Energy and Buildings最新文献

{"title":"Implementation of next-generation occupant-centric sequences of operation in an office building using supervisory control","authors":"Brodie W. Hobson ,&nbsp;Andre A. Markus ,&nbsp;Jayson Bursill ,&nbsp;H. Burak Gunay ,&nbsp;Darwish Darwazeh ,&nbsp;Zheng O’Neill","doi":"10.1016/j.enbuild.2024.115087","DOIUrl":"10.1016/j.enbuild.2024.115087","url":null,"abstract":"<div><div>ASHRAE RP-1747 is a CO₂-based demand-controlled ventilation (DCV) approach which uses trim and respond logic to dynamically adjust variable air volume (VAV) terminal units’ minimum airflow setpoints based on zones’ ventilation requirements. While simulation results and laboratory testing have estimated the impact on heating, ventilation, and air conditioning (HVAC) systems compared to traditional ventilation, there have been limited applications of RP-1747 in occupied, real-world buildings to date. This paper introduces a real-world implementation of RP-1747 DCV in an institutional office building over an eight-month period, using a supervisory control approach. During this implementation, a complementary temperature setback approach was also developed and employed in the case study building. These changes to the sequences of operation, as well as corrections to other sub-optimal sequences of operation discovered during implementation, resulted in a 36 ± 2 % and 2 ± 6 % reduction in heating and cooling energy use, respectively, while improving per person ventilation rates in the case study. The results aim to contribute to the body of literature on this emerging DCV approach and provide anecdotal evidence of its benefits and interactions with other control logic in a real-world application, while also demonstrating the benefits of supervisory control when implementing complex process control functions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"327 ","pages":"Article 115087"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing indoor environmental prediction in smart buildings: A comparative analysis of deep learning models","authors":"Roupen Minassian,&nbsp;Adriana-Simona Mihăiţă,&nbsp;Arezoo Shirazi","doi":"10.1016/j.enbuild.2024.115086","DOIUrl":"10.1016/j.enbuild.2024.115086","url":null,"abstract":"<div><div>This paper presents a comprehensive investigation into the application of deep learning models for predicting indoor environmental quality in smart buildings. Using data collected from a network of microclimate sensors deployed across a university campus in Sydney, we evaluated the performance of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models. Our study encompassed various aspects of model development, including data preparation, architecture design, hyperparameter optimization, and model interpretability. Contrary to common assumptions in time series forecasting, our results demonstrate that CNN models consistently outperformed LSTM and hybrid models in predicting indoor temperature. We found that multivariate input configurations enhanced prediction accuracy across all model types, highlighting the importance of capturing complex interactions between environmental parameters. Through SHapley Additive exPlanations (SHAP) analysis, we identified temperature, humidity, and Heating, Ventilation, and Air Conditioning (HVAC) status as the most influential features for predictions. Our experiments also revealed optimal configurations for historical input length and prediction horizon, providing practical guidelines for model implementation. This research contributes valuable insights for the development of more efficient and accurate smart building management systems, potentially leading to improved energy efficiency and occupant comfort in built environments.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"327 ","pages":"Article 115086"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sinergym – A virtual testbed for building energy optimization with Reinforcement Learning","authors":"Alejandro Campoy-Nieves,&nbsp;Antonio Manjavacas,&nbsp;Javier Jiménez-Raboso,&nbsp;Miguel Molina-Solana,&nbsp;Juan Gómez-Romero","doi":"10.1016/j.enbuild.2024.115075","DOIUrl":"10.1016/j.enbuild.2024.115075","url":null,"abstract":"<div><div>Simulation has become a crucial tool for Building Energy Optimization (BEO) as it enables the evaluation of different design and control strategies at a low cost. Machine Learning (ML) algorithms can leverage large-scale simulations to learn optimal control from vast amounts of data without supervision, particularly under the Reinforcement Learning (RL) paradigm. Unfortunately, the lack of open and standardized tools has hindered the widespread application of ML and RL to BEO. To address this issue, this paper presents <span>Sinergym</span>, an open-source Python-based virtual testbed for large-scale building simulation, data collection, continuous control, and experiment monitoring. <span>Sinergym</span> provides a consistent interface for training and running controllers, predefined benchmarks, experiment visualization and replication support, and comprehensive documentation in a ready-to-use software library. This paper 1) highlights the main features of <span>Sinergym</span> in comparison to other existing frameworks, 2) describes its basic usage, and 3) demonstrates its applicability for RL-based BEO through several representative examples. By integrating simulation, data, and control, <span>Sinergym</span> supports the development of intelligent, data-driven applications for more efficient and responsive building operations, aligning with the objectives of digital twin technology.