Energy and AI最新文献

{"title":"Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads","authors":"Luca Massidda,&nbsp;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}

{"title":"Advancing oil and gas emissions assessment through large language model data extraction","authors":"Zhenlin Chen ,&nbsp;Roujia Zhong ,&nbsp;Wennan Long ,&nbsp;Haoyu Tang ,&nbsp;Anjing Wang ,&nbsp;Zemin Liu ,&nbsp;Xuelin Yang ,&nbsp;Bo Ren ,&nbsp;James Littlefield ,&nbsp;Sanmi Koyejo ,&nbsp;Mohammad S. Masnadi ,&nbsp;Adam R. Brandt","doi":"10.1016/j.egyai.2025.100481","DOIUrl":"10.1016/j.egyai.2025.100481","url":null,"abstract":"<div><div>The oil and gas industry strives to improve environmental stewardship and reduce its carbon footprint, but lacks comprehensive global operational data for accurate environmental assessment and decision-making. This challenge is compounded by dispersed information sources and the high costs of accessing proprietary databases. This paper presents an innovative framework using Large Language Models (LLMs) – specifically GPT-4 and GPT-4o – to extract critical oil and gas asset information from diverse literature sources.</div><div>Our framework employs iterative comparisons between GPT-4’s output and a dataset of 129 ground truth documents labeled by domain experts. Through 11 training and testing iterations, we fine-tuned prompts to optimize information extraction. The evaluation process assessed performance using true positive rate, precision, and F1 score metrics. The framework achieved strong results, with a true positive rate of 83.74% and an F1 score of 78.16% on the testing dataset.</div><div>The system demonstrated remarkable efficiency, processing 32 documents in 61.41 min with GPT-4o, averaging 7.09 s per extraction - a substantial improvement over the manual method. Cost-effectiveness was also achieved, with GPT-4o reducing extraction costs by a factor of 10 compared to GPT-4.</div><div>This research has significant implications for the oil and gas industry. By creating an organized, transparent, and accessible database, we aim to democratize access to critical information. The framework supports more accurate climate modeling efforts, enhances decision-making processes for operations and investments, and contributes to the sector’s ability to meet environmental commitments. These improvements particularly impact emissions reduction and energy transition strategies, potentially transforming how data is extracted and utilized in this field and beyond.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100481"},"PeriodicalIF":9.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520623","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}

{"title":"Metaheuristic multi-objective optimization with artificial neural networks surrogate modeling for optimal energy-economic performance for CSP technology","authors":"A. Allouhi ,&nbsp;M. Benzakour Amine ,&nbsp;K.A. Tabet Aoul","doi":"10.1016/j.egyai.2025.100488","DOIUrl":"10.1016/j.egyai.2025.100488","url":null,"abstract":"<div><div>Among CSP technologies, the linear Fresnel reflector (LFR) can provide reliable carbon-neutral electricity for large-scale applications. In this study, the performance of a large solar LFR power plant under varying climatic conditions and the dependency of the performance on major plant design specifications, such as solar multiple and full-load thermal storage hours, were examined. Next, artificial neural network (ANN) surrogate models were introduced to predict the annual capacity factor of 100 MWe power plants operating with LFR technology. Single-hidden-layer ANN models with different numbers of neurons in the hidden layer were used and the Levenberg–Marquardt training algorithm was adopted. To overcome overfitting, validation and Bayesian Regularization approaches were compared. As training and testing data, 36 geographical sites with various combinations of design parameters were used. Through multi-objective optimization techniques, including the Multi-Objective Particle-Swarm Optimizer and Multi-Objective Grey Wolf Optimizer coupled with ANN surrogate modeling, this study navigates the trade-offs to identify Pareto-optimal solutions for large-scale LFR-based CSP integration based on the energy and cost criteria. The study also identified Site 4 (S4) as a promising candidate for optimal balance between the capacity factor (51.05%) and specific cost (5246.71$/kW), showcasing the practical implications of the research for sustainable and efficient CSP plant implementation.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100488"},"PeriodicalIF":9.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527504","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}

