a liberal democracy the issue of privacy connects to fundamental questions about the co-existence and collaboration between its citizens. One is the conflict between self-interest and the interest of the commons, whereby research on privacy topics is found in distant and disparate research streams. Sharing of data perceived as private may drastically increase collective welfare, while reducing it for single citizens. In this chapter, we present a metaphor to highlight the fundamentals of privacy and explain how the access to new data-processing technologies provokes new questions to be addressed. Furthermore, we illustrate how various research streams differ in presumptions and privacy topics of interest, and we stress the potential knowledge-producing value of bridging these streams. We end by pointing out some particularly interesting research venues for privacy and data. © Jay Liebowitz 2021.

  • 13.
    Dar, Ravi
    et al.
    Dalarna University, School of Culture and Society, Business Administration and Management.
    Huq, Asif M
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Sundberg, Klas
    Dalarna University, School of Culture and Society, Business Administration and Management.
    Pricing for 55: Implementing corporate climate strategy in choice architecture through internal pricing2021Conference paper (Other academic)
    Abstract [en]

    The job of management accounting and management control is not only to gather data/information about the company’s or organization’s past, but also to use and develop tools to form the future. While some tools are simple cost calculations to find breakeven points in price-setting, other tools can include far-reaching strategic purposes. Strategy decisions within business that focus on sustainability and mitigate climate change generally tend to focus on specific components or activities (even in the supply change) to make replacements or even cancel altogether. Onus is on the big decisions with major impact. There is another perception of strategy: practices through which strategy is enacted. Less as a grand plan by the top management, and more as a gradual managing that takes key decisions close to the everyday of business life.  With the global emergency of mitigating climate change, an area of near-untapped potential is management accounting. Research has pointed to the importance of middle level management and the role of the controller, where change happens, and problems need solutions. Recently there has been an increased interest in the way internal pricing (which includes transfer pricing) within a corporation can support a corporate strategy on climate change mitigation. Traditionally seen mainly as a manipulative method to avoid tax in multinational companies, there is new interest on the possibility of creating price mechanisms within companies that mimic the supply-demand curves of market economies, for example ways that cap-and-trade mechanisms on GHG emissions can be utilized inside corporations. The concept that underlies market price-setting is scarcity, but rather than using market price as a reflection of scarcity, the interest is in using corporate control and accounting devices to create situations of scarcity. This would create an accounted price on specific goods and services that reflects strategic concerns and a way of implementing corporate policies on climate change into the ‘nitty-gritty’ of everyday business decisions has choices between different burdens on the climate. It is our suggestion that similar mechanisms could broadly support and serve companies’ transition into a circular economy through the design of business choices. The concept of “choice architecture” from  behavorial economics with influence from marketing, most notably with the term ‘nudging’ in order to promote individual choices through design and gradually form norms in a way that would be beneficial to the environment. We want to make the point that the perception of strategizing ties in well with the ideas both of choice architectures and of implementing a circular economy policy in a business. If the concept (or ideal) of the circular economy is a promotion of resource re-use that can reach levels of complete or near-complete containment, then the switch from a linear perspective (of reducing waste, emissions and of not depleting finite resources) to a circular perspective means that ‘only’ megaprojects for re-use and production with lower energy consumption are not sufficient. The realization of the need for re-using spent materials and an avoidance of virgin resources has to permeate the organization. A first step is to review existing survey research of internal pricing that deviate from a cost accounting mandate and instead implement strategic ambitions and outcomes, and among such efforts, focus on those that can support business development towards GHG emission mitigation and circular thinking. 

  • 14. Davari, Mahtab
    et al.
    Edward Okon, Charles
    Aghanavesi, Somayeh
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Predictive Analytics of Student Performance Determinants in Education2022In: International Journal of Educational and Pedagogical Sciences, E-ISSN 1307-6892, Vol. 16, p. 716-721, article id 10012800Article in journal (Refereed)
    Abstract [en]

    Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest, Decision Tree, KNearestNeighbors, Linear Discriminant Analysis (LDA), and quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM(using a linear kernel), LDA, and LR were identified as the best-performing machine-learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

  • 15. Dong, Bing
    et al.
    Liu, Yapan
    Fontenot, Hannah
    Ouf, Mohamed
    Osman, Mohamed
    Chong, Adrian
    Qin, Shuxu
    Han, Mengjie
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    Carlucci, Salvatore
    Occupant behavior modeling methods for resilient building design,operation and policy at urban scale: a review2021In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 293, article id 116856Article in journal (Refereed)
  • 16. Fjellström, D.
    et al.
    Bai, W.
    Oliveira, Luis
    Dalarna University, School of Culture and Society, Business Administration and Management. Dalarna University, School of Information and Engineering, Microdata Analysis.
    Fang, T.
    Springboard internationalisation in times of geopolitical tensions2023In: International Business Review, ISSN 0969-5931, E-ISSN 1873-6149, Vol. 32, no 6, article id 102144Article in journal (Refereed)
    Abstract [en]