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"327 ","pages":"Article 115075"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon reductions through optimized solar heat gain glass properties considering future climate and grid emissions: case study of Chicago’s residential buildings","authors":"Yiwei Lyu ,&nbsp;Jialiang Xiang ,&nbsp;Holly Samuelson","doi":"10.1016/j.enbuild.2024.115080","DOIUrl":"10.1016/j.enbuild.2024.115080","url":null,"abstract":"<div><div>Existing resources leave confusion over the benefits of high versus low Solar Heat Gain Coefficient (SHGC) windows for energy performance in residential buildings retrofits in cold climates. Additionally, few studies have considered the impact of expected future climate conditions and time-variable grid emission rates on energy-related metrics. Utilizing the ResStock, residential building stock models from the National Renewable Energy Laboratory (NREL), this study investigates retrofits increasing the SHGC of windows in Chicago, a cold US city. The results indicate that increasing window SHGC increases summer cooling needs; however, in most cases, this effect is more than offset by reduced winter heating needs. This balance is particularly beneficial considering the state’s expected long-run marginal carbon emission rates. The study also examines the combined effects of high SHGC with improved window insulation values, demonstrating that such strategic window retrofits not only enhance overall building energy performance but also contribute to greater emission reductions. On average, the current Chicago residences (n = 4,826) save 4.6 % on heating and cooling carbon emissions by increasing the SHGC of the windows. If we assume that those homes are upgraded with heat pumps (electrification), a popular retrofit that reduces heating-related carbon emissions in particular, the increased window SHGC saves 2.5 % of long-run marginal carbon emissions. These results provide new insight into the carbon benefits of higher SHGC replacement windows in a cold climate. The benefits are significant, even considering future trends of a warming climate, higher demand grid emissions, and building electrification.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"327 ","pages":"Article 115080"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary game for incentivizing social cooperation of prosumers in transactive energy communities","authors":"Dan Mitrea,&nbsp;Tudor Cioara,&nbsp;Ionut Anghel,&nbsp;Liana Toderean","doi":"10.1016/j.enbuild.2024.115057","DOIUrl":"10.1016/j.enbuild.2024.115057","url":null,"abstract":"<div><div>Transactive energy communities offer significant potential for the smart grid’s transition to renewable energy. However, they typically rely on fixed trading contracts between prosumers, which limits flexibility and engagement. Extensive research has been conducted on the mechanisms for implementing transactive energy communities, but there is still limited focus on prosumers’ trading behaviours and their social cooperation in peer-to-peer trading. In this work, we are exploring the potential of evolutionary game theory for modelling the evolution of trading behaviours among community prosumers over time aiming to define a blockchain market mechanism that promotes social cooperation-based P2P interactions. The model, inspired by the Hawk-Dove game, incentivizes prosumers’ cooperative behaviours (Doves) and penalizes prosumers with self-centred trading behaviours (Hawks), fostering social cooperation despite the temptation of higher payoffs from defection. In trading, Doves avoid conflicts and are open to cooperation, while Hawks are not. During a market session, if both prosumers choose the Hawk trading strategy, they are penalized with higher trading costs. If one chooses the Hawk and the other Dove, the Hawk gains all the benefits, and the Dove gains nothing. If both choose Dove, they share the benefits equally. We have evaluated the effectiveness of the solution with a blockchain-based energy marketplace considering realistic scenarios using energy data from prosumers. Results suggest that matched pairs of prosumers in P2P energy transactions gradually shift towards altruistic behaviours. Additionally, at the end of the market session, over 92% of the transactions involve altruistic behaviours, therefore, the prosumers are effectively incentivized to change their behaviour. Moreover, it may significantly increase traded energy in market sessions by up to 50% driven by cooperative trading behaviours of prosumers which unlock higher levels of energy flexibility. Finally, it outperforms the market clearing mechanism in transactions energy prices by up to 3%.