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  • 51.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Håkansson, Johan
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Nyberg, Roger G.
    Högskolan Dalarna, Institutionen för information och teknik, Informatik.
    Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter2021Ingår i: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, artikel-id 1812647Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Due to the lack of wide availability of parking assisting applications, vehicles tend to cruise more than necessary to find an empty parking space. This problem is evident globally and the intensity of the problem varies based on the demand of parking spaces. It is a well-known hypothesis that the amount of cruising by a vehicle is dependent on the availability of parking spaces. However, the amount of cruising that takes place in search of parking spaces within a parking lot is not researched. This lack of research can be due to privacy and illumination concerns with suitable sensors like visual cameras. The use of thermal cameras offers an alternative to avoid privacy and illumination problems. Therefore, this paper aims to develop and demonstrate a methodology to detect and track the cruising patterns of multiple moving vehicles in an open parking lot. The vehicle is detected using Yolov3, modified Yolo, and custom Yolo deep learning architectures. The detected vehicles are tracked using Kalman filter and the trajectory of multiple vehicles is calculated on an image. The accuracy of modified Yolo achieved a positive detection rate of 91% while custom Yolo and Yolov3 achieved 83% and 75%, respectively. The performance of Kalman filter is dependent on the efficiency of the detector and the utilized Kalman filter facilitates maintaining data association during moving, stationary, and missed detection. Therefore, the use of deep learning algorithms and Kalman filter facilitates detecting and tracking multiple vehicles in an open parking lot.

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  • 52.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera2020Ingår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 14, nr 10, s. 1295-1302Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Parking vehicle is a daunting task and a common problem in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, aim of the paper is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect the data during varying environmental conditions such as; sunny, dusk, dawn, dark and snowy conditions. Vehicle detection with deep learning was implemented where image classification and object localization were performed for multi object detection. The dataset consists of 527 images which were manually labelled as there were no pre-labelled thermal images available. Multiple deep learning networks such as Yolo, ReNet18, ResNet50 and GoogleNet with varying layers and architectures were evaluated on vehicle detection. Yolo, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time while Resnet50 produced better detection results compared to other detectors. However, ResNet18 also produced minimal miss rates and is suitable for real time vehicle detection. The detected results were compared with a template of parking spaces and IoU value is used to identify vehicle occupancy information.

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  • 53.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Håkansson, Johan
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Nyberg, Roger G.
    Högskolan Dalarna, Institutionen för information och teknik, Informatik.
    CO2 Emissions Induced by Vehicles Cruising for Empty Parking Spaces in an Open Parking Lot2022Ingår i: Sustainability, E-ISSN 2071-1050, Vol. 14, nr 7, artikel-id 3742Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Parking lots are places of high air pollution as numerous vehicles cruise to find vacant parking places. Open parking lots receive high vehicle traffic, and when limited empty spaces are available it leads to problems, such as congestion, pollution, and driver frustration. Due to lack of return on investment, open parking lots are little studied, and there is a research gap in understanding the magnitude of CO2 emissions and cruising observed at open parking lots. Thus, this paper aims to estimate CO2 emissions and cruising distances observed at an open parking lot. A thermal camera was utilized to collect videos during peak and non-peak hours. The resulting videos were utilized to collect cruising trajectories of drivers searching for empty parking spaces. These trajectories were analyzed to identify optimal and non-optimal cruising, time to park, and walking distances of drivers. A new CO2 model was proposed to estimate emissions in smaller geographical regions, such as open parking lots. The majority of drivers tend to choose parking spaces near a shopping center, and they prefer to cruise non-optimal distances to find an empty parking space near the shopping center. The observed mean non-optimal cruising distance is 2.7 times higher than the mean optimal cruising distance. Excess CO2 emissions and non-optimal cruising were mainly observed during visitor peak hours when there were limited or no empty parking spaces. During visitor peak hours, several vehicles could not find an empty parking space in the region of interest, which also leads to excess cruising.

