Energy and AI最新文献_第2页

Machine learning identification of Electric Vehicles from charging session data

IF 9.6

Energy and AI Pub Date : 2025-03-17 DOI: 10.1016/j.egyai.2025.100502

Federico Ferretti, Antonio De Paola

{"title":"Machine learning identification of Electric Vehicles from charging session data","authors":"Federico Ferretti, Antonio De Paola","doi":"10.1016/j.egyai.2025.100502","DOIUrl":"10.1016/j.egyai.2025.100502","url":null,"abstract":"<div><div>Alternating Current (AC) charging is currently the most cost-effective and widely adopted solution for charging of Electric Vehicles (EVs). However, the existing AC charging infrastructure generally exhibits limited communication capabilities with the connected EVs, as information about the vehicle can only be collected through external logging systems that operate independently of the charger itself. A straightforward and interoperable method for extracting information from charging vehicles (e.g., vehicle model, battery capacity, and State of Charge) could significantly enhance the implementation of advanced smart charging strategies, unlocking the flexibility of connected EVs, enabling cost reductions and supporting the provision of ancillary services to the grid. This article implements a novel machine-learning approach to estimate relevant information on AC charging vehicles in a real-world experimental setting designed and implemented by the authors. The proposed approach does not require any hardware adjustment and is capable of predicting several features of the connected EVs (e.g., brand, model, year, battery capacity, End-of-Charge status) by exclusively considering their charging profile in response to specific prescribed current setpoints. Possible applications of the model range from the design of smart charging facilities capable of identifying regular users and forecasting their charging patterns to the real-time estimation of the aggregate flexibility of connected EVs, an essential component in vehicle-to-grid (V2G) applications. Extensive practical demonstrations based on experimental data are provided to validate the identification procedure. An example of flexibility envelope estimation of charging EVs is also included to demonstrate the potential applications of the proposed method for ancillary services provision.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100502"},"PeriodicalIF":9.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human-in-the-loop control strategy for IoT-based smart thermostats with Deep Reinforcement Learning

IF 9.6

Energy and AI Pub Date : 2025-03-15 DOI: 10.1016/j.egyai.2025.100490

Payam Fatehi Karjou, Fabian Stupperich, Phillip Stoffel, Drk Müller

{"title":"Human-in-the-loop control strategy for IoT-based smart thermostats with Deep Reinforcement Learning","authors":"Payam Fatehi Karjou, Fabian Stupperich, Phillip Stoffel, Drk Müller","doi":"10.1016/j.egyai.2025.100490","DOIUrl":"10.1016/j.egyai.2025.100490","url":null,"abstract":"<div><div>Thermostatic Radiator Valves (TRVs) are a widely used technology for regulating room heating in Europe countries. Smart TRVs can provide significant energy savings, often ranging from 20–40% compared to conventional heating systems. They use sensors and algorithms to learn user behavior and optimize heating schedules accordingly. They can often be easily retrofitted to existing heating systems, making them a practical option for enhancing energy efficiency in present buildings, especially in office buildings due to their highly dynamic operational patterns. This work presents a novel human-in-the-loop control strategy for Internet of Things (IoT)-based TRVs using Deep Reinforcement Learning (DRL). A key focus of this research is enhancing the adaptability of agents’ behavior by implementing a more generic and flexible Markov Decision Process (MDP) to promote policy generalization across diverse scenarios. The study explores the challenges of transferring control behaviors from simulation environments to real-world settings, examining the performance across different thermal zones and evaluating the integration flexibility of the control strategy within building systems. Real-world occupant behavior is incorporated, including dynamic comfort preferences and occupancy predictions, to better align thermostat operation with user preferences. Furthermore, this paper discusses the practical challenges encountered during implementation, including battery consumption of IoT devices, integration of occupancy detection and prediction systems, and maintenance requirements. By addressing these issues, the proposed control strategy seeks to improve the scalability and feasibility of IoT-based TRVs, thereby providing a viable solution for their widespread deployment in buildings.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100490"},"PeriodicalIF":9.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-constrained transfer learning: Open-circuit voltage curve reconstruction and degradation mode estimation of lithium-ion batteries