{"title":"Hybrid transformer model with liquid neural networks and learnable encodings for buildings’ energy forecasting","authors":"Gabriel Antonesi ,&nbsp;Tudor Cioara ,&nbsp;Ionut Anghel ,&nbsp;Ioannis Papias ,&nbsp;Vasilis Michalakopoulos ,&nbsp;Elissaios Sarmas","doi":"10.1016/j.egyai.2025.100489","DOIUrl":"10.1016/j.egyai.2025.100489","url":null,"abstract":"<div><div>Accurate forecasting of buildings' energy demand is essential for building operators to manage loads and resources efficiently, and for grid operators to balance local production with demand. However, nowadays models still struggle to capture nonlinear relationships influenced by external factors like weather and consumer behavior, assume constant variance in energy data over time, and often fail to model sequential data. To address these limitations, we propose a hybrid Transformer-based model with Liquid Neural Networks and learnable encodings for building energy forecasting. The model leverages Dense Layers to learn non-linear mappings to create embeddings that capture underlying patterns in time series energy data. Additionally, a Convolutional Neural Network encoder is integrated to enhance the model's ability to understand temporal dynamics through spatial mappings. To address the limitations of classic attention mechanisms, we implement a reservoir processing module using Liquid Neural Networks which introduces a controlled non-linearity through dynamic reservoir computing, enabling the model to capture complex patterns in the data. For model evaluation, we utilized both pilot data and state-of-the-art datasets to determine the model's performance across various building contexts, including large apartment and commercial buildings and small households, with and without on-site energy production. The proposed transformer model demonstrates good predictive accuracy and training time efficiency across various types of buildings and testing configurations. Specifically, SMAPE scores indicate a reduction in prediction error, with improvements ranging from 1.5 % to 50 % over basic transformer, LSTM and ANN models while the higher R² values further confirm the model's reliability in capturing energy time series variance. The 8 % improvement in training time over the basic transformer model, highlights the hybrid model computational efficiency without compromising accuracy.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100489"},"PeriodicalIF":9.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464875","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}

{"title":"Municipal heat planning within The World Avatar","authors":"Yi-Kai Tsai ,&nbsp;Markus Hofmeister ,&nbsp;Srishti Ganguly ,&nbsp;Kushagar Rustagi ,&nbsp;Yong Ren Tan ,&nbsp;Sebastian Mosbach ,&nbsp;Jethro Akroyd ,&nbsp;Markus Kraft","doi":"10.1016/j.egyai.2025.100479","DOIUrl":"10.1016/j.egyai.2025.100479","url":null,"abstract":"<div><div>This paper presents a novel integration of building energy simulation with The World Avatar (TWA), a dynamic knowledge graph and agent-based framework designed for comprehensive and interoperable digital representation of the world. The study addresses the imperative for accurate and granular building energy data in energy planning scenarios. By leveraging knowledge graph, agents within TWA replace default assumptions in simulation tools with real-time and location-specific input data, such as building geometry, usage, weather, and terrain elevation. This integrated approach automates the simulation process, enabling agents to retrieve input data, execute simulations, and update the knowledge graph with results in a consistent format. To demonstrate this approach, we developed a simulation agent using the City Energy Analyst. Validation against external datasets from Germany and Singapore shows that the agent significantly improves simulation accuracy. The study also highlights the challenges in data acquisition and processing for municipal heat planning, aligning with the requirements of the German Heat Planning Act. Using Pirmasens, a mid-sized city in Germany, as an example, we demonstrate the practical applicability of the agent in municipal heat planning by providing highly granular data on the heating demands and the solar potentials for heat generation. An accompanying economic analysis further evaluates the cost implications and energy storage requirements associated with the installation of solar collectors, and identifies zones in the city with high solar suitability. These insights enable data-driven decision-making, showcasing the potential of this integrated approach to support municipal heat planning.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100479"},"PeriodicalIF":9.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429796","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}