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  • 54.
    Saleh, Roxan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Swedish Transport Administration.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Factors Affecting Night-Time Visibility of Retroreflective Road Traffic Signs: A Review2021Ingår i: International Journal for Traffic and Transport Engineering, ISSN 2217-5652, Vol. 11, nr 1, s. 115-128, artikel-id 10.7708/ijtte.2021.11(1).07Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Road traffic signs define a visual language that can be interpreted by drivers.They represent the current traffic situation on the road, show danger and difficulties aroundthe drivers, give them warnings, and help them with their navigation by providing usefulinformation that makes driving safe and convenient. The main part of the road traffic sign isthe retroreflective material which reflects the light from the vehicle headlights to the driver.Driving during night-time is a challenge, and the rertoreflective material on the sign boardhelps the drivers to perceive and interpret the information on the road traffic sign properly.The aim of this paper is to study the factors affecting the performance of driving during nighttimeand the role the retroreflective material that plays in this regard. The vehicle headlights,ambient conditions, and the type of retroreflection material affect the light reflected from theroad traffic signs. It is also found that the retroreflectivity depends on vehicle factors such asheadlights colour and angle of illumination. Other factors such as environmental factors andsign factors can also affect the retroreflectivity.

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  • 55.
    Saleh, Roxan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Swedish Transport Adminstration.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Predicting the service life of road signs based on their retroreflectivity and color using Logistic Regression2023Ingår i: Transportation Research Procedia, ISSN 2352-1465, Vol. 73, s. 77-84Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    AbstractRoad signs play a vital role in providing drivers with crucial information for safe driving in both day and nighttime. The color of road signs enhances visibility during daylight hours, while retroreflectivity is essential for improving visibility during nighttime conditions. Road authorities, responsible for maintaining road signs, primarily consider the levels of retroreflectivity when deciding to replace them, ensuring optimal visibility for drivers. This study focuses on examining the degradation of road signs based on retroreflectivity and color to ensure safe driving through adequate visibility in both day and nighttime conditions. The study underscores the significance of regulating the deterioration of road sign colors to enhance visibility and legibility, while minimizing maintenance and replacement costs. The primary objective of this paper is to predict the age (service life) of road signs by considering both retroreflectivity and color status and using logistic regression. The results indicate that the age of road signs can be influenced by either retroreflectivity or color. For instance, approximately 50% of red road signs are projected to lose their color after 16 years, while their retroreflectivity remains acceptable. Similarly, around 50% of yellow and white road signs experience retroreflectivity degradation after 20 and 16 years, respectively, while their color remains acceptable. Finally, blue road signs demonstrate acceptable retroreflectivity and color levels even after 35 years.

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  • 56.
    Saleh, Roxan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Swedish Transport Administration, Borlänge.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan2024Ingår i: International Journal of Transportation Science and Technology, ISSN 2046-0430Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study addresses the critical safety issue of declining retroreflectivity values of road traffic signs, which can lead to unsafe driving conditions, especially at night. The paper aims to predict the retroreflectivity coefficient values of these signs and to classify their status as acceptable or rejected (in need of replacement) using machine learning models. Moreover, logistic regression and survival analysis are used to predict the median lifespans of road traffic signs across various geographical locations, focusing on signs in Croatia and Sweden as case studies. The results indicate high accuracy in the predictive models, with classification accuracy at 94% and an R2 value of 94% for regression analysis. A significant finding is that a considerable number of signs maintain acceptable retroreflectivity levels within their warranty period, suggesting the feasibility of extending maintenance checks and warranty periods to 15 years which is longer than the current standard of 10 years. Additionally, the study reveals notable variations in the median lifespans of signs based on color and location. Blue signs in Croatia and Sweden exhibit the longest median lifespans (28 to 35 years), whereas white signs in Sweden and red signs in Croatia show the shortest (16 and 10 years, respectively). The high accuracy of logistic regression models (72–90%) for lifespan prediction confirms the effectiveness of this approach. These findings provide valuable insights for road authorities regarding the maintenance and management of road traffic signs, enhancing road safety standards. © 2024 Tongji University and Tongji University Press

