{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:05:24Z","timestamp":1774022724525,"version":"3.50.1"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["9244024"],"award-info":[{"award-number":["9244024"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72404032"],"award-info":[{"award-number":["72404032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["9264024"],"award-info":[{"award-number":["9264024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Industrial Engineering"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.cie.2026.111875","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:30:52Z","timestamp":1770078652000},"page":"111875","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A data-driven multi-agent simulation framework for improved electricity spot price forecasting in medium and short terms"],"prefix":"10.1016","volume":"214","author":[{"given":"Yulin","family":"Jin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4184-0909","authenticated-orcid":false,"given":"Yu","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4796-1138","authenticated-orcid":false,"given":"Jing","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Xiaofeng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiaotong","family":"Qie","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"05","key":"10.1016\/j.cie.2026.111875_b0005","first-page":"1663","article-title":"Study on low carbon energy transition path toward carbon peak and carbon neutrality","volume":"43","author":"Shu","year":"2023","journal-title":"Proceedings of the CSEE"},{"key":"10.1016\/j.cie.2026.111875_b0010","first-page":"1","article-title":"International practices of the basic support mechanisms of electricity spot market","author":"Zhu","year":"2025","journal-title":"Southern Power System Technology"},{"key":"10.1016\/j.cie.2026.111875_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.enpol.2023.113722","article-title":"The effect of electricity market reform on energy efficiency in China","volume":"181","author":"Wang","year":"2023","journal-title":"Energy Policy"},{"issue":"4","key":"10.1016\/j.cie.2026.111875_b0020","doi-asserted-by":"crossref","first-page":"2822","DOI":"10.1109\/TSG.2022.3166791","article-title":"Electricity price prediction for energy storage system arbitrage: A decision-focused approach","volume":"13","author":"Sang","year":"2022","journal-title":"IEEE Transactions on Smart Grid"},{"key":"10.1016\/j.cie.2026.111875_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.123863","article-title":"A hybrid framework for day-ahead electricity spot-price forecasting: A case study in China","volume":"373","author":"Huang","year":"2024","journal-title":"Applied Energy"},{"key":"10.1016\/j.cie.2026.111875_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.124212","article-title":"Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization","volume":"254","author":"Meng","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.cie.2026.111875_b0035","series-title":"Proceedings of the CSEE","first-page":"1","article-title":"Day-ahead electricity price forecasting method driven by net spot bidding space considering medium and long-term market factors","author":"Fang","year":"2025"},{"issue":"02","key":"10.1016\/j.cie.2026.111875_b0040","first-page":"160","article-title":"Review of spot electricity price prediction studies based on machine learning methods","volume":"46","author":"Jia","year":"2025","journal-title":"Electric Power Construction"},{"issue":"6","key":"10.1016\/j.cie.2026.111875_b0045","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.3390\/en14061596","article-title":"Deep-learning forecasting method for electric power load via attention-based encoder-decoder with bayesian optimization","volume":"14","author":"Jin","year":"2021","journal-title":"Energies"},{"issue":"07","key":"10.1016\/j.cie.2026.111875_b0050","first-page":"22","article-title":"Forecasting model of day-ahead market electricity price considering 2D variation of time series","volume":"36","author":"Chen","year":"2024","journal-title":"Proceedings of the CSU-EPSA"},{"issue":"9","key":"10.1016\/j.cie.2026.111875_b0055","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.3390\/en15093423","article-title":"Short-term electricity prices forecasting using functional time series analysis","volume":"15","author":"Jan","year":"2022","journal-title":"Energies"},{"issue":"4","key":"10.1016\/j.cie.2026.111875_b0060","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.3390\/en16041570","article-title":"A Hawkes model approach to modeling price spikes in the Japanese electricity market","volume":"16","author":"Adline","year":"2023","journal-title":"Energies"},{"issue":"1","key":"10.1016\/j.cie.2026.111875_b0065","doi-asserted-by":"crossref","first-page":"65","DOI":"10.15353\/rea.v13i1.1822","article-title":"Forecasting price spikes in electricity markets","volume":"13","author":"Stathakis","year":"2021","journal-title":"Review