{"title":"Deciphering city-level residential AMI data: An unsupervised data mining framework and case study","authors":"Han Li,&nbsp;Miguel Heleno,&nbsp;Kaiyu Sun,&nbsp;Wanni Zhang,&nbsp;Luis Rodriguez Garcia,&nbsp;Tianzhen Hong","doi":"10.1016/j.egyai.2025.100484","DOIUrl":"10.1016/j.egyai.2025.100484","url":null,"abstract":"<div><div>Buildings account for more than one third of global energy consumption and carbon emissions, making the optimization of their energy use crucial for sustainability. Advanced Metering Infrastructures data offers a rich source of information for understanding and improving building energy performance, yet existing frameworks for leveraging this data are limited. This paper presents a comprehensive data-mining framework for analyzing Advanced Metering Infrastructures data at multiple temporal and spatial scales, beneficial for building owners, operators, and utility companies. Utilizing hourly electricity consumption data for the east region of Portland, Oregon, the study systematically extracts key statistics such as start hour, duration, and peak hour of load periods across daily, weekly, and annual evaluation windows. The framework employs a list of techniques including load-level detection, home vacancy detection, and weather-sensitivity analysis and statistical methods to provide detailed insights into building energy dynamics. As an unsupervised study, it reveals patterns and trends without predefined labels or categories. Key findings highlight the substantial impact of the COVID-19 pandemic on residential energy use, uncover patterns like intraday load variations, weekly consumption trends, and annual weather sensitivity. The insights gained can potentially inform better energy management strategies, support grid operations and planning, guide policy-making for energy efficiency improvements, as well as improve input and assumptions in the building energy modeling. This study opens pathways for future research, including integrating more data sources and collaborating with utility companies to validate hypotheses and further explore building energy use insights.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100484"},"PeriodicalIF":9.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429795","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}

{"title":"Explainable multi-step heating load forecasting: Using SHAP values and temporal attention mechanisms for enhanced interpretability","authors":"Alexander Neubauer,&nbsp;Stefan Brandt,&nbsp;Martin Kriegel","doi":"10.1016/j.egyai.2025.100480","DOIUrl":"10.1016/j.egyai.2025.100480","url":null,"abstract":"<div><div>The role of heating load forecasts in the energy transition is significant, given the considerable increase in the number of heat pumps and the growing prevalence of fluctuating electricity generation. While machine learning methods offer promising forecasting capabilities, their black-box nature makes them difficult to interpret and explain. The deployment of explainable artificial intelligence methodologies enables the actions of these machine learning models to be made transparent.</div><div>In this study, a multi-step forecast was employed using an Encoder–Decoder model to forecast the hourly heating load for an multifamily residential building and a district heating system over a forecast horizon of 24-h. By using 24 instead of 48 lagged hours, the simulation time was reduced from 92.75<!--> <!-->s to 45.80<!--> <!-->s and the forecast accuracy was increased. The feature selection was conducted for four distinct methods. The Tree and Deep SHAP method yielded superior results in feature selection. The application of feature selection according to the Deep SHAP values resulted in a reduction of 3.98% in the training time and a 8.11% reduction in the NRMSE. The utilisation of local Deep SHAP values enables the visualisation of the influence of past input hours and individual features. By mapping temporal attention, it was possible to demonstrate the importance of the most recent time steps in a intrinsic way.</div><div>The combination of explainable methods enables plant operators to gain further insights and trustworthiness from the purely data-driven forecast model, and to identify the importance of individual features and time steps.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100480"},"PeriodicalIF":9.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445462","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}

{"title":"Domain-informed CNN architectures for downscaling regional wind forecasts","authors":"Alexander M. Campbell ,&nbsp;Simon C. Warder ,&nbsp;B. Bhaskaran ,&nbsp;Matthew D. Piggott","doi":"10.1016/j.egyai.2025.100485","DOIUrl":"10.1016/j.egyai.2025.100485","url":null,"abstract":"<div><div>High-resolution wind speed forecasts are of great importance to the wind energy industry, from short-term energy forecasting and trading to longer-term resource assessment and planning. Generating high-resolution regional wind forecasts currently requires compute-intensive numerical models to downscale from a global forecast. Black-box AI models, once trained, can produce results in a fraction of the time and cost; however, they tend to produce smoothed outputs, are not interpretable and generalise poorly. The domain-informed AI architecture presented in this work seeks to address these problems by incorporating prior static fields directly into the model architecture. Specifically, the proposed approach combines two sequential U-Nets – the first upsamples the input wind fields and expands the number of feature maps, a fusion layer then injects prior static data such as topography, and a second U-Net generates the final output wind field. This approach improves all performance metrics versus a baseline U-Net model and generalises better to out-of-sample scenarios. In addition, this study compares the performance of several loss functions, including standard pixel-wise measures such as mean-squared error, structural similarity and frequency-focused functions, and a function based on Wiener filter theory. All loss functions, with the exception of the Wiener loss, perform comparably and tend to attenuate higher-frequency detail. Although the Wiener loss encourages higher frequencies, it over-estimates amplitudes. A composite Wiener-L1 loss function balances generating high-frequency detail and correctly predicting amplitudes.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100485"},"PeriodicalIF":9.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474432","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}