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  • 57.
    Saleh, Roxan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Swedish Transport Adminstration, Borlänge.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Using Supervised Machine Learning to Predict the Status of Road Signs2022Ingår i: Transportation Research Procedia, E-ISSN 2352-1465, Vol. 62, s. 221-228Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    There is no data collected and saved about road signs in Sweden and the status for these signs is unknown. Furthermore, the status of the sign colors, the quality of the sign, the type of the retroreflection material, and age of the road signs are unknown. Therefore, the status of a road sign (approved or not), which depends on these parameters, is unknown.The aim of this study is to predict the status of the road signs mounted on the Swedish roads by using supervised machine learning. This study investigates the effect of using principal component analysis (PCA) and data scaling on the accuracy of the prediction. The data were prepared before using then scaled using two methods which are the normalization and the standardization.The three algorithms that tested in this study are Random Forest, Artificial Neural Network (ANN), and Support Vector Machines (SVM). They are invoked to predict the status of the road signs. The algorithms exhibited overall high predicting accuracy (98%), high precision (98%), high recall (98%), and high F1 scores (98%).Random forest showed the best performance with 4 PC components on the normalized data with a highest accuracy of 98%.Using PCA showed different impacts on the performance of different techniques. In the case of ANN, invoking PCA improves the accuracy, while for SVM the accuracy decreases when PCA is used. On other hand, PCA has no effect on the accuracy of the random forest model when scaling is invoked.The effect of the data scaling using normalization and standardization is also investigated in this study, and it is noticed that scaling of the data increases the accuracy of the prediction for all the three models (ANN, SVM and Random Forest). Furthermore, better accuracy is achieved when the standardization is invoked compared with normalization.

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  • 58.
    Saleh, Roxan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Swedish Transport Adminstration, Borlänge.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Using supervised machine learning to predict the status of road signs2021Ingår i: Book of abstracts of the 24th Euro Working Group on Transportation Meeting / [ed] Margarida C. Coelho, Joaquim Macedo, Eloísa Macedo, Paulo Fernandes, Jorge Bandeira, Behnam Bahmankhah, UA Editora, Universidade de Aveiro , 2021, s. 64-64Konferensbidrag (Refereegranskat)
    Abstract [en]

    There is no data collected and saved about road signs in Sweden and the status for these signs is unknown. Furthermore, the status of the sign colors, the quality of the sign, the type of the retroreflection material, and age of the road signs are unknown. Therefore, it is difficult to know the status (approved or not) of any road sign without performing a costly inspection. The aim of this study is to predict the status of the road signs mounted on the Swedish roads by using supervised machine learning. This study investigates the effect of using principal component analysis (PCA) and data scaling on the accuracy of the prediction. The data were prepared before using then scaled using two methods which are the normalization and the standardization. The three algorithms that tested in this study are Random Forest, Artificial Neural Network (ANN), and Support Vector Machines (SVM). They are invoked to predict the status of the road signs. The algorithms exhibited overall high predicting accuracy (98%), high precision (98%), high recall (98%), and high F1 scores (98%). Random forest showed the best performance with 4 PC components on the normalized data with a highest accuracy of 98%. Using PCA showed different impacts on the performance of different techniques. In the case of ANN, invoking PCA improves the accuracy, while for SVM the accuracy decreases when PCA is used. On other hand, PCA has no effect on the accuracy of the random forest model when scaling is invoked. The effect of the data scaling using normalization and standardization is also investigated in this study, and it is noticed that scaling of the data increases the accuracy of the prediction for all the three models (ANN, SVM and Random Forest). Furthermore, better accuracy is achieved when the standardization is invoked compared with normalization. 

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  • 59.
    Saleh, Roxan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Swedish Transport Administration, Borlänge.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Alam, Moudud
    Högskolan Dalarna, Institutionen för information och teknik, Statistik.
    An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs2022Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 12, nr 5, artikel-id 2413Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The road traffic signs in Sweden have no inventory system and it is unknown when a sign has reached the end of its service life and needs to be replaced. As a result, the road authorities do not have a systematic maintenance program for road traffic signs, and many signs which are not in compliance with the minimum retroreflectivity performance requirements are still found on the roads. Therefore, it is very important to find an inexpensive, safe, easy, and highly accurate method to judge the retroreflectivity performance of road signs. This will enable maintenance staff to determine the retroreflectivity of road signs without requiring measuring instruments for retroreflectivity or colors performance. As a first step toward the above goal, this paper aims to identify factors affecting the retroreflectivity of road signs. Two different datasets were used, namely, the VTI dataset from Sweden and NMF dataset from Denmark. After testing different models, two logarithmic regression models were found to be the best-fitting models, with R2 values of 0.50 and 0.95 for the VTI and NMF datasets, respectively. The first model identified the age, direction, GPS positions, color, and class of road signs as significant predictors, while the second model used age, color, and the class of road signs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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  • 60.
    Saleh, Roxan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Swedish Transport Administration,Borlänge.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Alam, Moudud
    Högskolan Dalarna, Institutionen för information och teknik, Statistik.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Assessing the color status and daylight chromaticity of road signs through machine learning approaches2023Ingår i: IATSS Research, ISSN 0386-1112, Vol. 47, nr 3, s. 305-317Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs. The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden. The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates. The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively. The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97%, respectively. The RF and SVM models also performed well, with accuracy values of 74% and 79% and R2 ranging from 59% to 92%. The findings demonstrate the potential of machine learning to effectively predict the status and daylight chromaticity of road signs and their impact on road safety in the Swedish context. © 2023 International Association of Traffic and Safety Sciences

