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The European Union (EU) has introduced a range of policies to promote energy efficiency, including setting specific targets for energy-efficient renovations across the EU building stock. This study provides a comprehensive environmental and economic assessment of energy-efficient renovation scenarios in a large-scale multifamily building project that is district-heated, considering both the building and the broader urban energy system. A systematic framework was developed for this assessment and applied to a real case in Sweden, where emission factors from energy production are significantly lower than the EU average: 114 g CO2e/kWh for district heating and 37 g CO2e/kWh for electricity. The project involved the renovation of four similar district-heated multifamily buildings with comparable energy efficiency measures. The primary distinction between the measures lies in the type of HVAC system installed: (1) exhaust ventilation with air pressure control, (2) mechanical ventilation with heat recovery, (3) exhaust ventilation with an exhaust air heat pump, and (4) exhaust ventilation with an exhaust air heat pump combined with photovoltaic (PV) panels. The study's findings show that the building with an exhaust air heat pump which operates intermittently with PV panels achieves the best environmental performance from both perspectives. A key challenge identified for future research is balancing the reduced electricity production from Combined Heat and Power (CHP) plants within the energy system.

As the global demand for renewable energy and environmental protection continues to grow, anaerobic digestion of food waste as an effective way of resource recycling and energy production has attracted widespread attention. And forecasting methane generation with precision throughout the anaerobic digestion (AD) process is crucial for optimizing the process and improving energy recovery efficiency. Therefore, this paper proposed a new time series prediction model based on the iTransformer method to accurately predict the biogas production during the AD of food waste. The iTransformer uses the attention mechanism to capture the inter-variable relationships, and sequentially processes the historical observations features layer by layer along the time dimension through the feedforward network to capture the complex dynamic characteristics of production process data and build a predictive model. Finally, the proposed method is used to forecast the methane yield and carbon dioxide emissions during the AD of food waste. Compared with the gate recurrent unit (GRU), the autoregressive integrated moving average (ARIMA), the long short-term memory network (LSTM) and Transformer methodologies, the proposed iTransformer method based time series prediction method performs well in the productivity prediction of Garment Employees (PPGM) dataset and the AD dataset, where the mean square error (MSE), coefficient of determination (R2), and accuracy are 0.0231, 0.9036, and 95.9118% on the PPGM dataset, and the MSE, R2, the root mean square error (RMSE) and accuracy are 3946.9602, 0.9949, 7.1596, and 98.5517% on the AD dataset, respectively. Moreover, the impact of different operational parameters on the AD process can be optimized through the prediction results to increase biogas production and reduce carbon emissions.

Previous research has shown a lack of studies with comparisons between primary (virgin) and secondary (re-used) building materials, and their embodied emissions. The creation of different scenarios comparing the environmental impact of virgin vs. re-used materials is also motivated by the scarcity of raw materials in the world and the emergency of mitigating greenhouse gas (GHG) emissions from buildings. The aim of this study was to investigate scenarios, including new vs. re-used building products, applying the LCA method for a wooden single-family building. The findings showed a 23% reduction potential for total released (positive) CO₂e when comparing the Reference scenario with Scenario I, using re-used wooden-based materials. Further, Scenario II, using all re-used building materials except for installations, showed a 59% CO₂e reduction potential compared to the Reference scenario. Finally, Scenario III, which assumes all re-used building products, showed a 92% decreased global warming potential (GWP) impact compared to the Reference scenario. However, when including biogenic carbon and benefits (A5 and D module), the Reference scenario, based on newly produced wooden building materials, has the largest negative GHG emissions. It can be concluded that the re-use of building products leads to significant carbon savings compared to using new building products.

To effectively reduce building energy consumption, a novel full fresh air system with a heat source tower (HST) and a borehole heat exchanger (BHE) was proposed for space cooling and dehumidification in this paper. The cooling system only adopts geothermal energy to produce dry and cold fresh air for space cooling and dehumidification through the BHE and HST, which has the advantage of non-condensate water compared to BHE systems integrated with a fan coil or chilled beam. Based on the established mathematical model of the cooling system, this paper analyzed the system characteristics, feasibility, operation strategy, energy performance, and cost-effectiveness of the proposed model in detail. The results show that the mathematical model has less than 10% error in estimating the system performance compared to the practical HST-BHE experimental set up. Under the specific boundary conditions, the cooling and dehumidification capacity of this system increases with the decrease in the air temperature, air moisture content, and inlet water temperature of the HST. The optimal cooling capacity and the system COP can be achieved when the air-water flow ratio is at 4:3. A case study was conducted in a residential building in Shenyang with an area of about 1800 m2. It was found that this system can fully meet the cooling and dehumidification demand in such a residential building. The operation strategy of the cooling system can be optimized by adjusting the air-water flow ratio from 4:3 to 3:2 during the early cooling season (7 June-1 July) and end cooling season (3 August-1 September). As a result, the average COP of the cooling system during the whole cooling season can be improved from 6.1 to 8.7. Compared with the air source heat pump (ASHP) and the ground source heat pump (GSHP) for space cooling, the proposed cooling system can achieve an energy saving rate of 123% and 26%, respectively. Considering that the BHE of the GSHP can be part of the proposed HST-BHE cooling system, the integration of the HST and GHSP for space cooling (and heating) is strongly recommended in actual applications.