{"title":"Physics-informed machine learning for enhanced prediction of condensation heat transfer","authors":"Haeun Lee ,&nbsp;Cheonkyu Lee ,&nbsp;Hyoungsoon Lee","doi":"10.1016/j.egyai.2025.100482","DOIUrl":"10.1016/j.egyai.2025.100482","url":null,"abstract":"<div><div>Developing a universal model for predicting condensation heat transfer coefficients remains challenging, particularly for steam–non-condensable gas mixtures, owing to the intricate nonlinear interactions between multiphase flow, heat, and mass transfer phenomena. Data-driven machine learning (ML) shows promise in efficiently and accurately predicting condensation heat transfer coefficients. Research has employed various ML methods—multilayer perceptron neural networks, convolutional-neural-network–based DenseNet, backpropagation neural networks, etc.—to investigate steam condensation with non-condensable gases. However, these exhibit limited extrapolation ability and heavily rely on data quantity owing to their black-box nature. This study proposes a physics-informed ML model that combines physical constraints derived from the modified Nusselt model with conventional data-driven ML techniques. The model's predictive performance is evaluated using a comprehensive database (879 datapoints from 13 studies). A physics-constrained and eight data-driven ML methods are assessed. The results reveal that the physics-constrained approach combined with XGBoost significantly outperforms conventional ML methods on extrapolation datasets (199 datapoints from 3 studies), achieving a mean absolute percentage error of 11.22 %, which is approximately half that of the best-performing fully data-driven model at 21.63 %. The model demonstrates consistent and reliable performance across diverse datasets, making it an effective tool for predicting heat transfer coefficients in steam–non-condensable gas mixtures. By deepening the understanding of the underlying physical processes, the proposed model supports the development of precise and efficient engineering solutions for condensation heat transfer.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100482"},"PeriodicalIF":9.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437004","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}

{"title":"Artificial Neural Network (ANN) driven Techno-Economic Predictions for Micro Gas Turbines (MGT) based Energy Applications","authors":"A.H.Samitha Weerakoon,&nbsp;Mohsen Assadi","doi":"10.1016/j.egyai.2025.100483","DOIUrl":"10.1016/j.egyai.2025.100483","url":null,"abstract":"<div><div>This paper introduces a novel Artificial Neural Network (ANN)-driven methodology for the techno-economic assessment (TEA) of Micro Gas Turbines (MGT) in energy applications, addressing the limitations of traditional TEA approaches which often lack adaptability to dynamic market conditions and technological advancements. The developed ANN model, employing a multi-layer perceptron architecture, leverages advanced machine learning techniques to accurately predict key economic indicators such as Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Return on Investment (ROI). Analysis of over 450 MGT-related energy project profiles validates the model's efficacy, demonstrating high predictive accuracy with a Mean Squared Error (MSE) of 0.0005 and an R-squared value of 0.993. The model is further validated across key application areas for MGT's, including PV and Solar, Distributed Energy Generation (DEG) and Hydrogen-Natural Gas blended systems for microgrid applications, showcasing its potential to enhance decision-making for energy investments. This approach not only streamlines the economic assessment process, reducing time and effort significantly, but also enhances decision-making for stakeholders by providing rapid, real-time economic analyses. The integration of ANN into MGT TEA sets a new standard for conducting techno-economic evaluations, potentially transforming energy system optimization practices.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100483"},"PeriodicalIF":9.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420490","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}