IF 9.6

Energy and AI Pub Date : 2025-03-14 DOI: 10.1016/j.egyai.2025.100493

Tobias Hofmann , Jacob Hamar , Bastian Mager , Simon Erhard , Jan Philipp Schmidt

{"title":"Physics-constrained transfer learning: Open-circuit voltage curve reconstruction and degradation mode estimation of lithium-ion batteries","authors":"Tobias Hofmann , Jacob Hamar , Bastian Mager , Simon Erhard , Jan Philipp Schmidt","doi":"10.1016/j.egyai.2025.100493","DOIUrl":"10.1016/j.egyai.2025.100493","url":null,"abstract":"<div><div>Open-circuit voltage (OCV) updates are the key to accurate state of charge (SOC) estimates over lifetime. Degradation modes (DM) are directly coupled to OCV estimation. They offer a more detailed analysis of the battery’s state of health (SOH) and yield optimized usage strategy, and with that, a prolonged lifetime. In this study two data-driven models are coupled with physics-based models and compared in regards of their OCV and DM estimation accuracy: Two temporal convolutional — long short-term memory neural networks (TCN-LSTM) are trained from synthetic NCA-graphite battery data for OCV curve estimation (model 1) and alignment parameter estimation (model 2). Both models are fine-tuned with varying amounts of experimental NMC-graphite battery data during the transfer learning (TL) step. In the subsequent physics-constraining part the DMs are derived via optimization (model 1), i.e., fitting the OCV with half cell open-circuit potentials, or directly via mathematical equations (model 2). Both models prove that fine-tuning data from one aging path suffices, if it includes the maximum appearing DMs of the target domain. For these use cases both models maintain OCV mean absolute errors (MAEs), DM MAEs and SOH mean absolute percentage errors (MAPEs) under 10mV, 3.10% and 1.98%, respectively. The model 2 has less computational complexity and reaches slightly better results but requires labeled target data including alignment parameters for its application. This study shows that synthetic data is eligible for TL, even for varying cell chemistries, and that the mechanistic model helps to physically constrain the output.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100493"},"PeriodicalIF":9.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance assessment of machine learning techniques in electronic nose systems for power transformer fault detection

IF 9.6

Energy and AI Pub Date : 2025-03-13 DOI: 10.1016/j.egyai.2025.100497

Sergi Torres Araya , Jorge Ardila-Rey , Matías Cerda Luna , Jorge Portilla , Suganya Govindarajan , Camilo Alvear Jorquera , Roger Schurch

{"title":"Performance assessment of machine learning techniques in electronic nose systems for power transformer fault detection","authors":"Sergi Torres Araya , Jorge Ardila-Rey , Matías Cerda Luna , Jorge Portilla , Suganya Govindarajan , Camilo Alvear Jorquera , Roger Schurch","doi":"10.1016/j.egyai.2025.100497","DOIUrl":"10.1016/j.egyai.2025.100497","url":null,"abstract":"<div><div>Oil-filled transformers are critical assets in electrical power systems, both economically and operationally. Their condition is assessed through insulation system, which is greatly affected by various degradation mechanisms. Hence, effective fault diagnosis is essential to prolong their lifespan. Early detection and correction of incipient faults through Dissolved Gas Analysis (DGA) are crucial to prevent irreversible damage. Current measurement systems have significant limitations that impede their use in routine monitoring and underscore the need for new, accessible technologies that are both technically and economically viable to efficiently detect incipient faults.</div><div>This study evaluates the performance of various Machine Learning (ML) techniques to predict the concentrations of hydrogen (H₂), methane (CH₄), acetylene (C₂H₂), ethylene (C₂H₄), and ethane (C₂H₆) in oil samples subjected to different types of electrical faults, using data from a novel electronic nose (E-Nose) equipped with eleven MOS-type gas sensors. The evaluated ML techniques include Linear Regression (LR), Multivariate Linear Regression (MLR), Principal Component Regression (PCR), Multilayer Perceptron (MLP), Partial Least Squares Regression (PLS), Support Vector Regression (SVR), and Random Forest Regression (RFR). Experimental results from 218 measurement processes revealed that RFR and MLP models exhibited superior performance, with RFR achieving the highest accuracy for predicting H₂, C₂H₂, and C₂H₆, while MLP excelled for CH₄ and C₂H₄. A comparison with a commercial DGA system using the Duval Pentagon Method confirmed the effectiveness of these models in diagnosing transformer faults. These findings underscore the potential of combining E-Noses with ML techniques as an innovative and efficient solution for early fault diagnosis.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100497"},"PeriodicalIF":9.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic ultra-short-term solar photovoltaic power forecasting using natural gradient boosting with attention-enhanced neural networks