    Geopolitical tensions and a world where state interventions are driven by national security and ideology present novel challenges for emerging market multinational enterprises (EMNEs). Often, individual companies are targeted, and their corporate growth gets curbed. These phenomena are derived from non-market factors, which are generally absent in the springboard view of the international business discourse that explains the foreign expansion of EMNEs by viewing these firms as ambidextrous organisations capable of handling conflicting requirements. This research aims to understand the international expansion of EMNEs under geopolitical tensions by incorporating non-market factors into the ambidexterity model to enrich the springboard view. A case study of Huawei and its exclusion from the telecommunications industry in Sweden forms the empirical base of this research. The contributions are twofold. First, within the springboard view, the ambidexterity model can be upgraded by incorporating non-market factors that better explain the international expansion of EMNEs in changing geopolitical and business contexts. Second, the research highlights the management of EMNEs' subsidiaries while considering geopolitical tensions. © 2023 Elsevier Ltd

  • 17. Halabi, Ramzi
    et al.
    Hintze, Arend
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Ortiz, Abigail
    Multi-resolution Time-frequency Spectral Derivative Spike Detection for Episode Onset Detection using Passively Collected Sensor Data2023Manuscript (preprint) (Other academic)
  • 18.
    Han, Mengjie
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Canli, Ilkim
    Department of Architecture, Middle East Technical University, Ankara 06800, Türkiye;Center for Solar Energy Research and Applications (ODTÜ-GÜNAM), Middle East Technical University, Ankara 06800, Türkiye.
    Shah, Juveria
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    Dino, Ipek Gursel
    Department of Architecture, Middle East Technical University, Ankara 06800, Türkiye;METU Robotics and AI Technologies Application and Research Center (METU-ROMER), Middle East Technical University (METU), Ankara 06800, Türkiye.
    Kalkan, Sinan
    METU Robotics and AI Technologies Application and Research Center (METU-ROMER), Middle East Technical University (METU), Ankara 06800, Türkiye;Department of Computer Engineering, Middle East Technical University, Ankara 06800, Türkiye.
    Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts2024In: Buildings, E-ISSN 2075-5309, Vol. 14, no 2, article id 371Article in journal (Refereed)
    Abstract [en]

    The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.

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  • 19.
    Han, Mengjie
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Johari, Fatemeh
    Uppsala University.
    Huang, Pei
    Dalarna University, School of Information and Engineering, Energy Technology.
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method2022In: Energy and Built Environment, ISSN 2666-1233Article in journal (Refereed)
    Abstract [en]

    Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region.

  • 20.
    Han, Mengjie
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    May, Ross
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    Reinforcement Learning Methodologies for Controlling Occupant Comfort in Buildings2021In: Data-driven Analytics for Sustainable Buildings and Cities, Switzerland: Springer, 2021, p. 179-205Chapter in book (Other academic)
  • 21.
    Han, Mengjie
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Shah, Juveria
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    Review of natural language processing techniques for characterizing positive energy districts2023In: journal of Physics; Conference series, Institute of Physics Publishing (IOPP), 2023, Vol. 2600, no 8, article id 082024Conference paper (Refereed)
    Abstract [en]

    The concept of Positive Energy Districts (PEDs) has emerged as a crucial aspect of endeavours aimed at accelerating the transition to zero carbon emissions and climate-neutral living spaces. The focus of research has shifted from energy-efficient individual buildings to entire districts, where the objective is to achieve a positive energy balance over a specific timeframe. The consensus on the conceptualization of a PED has been evolving and a standardized checklist for identifying and evaluating its constituent elements needs to be addressed. This study aims to develop a methodology for characterizing PEDs by leveraging natural language processing (NLP) techniques to model, extract, and map these elements. Furthermore, a review of state-of-the-art research papers is conducted to ascertain their contribution to assessing the effectiveness of NLP models. The findings indicate that NLP holds significant potential in modelling the majority of the identified elements across various domains. To establish a systematic framework for AI modelling, it is crucial to adopt approaches that integrate established and innovative techniques for PED characterization. Such an approach would enable a comprehensive and effective implementation of NLP within the context of PEDs, facilitating the creation of sustainable and resilient urban environments. © 2023 Institute of Physics Publishing. All rights reserved.

  • 22.
    Han, Mengjie
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Wang, Zhenwu
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm2021In: Buildings, E-ISSN 2075-5309, Vol. 11, no 1Article in journal (Refereed)
    Abstract [en]

    In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.

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  • 23.
    Han, Mengjie
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Zhao, Jing
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    Shen, Jingchun
    Dalarna University, School of Information and Engineering, Construction.
    Li, Yu
    The reinforcement learning method for occupant behavior in building control: A review2021In: Energy and Built Environment, ISSN 2666-1233, Vol. 2, no 2, p. 137-148Article in journal (Refereed)
    Abstract [en]

    Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment. For complex and dynamic environments, the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable. It is expected that they will make a substantial contribution to reducing global energy consumption. Among these control techniques, the reinforcement learning (RL) method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control. Fruitful algorithms complement each other and guarantee the quality of the optimization. However, the examination of occupant behavior based on reinforcement learning methodologies is not well established. The way that occupant interacts with the RL agent is still unclear. This study briefly reviews the empirical applications using reinforcement learning, how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.