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  • 61.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Bales, Chris
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Feature Augmentation of Classifiers Using Learning Time Series Shapelets Transformation for Night Setback Classification of District Heating Substations2021Ingår i: Advances in Civil Engineering / Hindawi, ISSN 1687-8086, E-ISSN 1687-8094, Vol. 2021, artikel-id 8887328Artikel i tidskrift (Refereegranskat)
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  • 62.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Bales, Chris
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    From time series to image analysis: A transfer learning approach for night setback identification of district heating substations2021Ingår i: Journal of Building Engineering, E-ISSN 2352-7102, Vol. 43, artikel-id 102537Artikel i tidskrift (Refereegranskat)
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  • 63.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Bales, Chris
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Night setback identification of district heat substations using bidirectional long short term memory with attention mechanism2021Ingår i: Energy, ISSN 0360-5442, Vol. 224, artikel-id 120163Artikel i tidskrift (Refereegranskat)
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  • 64.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    A Review of Single Artificial Neural Network Models for Electricity Spot Price Forecasting2019Ingår i: European Energy Market 2019, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    Electricity price forecasting plays a crucial role in a liberalized electricity market. In terms of forecasting approaches, computational intelligence based models have been widely used with respect to electricity price forecasting and among all computation intelligence based models, artificial neural networks are most popular among researchers due to their flexibility and efficiency in handling complexity and non-linearity. However, a review of recent applications of neural networks for electricity price forecasting is not found in the literature. The motivation of this paper is to fill this research gap. In this study, existing approaches are analyzed and a summary of the strengths and weaknesses of each approach is presented. Besides, each neural network model is briefly summarized, followed by reviews of the corresponding studies of each neural network with respect to electricity forecasting from year 2010 onwards. Major contributions, datasets adopted as well as the corresponding experiment results are analyzed for each reviewed study. Apart from the review of existing studies, the advantages and disadvantages of each type of neural network model are discussed in details. Compared with neural networks based hybrid models, a single neural network model is easier to be implemented, less complex and more efficient. Scope of the review is the application of non-hybrid neural network models. It is found that most literature focuses on short term electricity price forecasting while medium and long term forecasting still remain relatively uncovered.

  • 65.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Anomaly Detection of Heat Energy Usage in District Heating Substations Using LSTM based Variational Autoencoder Combined with Physical Model2020Ingår i: 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020, s. 153-158Konferensbidrag (Refereegranskat)
    Abstract [en]

    District heating systems that distribute heat through pipelines to residential and commercial buildings have been widely used in Northern Europe. The energy efficiency of district heating systems is of great interest to energy stakeholders. However, it is not uncommon that district heating systems fail to achieve the expected performance due to various faults. Identification of such rare observations that are different significantly from the majority of the meter readings data plays a vital role in system diagnose. In this study, a new hybrid approach is proposed for anomaly detection of a district heating substation, which consists of a simplified physical model and a Long Short Term Memory based Variational Autoencoder (LSTM VAE). A dataset of an anonymous substation in Sweden is used as a case study. The performance of two state of art models, LSTM and long short term memory based autoencoder (LSTM AE) are evaluated and compared with the LSTM VAE. Experimental results show that LSTM VAE outperforms the baseline models in terms of Area under receiver operating characteristic (ROC) curve (AUC) and F1 score when an optimal threshold is applied.