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"327 ","pages":"Article 115057"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting building operation dynamics using a Physics-Informed Spatio-Temporal Graph Neural Network (PISTGNN) ensemble","authors":"Jongseo Lee ,&nbsp;Sungzoon Cho","doi":"10.1016/j.enbuild.2024.115085","DOIUrl":"10.1016/j.enbuild.2024.115085","url":null,"abstract":"<div><div>Forecasting future building operation states provides operators with comprehensive insights, allowing them to understand and optimize the factors influencing various aspects of building performance, including energy consumption. While conventional modeling tools such as EnergyPlus are widely employed to predict the behavior of buildings, they often struggle to capture the full complexity of real-world operational dynamics, as their outputs are greatly affected by the assumptions made during the modeling process and due to the stochasticity associated with real-world building operations. In this regard, this paper investigates the Physics-Informed Deep Spatio-Temporal Graph Neural Network (PISTGNN) Ensemble, which integrates residual learning and physics constraints into an encoder-decoder structured Diffusion Convolutional Recurrent Neural Network (DCRNN), to precisely estimate building operational dynamics 5 minutes in advance. The experimental results demonstrate that the Ensemble model achieved an average improvement of 44.7% in RMSE over the pure data-driven model across seasonal test sets, underscoring its robustness. Moreover, the model's predictions deviate by only 0.78% from the true values in real-world scenarios, highlighting its exceptional accuracy and reliability for practical applications. PINN integration enhances the model's capability to manage compounding errors in data-sparse regions, reducing model uncertainty.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"328 ","pages":"Article 115085"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of phase change material based cool pack performance in vests for personalized thermal comfort in extremely hot climatic conditions: Model development and case studies","authors":"Ankur Gupta ,&nbsp;Amit Shrivastava ,&nbsp;Hardik B. Kothadia ,&nbsp;Prodyut R. Chakraborty","doi":"10.1016/j.enbuild.2024.115058","DOIUrl":"10.1016/j.enbuild.2024.115058","url":null,"abstract":"<div><div>Vests with provisions for carrying multiple phase change material (PCM) based cool packs are one of the possible ways to provide personalized thermal comfort in extremely hot climatic conditions. Although this passive cooling mechanism is simple to implement, there are several challenges associated with the design of these heat packs. For instance, the heat packs must maintain the skin temperature within the comfortable range of <span><math><mn>33</mn><mo>±</mo><mn>2</mn></math></span> <span><math><mmultiscripts><mrow><mi>C</mi></mrow><mprescripts></mprescripts><none></none><mrow><mi>o</mi></mrow></mmultiscripts></math></span> (87.8-95 <span><math><mmultiscripts><mrow><mi>F</mi></mrow><mprescripts></mprescripts><none></none><mrow><mi>o</mi></mrow></mmultiscripts></math></span>) for the desired duration while also being as lightweight as possible. For the heat pack design, it is important to consider not only the hot climatic condition that causes heat to flow from ambient to body surface but also the heat generation from the body itself for different activity levels, such as reclining, standing, walking, jogging, running, and so on. In the present work, a numerical framework has been developed and experimental validation performed to investigate the performance of cool packs when it is loaded with any one of the following five PCMs, namely Ice, savE OM 21, Coconut oil, C18 paraffin, and Octadecane. The comparative performance of these cool packs is assessed based on the comfort duration for which they can maintain the body surface temperature within the specified range of <span><math><mn>33</mn><mo>±</mo><mn>2</mn></math></span> <span><math><mmultiscripts><mrow><mi>C</mi></mrow><mprescripts></mprescripts><none></none><mrow><mi>o</mi></mrow></mmultiscripts></math></span>. SavE OM21 PCM exhibited excellent performance in terms of providing thermal comfort more precisely. However, thermal conductivity must be enhanced from 0.14-0.21 <span><math><mi>W</mi><mo>/</mo><mi>m</mi><mi>K</mi></math></span> to 1.0 <span><math><mi>W</mi><mo>/</mo><mi>m</mi><mi>K</mi></math></span>. The lower masses of C18 paraffin and Octadecane PCM were deemed suitable for practical use in real-world scenarios.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"327 ","pages":"Article 115058"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Framework to select robust energy retrofit measures for residential communities","authors":"Lei Shu ,&nbsp;Tianzhen Hong ,&nbsp;Kaiyu Sun ,&nbsp;Dong Zhao","doi":"10.1016/j.enbuild.2024.115077","DOIUrl":"10.1016/j.enbuild.2024.115077","url":null,"abstract":"<div><div>Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework’s robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework’s effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework’s potential for broader applications in optimizing community-scale residential energy retrofits.