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  • 24.
    Hintze, Arend
    Dalarna University, School of Information and Engineering, Microdata Analysis. BEACON Center for the Study of Evolution in Action, Michigan State University, USA .
    ChatGPT believes it is conscious2023Manuscript (preprint) (Other academic)
  • 25.
    Hintze, Arend
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    The Role Weights Play in Catastrophic Forgetting2021In: 2021 8TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE (ISCMI 2021), IEEE, 2021, p. 160-166Conference paper (Refereed)
    Abstract [en]

    Catastrophic forgetting is the sudden loss of performance when a neural network is trained on a new task or when experiencing unbalanced training. It often limits the ability of neural networks to learn new tasks. Previous work focused on the training data by changing the training regime, balancing the data, or replaying previous training episodes. Other methods used selective training to either allocate portions of the network to individual tasks or otherwise preserve prior task expertise. However, those approaches assume that network attractors are finely tuned, and even small changes to the weights cause misclassification. This fine-tuning is also believed to happen during overfitting and can be addressed with regularization. This paper introduces a method that quantifies how individual weights contribute to different tasks independent of weight strengths or previous training gradients. Applying this method reveals that backpropagation recruits all weights to contribute to a new task and that single weights may be somewhat more robust to noise than previously assumed.

  • 26.
    Hintze, Arend
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis. Michigan State University, East Lansing, MI, USA.
    Adami, Christoph
    Michigan State University, East Lansing, MI, USA; .
    Detecting Information Relays in Deep Neural Networks2023In: Entropy, E-ISSN 1099-4300, Vol. 25, no 3, article id 401Article in journal (Refereed)
    Abstract [en]

    Deep learning of artificial neural networks (ANNs) is creating highly functional processes that are, unfortunately, nearly as hard to interpret as their biological counterparts. Identification of functional modules in natural brains plays an important role in cognitive and neuroscience alike, and can be carried out using a wide range of technologies such as fMRI, EEG/ERP, MEG, or calcium imaging. However, we do not have such robust methods at our disposal when it comes to understanding functional modules in artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here, we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information IR. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to identify computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.

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  • 27.
    Hintze, Arend
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Adami, Christoph
    Neuroevolution gives rise to more focused information transfer compared to backpropagation in recurrent neural networks2022In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058Article in journal (Refereed)
    Abstract [en]

    Artificial neural networks (ANNs) are one of the most promising tools in the quest to develop general artificial intelligence. Their design was inspired by how neurons in natural brains connect and process, the only other substrate to harbor intelligence. Compared to biological brains that are sparsely connected and that form sparsely distributed representations, ANNs instead process information by connecting all nodes of one layer to all nodes of the next. In addition, modern ANNs are trained with backpropagation, while their natural counterparts have been optimized by natural evolution over eons. We study whether the training method influences how information propagates through the brain by measuring the transfer entropy, that is, the information that is transferred from one group of neurons to another. We find that while the distribution of connection weights in optimized networks is largely unaffected by the training method, neuroevolution leads to networks in which information transfer is significantly more focused on small groups of neurons (compared to those trained by backpropagation) while also being more robust to perturbations of the weights. We conclude that the specific attributes of a training method (local vs. global) can significantly affect how information is processed and relayed through the brain, even when the overall performance is similar.

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  • 28.
    Huang, Pei
    et al.
    Dalarna University, School of Information and Engineering, Energy Technology.
    Han, Mengjie
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    Hussain, Sayed Asad
    The University of British Columbia, Canada.
    Jayprakash Bhagat, Rohit
    Hogarehalli Kumar, Deepu
    Characterization and optimization of energy sharing performances in energy-sharing communities in Sweden, Canada and Germany2022In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 326, article id 120044Article in journal (Refereed)
    Abstract [en]

    Peer-to-peer (P2P) renewable power sharing within a building community is a promising solution to enhance the community's self-sufficiency and relieve the grid stress posed by the increased deployment of distributed renewable power. Existing studies have pointed out that the energy sharing potentials of a building community are affected by various factors including location, community scale, renewable energy system (RES) capacity, energy system type, storage integration, etc. However, the impacts of these factors on the energy sharing potentials in a building community are not fully studied. Being unaware of those factors’ impacts could lead to reduced energy sharing potentials and thus limit the associated improvement in energy and economic performances. Thus, this study conducts a comprehensive analysis of various factors’ impacts on the energy sharing performances in building communities. Two performance indicators are first proposed to quantify the energy sharing performances: total amount of energy sharing and energy sharing ratio (ESR). Then, parametric studies are conducted based on real electricity demand data in three countries to reveal how these factors affect the proposed indictors and improvements in self-sufficiency, electricity costs, and energy exchanges with the power grid. Next, a genetic algorithm based design method is developed to optimize the influential parameters to maximize the energy sharing potentials in a community. The study results show that the main influential factors are RES capacity ratio, PV capacity ratio, and energy storage system capacity. A large energy storage capacity can enhance the ESR. To achieve the maximized ESR, the optimal RES capacity ratio should be around 0.4 ∼ 1.1. The maximum energy sharing ratio is usually smaller in high latitude districts such as Sweden. This study characterizes the energy sharing performances and provides a novel perspective to optimize the design of energy systems in energy sharing communities. It can pave the way for the large integration of distributed renewable power in the future. © 2022 The Author(s)

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  • 29.
    Huang, Pei
    et al.
    Dalarna University, School of Information and Engineering, Energy Technology.
    Tu, Ran
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    Han, Mengjie
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Sun, Yongjun
    Hussain, Syed Asad
    Zhang, Linfeng
    Investigation of electric vehicle smart charging characteristics on the power regulation performance in solar powered building communities and battery degradation in Sweden2022In: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 56, p. 105907-105907, article id 105907Article in journal (Refereed)
  • 30.
    Huq, Asif M
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Corporate Disclosures Regulations: Social Solution or a Problem?2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Regulations are argued to have the answer to solving various social and economic problems that society faces today (e.g., climate change, tax evasion, etc.). However, regulations may instead become the problem (e.g., overregulation). The central research question of this doctoral thesis is “are corporate disclosures regulations a social solution or a problem?” 