  • 66.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Hybrid Artificial Neural Networks Based Models for Electricity Spot Price Forecasting - A Review2019Ingår i: 2019 16th European Energy Market Conference (EEM 19), 2019, artikel-id 8916245Konferensbidrag (Refereegranskat)
    Abstract [en]

    Electricity price forecasting plays a crucial role in aliberalized electricity market. In terms of forecasting approaches,artificial neural networks are the most popular amongresearchers due to their flexibility and efficiency in handlingcomplexity and non-linearity. On the other hand, a single neuralnetwork presents certain limitations. Therefore, in recent years,hybrid models that combine multiple algorithms to balance outthe advantages of a single model have become a trend. However,a review of recent applications of hybrid neural networks basedmodels with respect to electricity price forecasting is not found inthe literature and hence, the motivation of this paper is to fill thisresearch gap. In this study, methodologies of existing forecastingapproaches are briefly summarized, followed by reviews of neuralnetwork based hybrid models concerning electricity forecastingfrom year 2015 onwards. Major contributions of each study,datasets adopted in experiments as well as the correspondingexperiment results are analyzed. Apart from the review ofexisting studies, the novelty and advantages of each type of hybridmodel are discussed in detail. Scope of the review is theapplication of hybrid neural network models. It is found that theforecast horizon of the reviewed literature is either hour ahead orday ahead. Medium and long term forecasting are notcomprehensively studied. In addition, though hybrid modelsrequire relatively large computational time, time measurementsare not reported in any of the reviewed literature.

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  • 67.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Short Term Electricity Spot Price Forecasting Using CatBoost and Bidirectional Long Short Term Memory Neural Network2019Ingår i: 19th European Energy Market Conference (EEM 19), 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    Electricity price forecasting plays a crucial role in liberalized electricity markets. Generally speaking, short term electricity price forecasting is essential for electricity providers to adjust the schedule of production in order to balance consumers’ demands and electricity generation. Short term forecasting results are also utilized by market players to decide the timing of purchasing or selling to gain maximized profit. Among existing forecasting approaches, neural networks are regarded as the state of art method. However, deep neural networks are not studied comprehensively in this field, thus the motivation of this study is to fill this research gap. In this paper, a novel hybrid approach is proposed for short term electricity price forecasting. To be more specific, categorical boosting (Catboost) algorithm is used for feature selection and a bidirectional long short term memory neural network (BDLSTM) serves as the main forecasting engine. To evaluate the effectiveness of the proposed approach, two datasets from the Nord Pool market are employed in the experiment. Moreover, the performance of multi-layer perception (MLP) neural network, support vector regression (SVR) and ensemble tree models are evaluated and compared with the proposed model. Results show that the proposed approach outperforms the rest models in terms of mean absolute percentage error (MAPE).

  • 68.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Fleyeh, Hasan
    Högskolan Dalarna, Institutionen för information och teknik, Datateknik.
    Bales, Chris
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting2022Ingår i: Journal of the Operational Research Society, ISSN 0160-5682, E-ISSN 1476-9360, Vol. 73, nr 2, s. 301-325Artikel i tidskrift (Refereegranskat)
    Ladda ner fulltext (pdf)
    fulltext
  • 69.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Wang, X.
    Lu, M.
    Construction site accident analysis using text mining and natural language processing techniques2019Ingår i: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 99, s. 238-248Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Workplace safety is a major concern in many countries. Among various industries, construction sector is identified as the most hazardous work place. Construction accidents not only cause human sufferings but also result in huge financial loss. To prevent reoccurrence of similar accidents in the future and make scientific risk control plans, analysis of accidents is essential. In construction industry, fatality and catastrophe investigation summary reports are available for the past accidents. In this study, text mining and natural language process (NLP) techniques are applied to analyze the construction accident reports. To be more specific, five baseline models, support vector machine (SVM), linear regression (LR), K-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB) and an ensemble model are proposed to classify the causes of the accidents. Besides, Sequential Quadratic Programming (SQP) algorithm is utilized to optimize weight of each classifier involved in the ensemble model. Experiment results show that the optimized ensemble model outperforms rest models considered in this study in terms of average weighted F1 score. The result also shows that the proposed approach is more robust to cases of low support. Moreover, an unsupervised chunking approach is proposed to extract common objects which cause the accidents based on grammar rules identified in the reports. As harmful objects are one of the major factors leading to construction accidents, identifying such objects is extremely helpful to mitigate potential risks. Certain limitations of the proposed methods are discussed and suggestions and future improvements are provided.

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