IF 9.6

Energy and AI Pub Date : 2025-03-11 DOI: 10.1016/j.egyai.2025.100496

Zhe Song , Fu Xiao , Zhe Chen , Henrik Madsen

{"title":"Probabilistic ultra-short-term solar photovoltaic power forecasting using natural gradient boosting with attention-enhanced neural networks","authors":"Zhe Song , Fu Xiao , Zhe Chen , Henrik Madsen","doi":"10.1016/j.egyai.2025.100496","DOIUrl":"10.1016/j.egyai.2025.100496","url":null,"abstract":"<div><div>Probabilistic forecasting provides insights in estimating the uncertainty of photovoltaic (PV) power forecasts. In this study, an innovative probabilistic ultra-short-term PV power forecasting framework that integrates natural gradient boosting (NGBoost) and deep neural networks is developed. Specifically, an attention-enhanced neural network combining convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks is employed for feature engineering to extract abstract features from time-series data. The extracted features are then fed into an optimized NGBoost model to yield probabilistic forecasts. In comparison to the benchmark models, i.e., the recently reported quantile regression (QR)-based deep learning methods and NGBoost, the proposed model demonstrates an enhanced ability to capture variation patterns in PV power output, further improving the forecast skill score by approximately 15–60 % in deterministic forecasting. In terms of probabilistic forecasting, the proposed model shows superior forecast reliability and sharpness compared to all benchmark methods. Its continuous ranked probability score (CRPS) ranges from 0.0710 kW to 0.0898 kW, achieving reductions of approximately 21–43 % over QR-based models and 29–40 % over NGBoost. Furthermore, within confidence intervals of 10–90 %, the proposed model consistently maintains higher coverage probabilities along with narrower average forecast intervals, as evidenced by a lower Winkler score (WS) than the benchmark models. The findings of this study provide insightful references for probabilistic PV power forecasting research, contributing to efficient solar power management and dispatch.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100496"},"PeriodicalIF":9.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative framework for evaluating machine learning models in forecasting electricity demand for port microgrids

IF 9.6

Energy and AI Pub Date : 2025-03-11 DOI: 10.1016/j.egyai.2025.100494

Alexander Micallef , Maurice Apap , John Licari , Cyril Spiteri Staines , Zhaoxia Xiao