    To answer the central research question, Papers I and II examine the economic effects of an EU-wide audit reform, the Annual Accounts Directive: 2013/34/EU, on firms and the society. Papers III, IV, and V examine firm behavior to assess the need for public regulation of nonfinancial reporting in the light of an EU-wide reform, the Nonfinancial Reporting Directive: 2014/95/EU, commonly known as the NFRD.

    The thesis posits that the current implementations of these reforms in some settings are imperfect and thus costly for the firms and society. It recommends deregulation of the monitoring of financial disclosure, i.e., to allow more small firms the option of deciding if an audit is beneficial for them or not. On the other hand, recommends a different approach for regulating nonfinancial reporting, e.g., sustainability reporting. For instance, regulations that can influence firms’ governance structure, e.g., board diversity. A firm with a diverse board is more likely to adopt a sustainability agenda which is better aligned with the expectations of the EU regulators. 

    Stakeholders use firms’ disclosures to evaluate its performance and behavior for various decision making. For example, shareholders, in their investing or divesting decisions; analysts, in making various forecasts and recommendations; or governments, in assessing the need for reforms. Historically, stakeholders commonly used financial information for these types of decision making. Hence, there are well established generic measures to evaluate firms’ financial information (e.g., earnings quality measures and financial-statement ratios). Nowadays, stakeholders are increasingly using firms’ sustainability related information in their decision-making process as well. However, replicable and scalable generic measures to evaluate such information are missing. This thesis develops objective approaches and a generic measure, to evaluate firms’ sustainability related disclosures. The developed approaches for analyzing unstructured text data may be applied to other fields that can benefit from the use of natural language processing tools.

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  • 31.
    Huq, Asif M
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis. Dalarna University.
    Carling, Kenneth
    Dalarna University, School of Information and Engineering, Microdata Analysis. Dalarna University.
    Measuring Accountable Information in CSR Reports: A New Operationalization and Analysis Applied to Greenhouse Gas Disclosures2024In: Journal of Emerging Technologies in Accounting, ISSN 1554-1908, E-ISSN 1558-7940, p. 1-30Article in journal (Refereed)
    Abstract [en]

    We develop a novel and generic text-based measure to classify and evaluate greenhouse gas (GHG) disclosures. We construct the measure using collocation analysis of GHG-related words and regular expressions. Automated implementation achieved high concordance compared to manual investigations. We move beyond the “bag-of-words” approach in classifying voluminous nonfinancial corporate disclosure. We also outline a methodology that is manyfold scalable and makes replicability straightforward. Compared to past studies, we work with a significantly larger sample of 5,017 reports across 80 countries, thereby dealing with greater complexity and leading to better generalizability. We also contribute to the debate on whether nonfinancial disclosures exhibit accountability or are merely greenwashing. We find a negative trend in accountability worldwide, and firm-level accountability in GHG disclosures is not detectable in a country-level reduction of GHG emissions. Moreover, firms disclose significantly higher accountable information in a civil-law legal environment compared to those in a common-law legal environment.

  • 32.
    Huq, Asif M
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Daunfeldt, Sven-Olov
    Dalarna University, School of Culture and Society, Economics. Institute of Retail Economics, Stockholm.
    Hartwig, Fredrik
    University of Gävle.
    Rudholm, Niklas
    Institute of Retail Economics, Stockholm.
    Free to Choose: Do Voluntary Audit Reforms Increase Employment Growth?2021In: International Journal of the Economics of Business, ISSN 1357-1516, E-ISSN 1466-1829, Vol. 28, no 1, p. 163-178Article in journal (Refereed)
  • 33.
    Huq, Asif M
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Hartwig, F.
    Rudholm, N.
    Do audited firms have a lower cost of debt?2022In: International Journal of Disclosure & Governance, ISSN 1741-3591, E-ISSN 1746-6539Article in journal (Refereed)
    Abstract [en]

    The purpose of this study is to investigate if audited financial statements add value for firms in the private debt market. Using an instrumental variable method, we find that firms with audited financial statements, on average, save 0.47 percentage points on the cost of debt compared to firms with unaudited financial statements. We also find that using the big, well-known auditing firms does not yield any additional cost of debt benefits. Lastly, we investigate if there are industries where alternative sources of information make auditing less valuable in reducing the cost of debt. Here, we find that auditing is less important in lowering cost in one industry, agriculture, where one lender has a 74% market share and a 100-year history of lending to firms within that industry. As such, it seems that lenders having high exposure to a certain industry might act as an alternative to auditing in reducing the information asymmetry between the firm and the lender. © 2021, The Author(s).