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"327 ","pages":"Article 115077"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing energy savings verification in industrial settings using deep learning and anomaly detection within the IPMVP framework","authors":"Suziee Sukarti ,&nbsp;Mohamad Fani Sulaima ,&nbsp;Aida Fazliana Abdul Kadir ,&nbsp;Nur Izyan Zulkafli ,&nbsp;Mohammad Lutfi Othman ,&nbsp;Dawid P. Hanak","doi":"10.1016/j.enbuild.2024.115096","DOIUrl":"10.1016/j.enbuild.2024.115096","url":null,"abstract":"<div><div>This study advances industrial energy Measurement and Verification (M&amp;V) practices by integrating Deep Learning (DL) techniques with automated anomaly detection, challenging traditional M&amp;V reliance on manual non-routine adjustments. The research explores whether automated, data-driven anomaly detection can replace these adjustments, enhancing accuracy and efficiency in energy savings verification post-energy conservation measures (ECMs)—a critical need for industrial applications. Utilizing a dataset with 30-minute to weekly interval readings, CNN, DNN, and RNN models were applied across 12 datasets to identify the most effective model for baseline prediction using key IPMVP performance metrics (CVRMSE, NMBE, R2) alongside MAPE and RMSE. The baseline modelling findings indicate that DNN performs optimally at 30-minute intervals (R2 = 0.9600, RMSE = 22.82), hourly intervals (R2 = 0.9581, RMSE = 23.27), and daily intervals (R2 = 0.9347, RMSE = 28.00). CNN, however, demonstrated the best performance for weekly intervals (R2 = 0.8875, RMSE = 31.91). DNN provides the best overall performance across most intervals, offering a reliable balance of accuracy and practicality for regular energy baseline prediction. For anomaly detection and savings impact, the 30-minute RNN model achieved the highest estimated savings of 4.38 million kWh which translates to 27.35 % of the total energy consumption of 16,000,000 kWh with a low standard error (0.634 kWh), demonstrating strong predictive precision. Across all frequencies, savings estimates exceeded twice the standard error, meeting IPMVP acceptability criteria and confirming the robustness of this approach. These findings substantiate that deep learning-based anomaly detection can effectively replace traditional non-routine adjustments, providing a reliable, streamlined solution for energy savings calculations. Visualizations within the study illustrate the model’s enhancements, with comparative charts showing both original and anomaly-adjusted energy consumption and savings. This study contributes to the M&amp;V field by demonstrating that, when integrated into the IPMVP framework, anomaly detection offers an efficient and accurate method for energy savings verification, paving the way for more streamlined, data-driven M&amp;V processes in industrial settings. Additionally, it provides insights into optimizing deep learning models for energy data analysis, supporting quicker, more precise energy management decisions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"327 ","pages":"Article 115096"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PV on façades: A financial, technical and environmental assessment","authors":"W.L. Schram ,&nbsp;E. Shirazi","doi":"10.1016/j.enbuild.2024.115010","DOIUrl":"10.1016/j.enbuild.2024.115010","url":null,"abstract":"<div><div>As roof space has become increasingly occupied, facades serve as an alternative for deploying PV, often in the form of Building-Integrated Photovoltaics (BIPV). In this study, a comprehensive analysis over multiple years of the value of integrating PV on facades with different configurations is provided. The comparison comprises financial, technical and environmental metrics and spans the Dutch and German electricity sector. As expected, due to the much higher yield of optimally oriented PV, it generates higher revenues and reduce more <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions than facade PV. Over the course of five years of this study, south, east, and west facades reduce 1725, 1492, 1335 kg of <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> emission per kWp of PV installation, while this is 2434 for the optimally oriented PV. However, analyzing the value factor of PV-generated electricity shows east and west facade are increasingly generating more economic value compared to optimally-oriented PV: the value factor or capture rate of optimally-oriented PV decreased to 0.73 in 2023, whereas the value factor for east facade and west facade remained higher (0.87 and 0.84, respectively). From a technical viewpoint, facade PV results in much higher self-consumption ratios 42% for east facade, 46% for west facade, compared to only 30% for optimally-oriented (all 1 kWp installed per 1 MWh demand). Concluding, facade PV, while having a much lower yield than optimally oriented PV, does hold some significant advantages over optimally-oriented PV – mainly in technical terms and in economic terms. Therefore, facade PV can be seen as a very promising option for the PV sector in general, and the BIPV sector specifically – especially in a more and more congested electricity grid.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"328 ","pages":"Article 115010"},"PeriodicalIF":6.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}