The Heating, Ventilation and Air Conditioning (HVAC) systems are complex and prone to failures during operation, often leading to significant energy waste. Timely and accurate Fault Detection and Diagnosis (FDD) can enhance energy efficiency. The HVAC system operates under diverse conditions, data-driven models trained under existing conditions may experience performance degradation when faced with new conditions. Transfer learning offers an effective solution to this issue. This study proposes a novel transfer learning ensemble model based on adaptive weights, leveraging different transfer learning strategies to improve diagnosis performance under new conditions. Multiple cross-condition transfer learning tasks were implemented to test the proposed method, and its effectiveness was validated through multiple experiments to minimize the impact of randomness. Results showed that, compared to fine-tuning (FT), domain-adversarial neural network (DANN), and baseline models, the proposed method outperforms the other models. The average accuracy of multiple experiments improved by 0.21 % to 2.34 % compared to FT. Additionally, modifying DANN to utilize a small amount of labeled information from the target domain has led to greater overlap between the feature distributions of the source and target domains, resulting in improved performance that is close to that of FT. Finally, we analyzed the impact of target domain data volume on the performance of the four methods. The performance of the baseline model improved significantly with the increase in data volume, while the other models showed less improvement. Meanwhile, the diagnostic results of the baseline model were significantly influenced by experimental randomness when there is less training data, whereas the FT diagnostic results were relatively more stable. © 2024 Elsevier B.V.

Traditional asset management of lighting systems typically focuses on functionality, cost, and lifespan. In contrast, a human-centric approach prioritizes social sustainability and user well-being by ensuring lighting assets “provide the right light at the right time” for diverse activities. Light-emitting diode (LED) bulbs, known for energy efficiency and longevity, have become a preferred choice, yet public libraries often struggle to manage these assets sustainably, remaining in a reactive “fix/replace when it breaks” stage. Current predictive methods, such as artificial intelligence and machine learning, rely on laboratory data that often overlook real-world contexts, leading to performance gaps. This paper presents a context-driven, human-centric methodology for LED prognosis and maintenance strategies in public libraries, employing limited degradation data from LED testing. Advanced analytical techniques, including Markov Chain Monte Carlo (MCMC) and Deviance Information Criterion (DIC), support a shift from function-based to performance-based reliability assessment. By incorporating Mean Time of Exposure (MTOE) and Critical Integrated Levels (CILs), the approach defines optimal maintenance inspection intervals. This research enhances sustainable LED lighting management in public libraries, offering a framework adaptable to broader applications and aligned with human-centric goals.

Building-Integrated Photovoltaics (BIPV) offer a promising solution to enhance building energy efficiency and reduce building energy consumption. Among the various application of BIPV, BIPV windows stand out as an intriguing and notable example. This paper investigates an office building with BIPV windows in five different climatic cities in China. The study considers four variables, including building orientation (BO), window size (WZ), window visible light transmittance (VLT), and type of PV (TOPV). The objective is to minimize both the annual net electricity cost (ANEC) and the extra investment cost of BIPV windows. To simulate the design variables and objective functions, the jEPlus software is employed. Additionally, the jEPlus + EA software uses the SRC and Morris algorithms for sensitivity analysis of design parameters. The multi-objective problem is addressed using the NSGA-II. By conducting optimization, a set of Pareto optimal solutions were obtained. The results demonstrate that the building with BIPV windows can save a minimum of 6.83 % annual electricity costs. Moreover, the static investment payback period for all cities ranges from 7 to 14 years, indicating the economic feasibility of implementing BIPV windows.

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.

This article highlights the significant impact of small faults in heating, ventilation, and air conditioning (HVAC) systems on energy efficiency and operational costs, using a case study from Northern Sweden. Even minor malfunctions, such as a faulty heat exchanger sensor, can lead to drastic inefficiencies and increased energy waste, with financial consequences for homeowners. This article emphasizes the importance of regular maintenance and monitoring and explores the potential of emerging technologies such as industrial AI, cyber–physical systems (CPSs), and digital twins (DTs) to revolutionize HVAC optimization. By adopting proactive strategies, both energy efficiency and sustainability goals can be achieved.

Most lighting is only designed to meet the visual needs in most public libraryenvironments in Sweden. Although lighting-related impacts are relevant to six Unite Nationssustainability goals, some important lighting considerations, such as circadian phase disruption,mode and productivity impact, and energy-efficient operation, are missing in current lightingoperating practices. Moreover, most of the current lighting asset management practice in publicbuildings remains “fix it if only it breaks”. With respect to people-centric health factors, visualindex, and lighting asset energy-efficient operation, this study sublimates lighting into a newperspective. Finally, the suggested comprehensive lighting operating strategies integratingdigital twins can help designers and operators in defining the optimal design/control strategy inpublic-built environments, like public library. Digital twin-based decision-making is expected tobe applied to lighting design and control in public spaces that improves visual acuity and comfort,positively impact mood and productivity, and provides recommendations on engagementprinciples under Environment Social Governance (ESG) framework to asset manager/operators.