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  • 34.
    Huq, Asif M
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Sundberg, Klas
    Dalarna University, School of Culture and Society, Business Administration and Management.
    Friedman doctrine prevails! Or does it? Evidence from the views of practitioners on corporate sustainability in their firms2022Conference paper (Refereed)
  • 35.
    Huq, Asif M
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Sundberg, Klas
    Dalarna University, School of Culture and Society, Business Administration and Management.
    Friedman doctrine prevails! Or does it?: Evidence from the views of practitioners on corporate sustainability in their firms2021Report (Other academic)
    Abstract [en]

    The purpose of this paper is to provide insights on the views of firms regarding corporate sustainability (CS) and how the structure of the board affects this. We surveyed the CEOs, CFOs, and Environment Officers of about 850 Swedish firms (response rate 21%) affected by mandatory sustainability reporting after the implementation of the EU Directive 2014/95/EU. The six-transcending ambition levels (namely: pre-CS, compliance-driven, profit-driven, caring, synergistic, and holistic) of corporate sustainability proposed by van Marrewijk & Werre (2003) were used to classify the views of key officers on the sustainability agenda of their respective firms. We find that the drive by firms for higher CS ambition levels is positively influenced by a diverse board (i.e., representation of female board members), and the effect is more pronounced if the board is constituted with a female top executive. Moreover, younger top executives are more likely to have a higher CS ambition level. On the other hand, external CEOs, external board members, and forceful disclosure of sustainability activity (e.g., EU Directive 2014/95/EU) do not significantly influence CS ambition levels, whereas firm size and industry affiliation do. Our findings are useful for top managers and regulators interested in corporate governance issues and influencing the sustainability efforts of their firms. Methodologically, the use of a survey method is an extension to an otherwise high reliance on archival research in the field of CS. Furthermore, the dataset is unique, and the results are robust to various sensitivity analyses.

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  • 36.
    Huq, Asif M
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Zwilling, Moti
    Ariel University, Israel.
    Carling, Kenneth
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    ­­­­­Cyber Security Challenges and Opportunities in a Multi-A­­­gent Environment: The case of Swedish Transport Administration2022Report (Other academic)
    Abstract [en]

    Cyber risks are considered as one of the main challenges that harm technology, data, and privacy of individuals and organizations. While cyber criminals tend to use improved methods and tools to steal data or hack into systems, the ability of organizations to mitigate such risks tend to become more difficult. Especially when the organization and various other agents operate in a multi-agent environment that is often connected to the internet. This report highlights some of the most common and fundamental cyber threats and cyber security gaps or vulnerabilities which might be found in risk mitigation tools that are used for cyber defense in a multi-agent environment. The Swedish Transport Administration for example operates in such an environment. The study exhibits latest challenges and opportunities in the transport arena while focusing on unique and specific disciplines related to the Swedish Transport Sector. It suggests future applicative research studies that will yield recommendations to cyber defense managers in the transport sector whose one of the major tasks is to perform mitigation of cyber risks. The study employed a structured literature review methodology drawing on existing scientific scholarship to prepare the report. 

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  • 37. Incorvaia, Darren C.
    et al.
    Hintze, Arend
    Dalarna University, School of Information and Engineering, Microdata Analysis. Michigan State Univ.
    Dyer, Fred C.
    Spatial allocation without spatial recruitment in bumblebees2021In: Behavioral Ecology, ISSN 1045-2249, E-ISSN 1465-7279, Vol. 32, no 2, p. 265-276Article in journal (Refereed)
    Abstract [en]

    Any foraging animal is expected to allocate its efforts among resource patches that vary in quality across time and space. For social insects, this problem is shifted to the colony level: the task of allocating foraging workers to the best patches currently available. To deal with this task, honeybees rely upon differential recruitment via the dance language, while some ants use differential recruitment on odor trails. Bumblebees, close relatives of honeybees, should also benefit from optimizing spatial allocation but lack any targeted recruitment system. How bumblebees solve this problem is thus of immense interest to evolutionary biologists studying collective behavior. It has been thought that bumblebees could solve the spatial allocation problem by relying on the summed individual decisions of foragers, who occasionally sample and shift to alternative resources. We use field experiments to test the hypothesis that bumblebees augment individual exploration with social information. Specifically, we provide behavioral evidence that, when higher-concentration sucrose arrives at the nest, employed foragers abandon their patches to begin searching for the better option; they are more likely to accept novel resources if they match the quality of the sucrose solution experienced in the nest. We explored this strategy further by building an agent-based model of bumblebee foraging. This model supports the hypothesis that using social information to inform search decisions is advantageous over individual search alone. Our results show that bumblebees use a collective foraging strategy built on social modulation of individual decisions, providing further insight into the evolution of collective behavior.

  • 38. Jin, Yuan
    et al.
    Yan, Da
    Zhang, Xingxing
    Dalarna University, School of Information and Engineering, Energy Technology.
    An, Jingjing
    Han, Mengjie
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development2021In: Building Simulation, ISSN 1996-3599, E-ISSN 1996-8744, Vol. 14, p. 219-235Article in journal (Refereed)
  • 39.
    Johanson, Martin
    et al.
    Dalarna University, School of Culture and Society, Business Administration and Management.
    Oliveira, Luis
    Dalarna University, School of Culture and Society, Business Administration and Management. Dalarna University, School of Information and Engineering, Microdata Analysis.
    The performance of decision-making strategies in SME internationalizationManuscript (preprint) (Other academic)
    Abstract [en]

    While the relevance of non-predictive strategies for the international expansion of small and medium-sized enterprises (SMEs) has been increasingly discussed, available studies tend to ignore the possibility of synergistic effects between predictive and non-predictive approaches in such a context. This paper contrasts the effects of both strategies on SMEs’ international market performance, considering that their combination can make room for synergistic effects. The analysis combines primary survey data from 851 SMEs in Brazil, China, Italy, Poland, and Sweden with secondary data retrieved from the World Bank. Besides supporting both independent and synergistic performance effects of predictive and non-predictive strategies, the results indicate that foreign market institutions affect each of these effects differently and suggest firm size effects worth consideration. Contributions include the test of the consensus that has been formed around the superiority of non-predictive strategies and the contextualization of SMEs’ decision-making strategies in both external and internal organizational terms.