{"title":"A comparative framework for evaluating machine learning models in forecasting electricity demand for port microgrids","authors":"Alexander Micallef , Maurice Apap , John Licari , Cyril Spiteri Staines , Zhaoxia Xiao","doi":"10.1016/j.egyai.2025.100494","DOIUrl":"10.1016/j.egyai.2025.100494","url":null,"abstract":"<div><div>This study presents a framework for forecasting electricity demand in port microgrids using advanced machine learning models, including Random Forest, Least Squares Boosting Ensemble, and Gaussian Process Regression. These models were evaluated under different forecasting setups (fixed origin, expanding windows, and rolling windows) and compared against simpler baseline methods, such as Linear Regression and Naive models. The study assessed the effectiveness of machine learning models in handling dynamic electricity demand patterns in port environments and highlighted the advantages of data-driven models. Results indicate that the Random Forest (expanding window) model outperforms the other models, achieving a root mean square error of 1.1848 MW and a mean average percentage error of 7.2483 %. Gaussian Process Regression with Exponential kernel follows closely with a root mean square error of 1.1904 MW and a mean average percentage error of 7.5017 %. In contrast, the Naive Method (previous day) shows the poorest performance with a root mean square error of 4.5357 MW and a mean average percentage error of 18.1485 %. Partial Dependence Plots reveal that features such as weighted port calls play a significant role in improving prediction accuracy. These findings highlight the effectiveness of machine learning models in accurately forecasting port microgrid demand and optimizing energy management.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100494"},"PeriodicalIF":9.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Review of machine learning techniques for energy sharing and biomass waste gasification pathways in integrating solar greenhouses into smart energy systems 将太阳能温室纳入智能能源系统的能源共享和生物质废物气化途径的机器学习技术综述

IF 9.6

Energy and AI Pub Date : 2025-03-11 DOI: 10.1016/j.egyai.2025.100498

Navid Mahdavi, Animesh Dutta, Syeda Humaira Tasnim, Shohel Mahmud

{"title":"Review of machine learning techniques for energy sharing and biomass waste gasification pathways in integrating solar greenhouses into smart energy systems","authors":"Navid Mahdavi, Animesh Dutta, Syeda Humaira Tasnim, Shohel Mahmud","doi":"10.1016/j.egyai.2025.100498","DOIUrl":"10.1016/j.egyai.2025.100498","url":null,"abstract":"<div><div>The integration of solar greenhouses into smart energy systems (SESs) remains largely unexplored, despite their potential to enhance energy sharing and hydrogen production. This review investigates the role of solar greenhouses as active energy contributors within SESs, emphasizing their biomass waste gasification for hydrogen production and their integration into district heating and cooling (DHC) networks. A structured classification of machine learning (ML) and deep learning (DL) techniques applied in forecasting and optimizing these processes is provided. Additionally, the evolution of DHC systems is analyzed, with a focus on fifth-generation DHC (5GDHC) networks, which facilitate bidirectional energy exchange at near-ambient temperatures. The review highlights that existing studies have predominantly addressed SES advancements and ML-driven energy management without considering the contributions of solar greenhouses. A novel framework is proposed, illustrating their role as prosumers capable of exchanging electricity, hydrogen, and thermal energy within SESs. Key findings reveal that integrating solar greenhouses with SESs can enhance energy efficiency, reduce carbon emissions, and improve system resilience. Furthermore, ML-driven predictive control strategies, particularly model predictive control (MPC), are identified as essential for optimizing real-time energy flows and biomass gasification processes. This study provides a foundation for future research on the technical, economic, and environmental feasibility of integrating greenhouses into SESs. The insights presented offer a pathway toward more sustainable, AI-driven energy-sharing networks, supporting policymakers and industry stakeholders in the transition toward low-carbon energy solutions.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100498"},"PeriodicalIF":9.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-objective optimization of hybrid electric vehicles energy management using multi-agent deep reinforcement learning framework