  • 40. Kvam, Peter D
    et al.
    Sokratous, Konstantina
    Fitch, Anderson
    Hintze, Arend
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Using artificial intelligence to fit, compare, evaluate, and discover models of decision behavior2023Manuscript (preprint) (Other academic)
  • 41.
    Lagin, Madelen
    et al.
    Dalarna University, School of Culture and Society, Business Administration and Management.
    Håkansson, Johan
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Nordström, Carin
    Dalarna University, School of Information and Engineering, Entrepreneurship and Innovation.
    Nyberg, Roger G.
    Dalarna University, School of Information and Engineering, Informatics.
    Öberg, Christina
    CTF, Service Research Center, Karlstad University; Ratio Institute, Stockholm.
    Last-mile logistics of perishable products: a review of effectiveness and efficiency measures used in empirical research2022In: International Journal of Retail & Distribution Management, ISSN 0959-0552, E-ISSN 1758-6690, Vol. 50, no 13, p. 116-139Article in journal (Refereed)
    Abstract [en]

    Purpose

    Current online business development redistributes last-mile logistics (LML) from consumer to retailer and producer. This paper identifies how empirical LML research has used and defined logistic performance measures for key grocery industry actors. Using a multi-actor perspective on logistic performance, the authors discuss coordination issues important for optimising LML at system level.

    Design/methodology/approach

    A semi-systematic literature review of 85 publications was conducted to analyse performance measurements used for effectiveness and efficiency, and for which actors.

    Findings

    Few empirical LML studies exist examining coordination between key actors or on system level. Most studies focus on logistic performance measurements for retailers and/or consumers, not producers. Key goals and resource utilisations lack research, including all key actors and system-level coordination.

    Research limitations/implications

    Current LML performance research implies a risk for sub-optimisation. Through expanding on efficiency and effectiveness interplay at system level and introducing new research perspectives, the review highlights the need to revaluate single-actor, single-measurement studies.

    Practical implications

    No established scientific guidelines exist for solving LML optimisation in the grocery industry. For managers, it is important to thoroughly consider efficiency and effectiveness in LML execution, coordination and collaboration among key actors, avoiding sub-optimisations for business and sustainability.

    Originality/value

    The study contributes to current knowledge by reviewing empirical research on LML performance in the grocery sector, showing how previous research disregards the importance of multiple actors and coordination of actors, efficiency and effectiveness.

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  • 42.
    Lagin, Madelen
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Olsmats, Carl
    Dalarna University, School of Information and Engineering, Industrial Engineering and Management.
    Espegren, Yanina
    Dalarna University, School of Culture and Society, Business Administration and Management.
    Nordström, Carin
    Dalarna University, School of Information and Engineering, Entrepreneurship and Innovation.
    The value creation failure of grocery retailers’ last-mile value proposition: A sustainable business model perspective2022In: Cleaner and Responsible Consumption, ISSN 2666-7843, article id 100088Article in journal (Refereed)
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  • 43.
    Lindgren, Charlie
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Voluntary Information Sharing, Retail Pricing and Firm Performance2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Online information sharing by firms has created an unprecedented amount of data to analyze by researchers. While conclusions from research should be drawn with basis in sound theory, there has also emerged a need to supplement these theoretical considerations with advanced data collection, storage and analysis as well as reporting to decisionmakers. As such, the emergence of the research field of microdata analysis has given aid in the process of gathering large quantities of data and managing databases, analyzing said data with knowledge in advanced areas, e.g., statistical inference, machine learning, artificial intelligence and the like, and presenting the results for decisionmakers and stakeholders in a clear, coherent way while also stating economic consequences to enable decision-making. This dissertation consists of five individual papers contributing to this field of research, and in the process answering a set of questions related to voluntary information sharing, retail pricing, and firm performance.

    In the first paper, a large-scale data collection of corporate social responsibility reports was coupled with state-of-the-art topic modelling analysis to answer the question who the intended users of these reports are, and the results show that the shareholder perspective is more prominent rather than the stakeholder perspective. The second paper empirically shows the value of having lowest price or highest ratings on a price comparison website, with the former being found to have a profound impact on demand, while the effect of the latter is more unclear. The third paper relies on time series clustering analysis to test if intertemporal price discrimination is the cause of remaining price dispersion in low search cost markets. The empirical evidence rejects an established theory of explaining price dispersion in a wide range of markets characterized by low search costs. The fourth paper provides an investigation into how the increased use of a price comparison website affect pricing. It is found that an increased use of the platform and number of retailers entering lead to a reduction in average prices with substantial consumer savings as the general outcome. Following the results of the third paper, a more likely model to explain the persistent price dispersion in low search cost markets is also presented. The fifth and final paper combines two datasets with rigorous statistical analysis to answer why firms compete on price comparison websites, despite the threat of increased competition and reductions in prices. The results show that retailers competing on price comparison websites increase their productivity which creates increased profits that are shared between shareholders and employees. The different types of information sharing studied in this thesis is thus found to have profound impact on consumers, firms and society at large.

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  • 44.
    Lindgren, Charlie
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Daunfeldt, Sven-Olov
    Dalarna University, School of Culture and Society, Economics. Institute of Retail Economics, Stockholm.
    Rudholm, N.
    Yella, Siril
    Dalarna University, School of Information and Engineering, Computer Engineering.
    Is intertemporal price discrimination the cause of price dispersion in markets with low search costs?2021In: Applied Economics Letters, ISSN 1350-4851, E-ISSN 1466-4291, Vol. 28, no 11, p. 968-971Article in journal (Refereed)
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  • 45.
    Lindgren, Charlie
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Huq, Asif M
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Who are the intended users of CSR reports?: Insights from a data-driven approach2021In: Sustainability, E-ISSN 2071-1050, Vol. 13, no 3, p. 1070-1090Article in journal (Refereed)
    Abstract [en]

    There is extant research on theorization, conceptualization, determinants, and consequences of corporate social responsibility (CSR). However, what firms include in their CSR or sustainability reports are much less covered and are predominantly covered in case studies of individual firms. In this paper, we instead take a holistic view and simultaneously explore what firms around the globe currently disclose in these reports, more specifically we investigate if firms are shareholder or stakeholder focused. In this investigation, we check the alignment of the reports to the materiality framework of Sustainability Accounting Standards Board (SASB) which was developed having shareholders as the intended user. To estimate what firms disclose in CSR reports we used the unsupervised Bayesian machine learning approach latent Dirichlet allocation (LDA) developed by Blei et al. We conclude that firms target shareholders as the intended users of these reports, even in environments where stakeholder approach of management is argued to be more dominant. Methodologically, we contribute by demonstrating that topic modeling can enhance the objectivity in reviewing CSR-reports.

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  • 46.
    Lindgren, Charlie
    et al.
    Dalarna University, School of Information and Engineering, Informatics.
    Li, Yujiao
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Rudholm, Niklas
    Institute of Retail Economics, Stockholm, Sweden.
    Why do firms compete on price comparison websites? The impact on productivity, profits, and wages2022In: International Review of Retail Distribution & Consumer Research, ISSN 0959-3969, E-ISSN 1466-4402, p. 1-13Article in journal (Refereed)
    Abstract [en]

    A substantial literature indicates that competition on price comparison websites is fierce, leading to lower prices for products sold. As such, we want to answer the key research question: Why do firms compete on price comparison websites? Based on theory, we suggest that participation in these marketplaces leads to increased productivity, i.e., output increases when holding constant the level of inputs used. This, in turn, leads to increased profits, motivating firms to enter price comparison websites despite fierce competition. To find out if theory holds, we empirically investigate how firm entry into a price comparison website affects firm productivity, profits, and wages. Empirically investigating the impact of PriceSpy market participation on productivity, profits, and wages is not easy since firms are free to select whether and when to enter or exit the PriceSpy marketplace, and we use a two-step procedure to address this problem. In the first step, we control for differences in observables between entering firms and potential control-group firms. Then, in a second step, we use a within-firm difference-in-difference estimator on the matched data to investigate how entry into the PriceSpy marketplace affects output while holding inputs constant. Our results indicate that for the full sample of firms, PriceSpy participation increases output by almost 12% when holding the level of inputs constant. Also, an investigation of who gains from the increased productivity shows that, for entering firms, operating profits increase by 9% and gross wages by 14% when studying the full sample of firms. That labor gains more from PriceSpy participation is even clearer when studying the impact on wholesale and retail firms separately. For those firms, wages increased by 16–17% after entry, while no statistically significant impact was found regarding operating profits.

  • 47.
    Malek, Wasim
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Mortazavi, Reza
    Dalarna University, School of Culture and Society, Economics.
    Cialani, Catia
    Dalarna University, School of Culture and Society, Economics.
    Nordström, Jonas
    Dalarna University, School of Culture and Society, Economics.
    How have waste management policies impacted the flow of municipal waste? An empirical analysis of 14 European countries2023In: Waste Management, ISSN 0956-053X, E-ISSN 1879-2456, Vol. 164, p. 84-93Article in journal (Refereed)
    Abstract [en]

    Waste management policies aim to divert waste from lower positions on the waste hierarchy such as landfill and incineration to higher positions in the hierarchy such as energy recovery and recycling. However, empirical evaluations of such policies are scarce. This study highlighted the effect of waste management policies on the amount of waste treated with landfill, incineration, energy recovery and recycling by analysing a panel dataset consisting of 14 European countries and the period 1996 to 2018. Findings from a seemingly unrelated regression model suggest that the landfill ban is associated with a decrease in landfill waste, but an increase in incineration, energy recovery and recycling waste. The landfill tax is also correlated with an increase in energy recovery waste but, in contrast, it is associated with a reduction in incineration and recycling waste. Meanwhile, the deposit refund scheme is associated with a decrease in the amount of landfill waste. Concerning the effects on total waste generated, regression results from a fixed effects model indicate that the landfill tax and the deposit refund scheme are both correlated with a reduction in the amount of waste generated. These findings contribute to the scarce academic literature evaluating waste management policies and may better inform policy makers on their longer-term implications.