IF 9.6

Energy and AI Pub Date : 2025-03-02 DOI: 10.1016/j.egyai.2025.100491

Xiaoyu Li , Zaihang Zhou , Changyin Wei , Xiao Gao , Yibo Zhang

{"title":"Multi-objective optimization of hybrid electric vehicles energy management using multi-agent deep reinforcement learning framework","authors":"Xiaoyu Li , Zaihang Zhou , Changyin Wei , Xiao Gao , Yibo Zhang","doi":"10.1016/j.egyai.2025.100491","DOIUrl":"10.1016/j.egyai.2025.100491","url":null,"abstract":"<div><div>Hybrid electric vehicles (HEVs) have the advantages of lower emissions and less noise pollution than traditional fuel vehicles. Developing reasonable energy management strategies (EMSs) can effectively reduce fuel consumption and improve the fuel economy of HEVs. However, current EMSs still have problems, such as complex multi-objective optimization and poor algorithm robustness. Herein, a multi-agent reinforcement learning (MADRL) framework is proposed based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve such problems. Specifically, a vehicle model and dynamics model are established, and based on this, a multi-objective EMS is developed by considering fuel economy, maintaining the battery State of Charge (SOC), and reducing battery degradation. Secondly, the proposed strategy regards the engine and battery as two agents, and the agents cooperate with each other to realize optimal power distribution and achieve the optimal control strategy. Finally, the WLTC and HWFET driving cycles are employed to verify the performances of the proposed method, the fuel consumption decreases by 26.91 % and 8.41 % on average compared to the other strategies. The simulation results demonstrate that the proposed strategy has remarkable superiority in multi-objective optimization.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100491"},"PeriodicalIF":9.6,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geospatial Mapping of Large-Scale Electric Power Grids: A Residual Graph Convolutional Network-Based Approach with Attention Mechanism

IF 9.6

Energy and AI Pub Date : 2025-03-01 DOI: 10.1016/j.egyai.2025.100486

Razzaqul Ahshan , Md. Shadman Abid , Mohammed Al-Abri

{"title":"Geospatial Mapping of Large-Scale Electric Power Grids: A Residual Graph Convolutional Network-Based Approach with Attention Mechanism","authors":"Razzaqul Ahshan , Md. Shadman Abid , Mohammed Al-Abri","doi":"10.1016/j.egyai.2025.100486","DOIUrl":"10.1016/j.egyai.2025.100486","url":null,"abstract":"<div><div>Precise geospatial mapping of grid infrastructure is essential for the effective development and administration of large-scale electrical infrastructure. The application of deep learning techniques in predicting regional energy network architecture utilizing extensive datasets of geographical information systems (GISs) has yet to be thoroughly investigated in previous research works. Moreover, although graph convolutional networks (GCNs) have been proven to be effective in capturing the complex linkages within graph-structured data, the computationally demanding nature of modern energy grids necessitates additional computational contributions. Hence, this research introduces a novel residual GCN with attention mechanism for mapping critical energy infrastructure components in geographic contexts. The proposed model accurately predicts the geographic locations and links of large-scale grid infrastructure, such as poles, electricity service points, and substations. The proposed framework is assessed on the Sultanate of Oman’s regional energy grid and further validated on Nigeria’s electricity transmission network database. The obtained findings showcase the model’s capacity to accurately predict infrastructure components and their spatial relationships. Results show that the proposed method achieves a link-prediction accuracy of 95.88% for the Omani network and 92.98% for the Nigerian dataset. Furthermore, the proposed model achieved <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values of 0.99 for both datasets in terms of regression. Therefore, the proposed architecture facilitates multifaceted assessment and enhances the capacity to capture the inherent geospatial aspects of large-scale energy distribution networks.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100486"},"PeriodicalIF":9.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads

IF 9.6

Energy and AI Pub Date : 2025-02-25 DOI: 10.1016/j.egyai.2025.100487

Luca Massidda, Marino Marrocu

{"title":"Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads","authors":"Luca Massidda, Marino Marrocu","doi":"10.1016/j.egyai.2025.100487","DOIUrl":"10.1016/j.egyai.2025.100487","url":null,"abstract":"<div><div>Demand-side flexibility is crucial to balancing supply and demand, as renewable energy sources are increasingly integrated into the energy mix, and heating and transport systems are becoming more and more electrified. Historically, this balancing has been managed from the supply side. However, the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants, increasing the importance of demand response (DR) techniques to achieve the required flexibility. Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer, a task complicated by numerous influencing variables. Based on a top-down approach, this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads. We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads, subject to flexibility, which is simulated by a virtual battery model. The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions. The results demonstrate the model’s ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption. The model achieves a mean absolute percentage error (MAPE) lower than 17.0%, comparable to the accuracy without flexibility. The results obtained are compared with a direct data-driven approach, demonstrating the validity and effectiveness of our model.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100487"},"PeriodicalIF":9.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0