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  • 48.
    May, Ross
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    On the Feasibility of Reinforcement Learning in Single- and Multi-Agent Systems: The Cases of Indoor Climate and Prosumer Electricity Trading Communities2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Over half of the world’s population live in urban areas, a trend which is expected to only grow as we move further into the future. With this increasing trend in urbanisation, challenges are presented in the form of the management of urban infrastructure systems. As an essential infrastructure of any city, the energy system presents itself as one of the biggest challenges. Indeed, as cities expand in population and economically, global energy consumption increases, and as a result, so do greenhouse gas (GHG) emissions. Key to realising the goals as laid out by the 2030 Agenda for Sustainable Development, is the energy transition - embodied in the goals pertaining to affordable and clean energy, sustainable cities and communities, and climate action. Renewable energy systems (RESs) and energy efficiency have been shown as key strategies towards achieving these goals. While the building sector is considered to be one of the biggest contributors to climate change, it is also seen as an area with many opportunities for realising the energy transition. Indeed, the emergence of the smart city and the internet of things (IoT), alongside Photovoltaic and battery technology, offers opportunities for both the smart management of buildings, as well as the opportunity to form self-sufficient peer-to-peer (P2P) electricity trading communities. Within this context, advanced building control offers significant potential for mitigating global warming, grid instability, soaring energy costs, and exposure to poor indoor building climates. Most advanced control strategies, however, rely on complex mathematical models, which require a great deal of expertise to construct, thereby costing in time and money, and are unlikely to be frequently updated - which can lead to sub-optimal or even wrong performance. Furthermore, arriving at solutions in economic settings as complex and dynamic as the P2P electricity markets referred to above, often leads to solutions that are computationally intractable. A model-based approach thus seems, as alluded to above, unsustainable, and I thus propose taking a model-free alternative instead. One such alternative is the reinforcement learning (RL) method. This method provides a beautiful solution that addresses many of the limitations seen in more classical approaches - those based on complex mathematical models - to single- and multi-agent systems. To address the feasibility of RL in the context of building systems, I have developed four papers. In studying the literature, while there is much review work in support of RL for controlling energy consumption, it was found that there were no such works analysing RL from a methodological perspective w.r.t. controlling the comfort level of building occupants. Thus, in Paper I, to fill in this gap in knowledge, a comprehensive review in this area was carried out. To follow up, in Paper II, a case study was conducted to further assess, among other things, the computational feasibility of RL for controlling occupant comfort in a single agent context. It was found that the RL method was able to improve thermal and indoor air quality by more than 90% when compared with historically observed occupant data. Broadening the scope of RL, Papers III and IV considered the feasibility of RL at the district scale by considering the efficient trade of renewable electricity in a peer-to-peer prosumer energy market. In particular, in Paper III, by extending an open source economic simulation framework, multi-agent reinforcement learning (MARL) was used to optimise a dynamic price policy for trading the locally produced electricity. Compared with a benchmark fixed price signal, the dynamic price mechanism arrived at by RL, increased community net profit by more than 28%, and median community self-sufficiency by more than 2%. Furthermore, emergent social-economic behaviours such as changes in supply w.r.t changes in price were identified. A limitation of Paper III, however, is that it was conducted in a single environment. To address this limitation and to assess the general validity of the proposed MARL-solution, in Paper IV a full factorial experiment based on the factors of climate - manifested in heterogeneous demand/supply profiles and associated battery parameters, community scale, and price mechanism, was conducted in order to ascertain the response of the community w.r.t net-loss (financial gain), self-sufficiency, and income equality from trading locally produced electricity. The central finding of Paper IV was that the community, w.r.t net-loss, performs significantly better under a learned dynamic price mechanism than under the benchmark fixed price mechanism, and furthermore, a community under such a dynamic price mechanism stands an odds of 2 to 1 in increased financial savings. 

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  • 49.
    May, Ross
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Huang, Pei
    Dalarna University, School of Information and Engineering, Energy Technology.
    Does a smart agent overcome the tragedy of the commons in residential prosumer communities?2023Article in journal (Refereed)
  • 50.
    May, Ross
    et al.
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Huang, Pei
    Dalarna University, School of Information and Engineering, Energy Technology.
    A multi-agent reinforcement learning approach for investigating and optimising peer-to-peer prosumer energy markets2023In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 334, article id 120705Article in journal (Refereed)
    Abstract [en]

    Current power grid infrastructure was not designed with climate change in mind, and, therefore, its stability, especially at peak demand periods, has been compromised. Furthermore, in light of the current UN’s Intergovernmental Panel on Climate Change reports concerning global warming and the goal of the 2015 Paris climate agreement to constrain global temperature increase to within 1.5–2 °C above pre-industrial levels, urgent sociotechnical measures need to be taken. Together, Smart Microgrid and renewable energy technology have been proposed as a possible solution to help mitigate global warming and grid instability. Within this context, well-managed demand-side flexibility is crucial for efficiently utilising on-site solar energy. To this end, a well-designed dynamic pricing mechanism can organise the actors within such a system to enable the efficient trade of on-site energy, therefore contributing to the decarbonisation and grid security goals alluded to above. However, designing such a mechanism in an economic setting as complex and dynamic as the one above often leads to computationally intractable solutions. To overcome this problem, in this work, we use multi-agent reinforcement learning (MARL) alongside Foundation – an open-source economic simulation framework built by Salesforce Research – to design a dynamic price policy. By incorporating a peer-to-peer (P2P) community of prosumers with heterogeneous demand/supply profiles and battery storage into Foundation, our results from data-driven simulations show that MARL, when compared with a baseline fixed price signal, can learn a dynamic price signal that achieves both a lower community electricity cost, and a higher community self-sufficiency. Furthermore, emergent social–economic behaviours, such as price elasticity, and community coordination leading to high grid feed-in during periods of overall excess photovoltaic (PV) supply and, conversely, high community trading during overall low PV supply, have also been identified. Our proposed approach can be used by practitioners to aid them in designing P2P energy trading markets.

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