of Economic Analysis"},{"issue":"5","key":"10.1016\/j.cie.2026.111875_b0070","doi-asserted-by":"crossref","first-page":"4739","DOI":"10.1109\/TPWRS.2022.3218712","article-title":"Short-term electricity price forecast using frequency analysis and price spikes oversampling","volume":"38","author":"Zhang","year":"2022","journal-title":"IEEE Transactions on Power Systems"},{"issue":"6","key":"10.1016\/j.cie.2026.111875_b0075","doi-asserted-by":"crossref","first-page":"5726","DOI":"10.1109\/TIA.2021.3114129","article-title":"Forecasting electricity price in different time horizons: An application to the Italian electricity market","volume":"57","author":"Imani","year":"2021","journal-title":"IEEE Transactions on Industry Applications"},{"issue":"22","key":"10.1016\/j.cie.2026.111875_b0080","doi-asserted-by":"crossref","first-page":"7333","DOI":"10.3390\/en14217333","article-title":"A multiscale electricity price forecasting model based on tensor fusion and deep learning","volume":"14","author":"Xie","year":"2021","journal-title":"Energies"},{"key":"10.1016\/j.cie.2026.111875_b0085","doi-asserted-by":"crossref","unstructured":"Syah, R., Afshin, D., Elveny, M., et al. (2021). Forecasting daily electricity price by hybrid model of fractional wavelet transform, feature selection, support vector machine and optimization algorithm.Electronics, 10, 2214.","DOI":"10.3390\/electronics10182214"},{"issue":"02","key":"10.1016\/j.cie.2026.111875_b0090","first-page":"35","article-title":"A short-term electricity price forecasting method based on improved VMD-PSO-CNN-LSTM","volume":"39","author":"Guo","year":"2024","journal-title":"Journal of Electric Power Science and Technology"},{"issue":"17","key":"10.1016\/j.cie.2026.111875_b0095","first-page":"125","article-title":"CNN-LSTM short-term electricity price prediction based on an attention mechanism","volume":"50","author":"Ji","year":"2022","journal-title":"Power System Protection and Control"},{"issue":"10","key":"10.1016\/j.cie.2026.111875_b0100","doi-asserted-by":"crossref","DOI":"10.1007\/s11227-021-03686-8","article-title":"Mid-term electricity load prediction using CNN and Bi-LSTM","volume":"77","author":"Gul","year":"2021","journal-title":"Journal of Supercomputing"},{"issue":"04","key":"10.1016\/j.cie.2026.111875_b0105","first-page":"1325","article-title":"Power load forecasting of SVM based on real-time price and weighted grey relational projection algorithm","volume":"44","author":"Zhao","year":"2020","journal-title":"Power System Technology"},{"issue":"4","key":"10.1016\/j.cie.2026.111875_b0110","first-page":"1576","article-title":"Adaptive temporal-frequency network for time-series forecasting","volume":"34","author":"Yang","year":"2020","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.cie.2026.111875_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.126099","article-title":"A hybrid day-ahead electricity price forecasting framework based on time series","volume":"264","author":"Xiong","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.cie.2026.111875_b0120","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.energy.2025.138799","article-title":"A Transformer-based model fusing temporal dependence and variable correlation for short and medium-term electricity price forecasting","volume":"338","author":"Li","year":"2025","journal-title":"Energy"},{"issue":"08","key":"10.1016\/j.cie.2026.111875_b0125","first-page":"3418","article-title":"Medium- and long-term load forecasting Model based on double decomposition and BiLSTM","volume":"48","author":"Wang","year":"2024","journal-title":"Power System Technology"},{"key":"10.1016\/j.cie.2026.111875_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2019.07.134","article-title":"Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform","volume":"187","author":"Chang","year":"2019","journal-title":"Energy"},{"issue":"04","key":"10.1016\/j.cie.2026.111875_b0135","article-title":"Forecasting method of clearing price in spot market by random forest regression","volume":"41","author":"Wei","year":"2021","journal-title":"Proceedings of the CSEE"},{"issue":"09","key":"10.1016\/j.cie.2026.111875_b0140","first-page":"327","article-title":"Random forest model with gradient boosting and its application to forecasting on day-ahead market clearing price","volume":"35","author":"Xie","year":"2018","journal-title":"Computer Applications and Software"},{"issue":"11","key":"10.1016\/j.cie.2026.111875_b0145","first-page":"9","article-title":"Short-term electricity load forecasting based on support vector regression forecasting model considering weather factors and time-of-use tariff factors","volume":"39","author":"Wei","year":"2023","journal-title":"Power System and Clean Energy"},{"issue":"07","key":"10.1016\/j.cie.2026.111875_b0150","article-title":"Research on long-term load forecasting of the power system based on multi-factor combination analysis","volume":"40","author":"Li","year":"2024","journal-title":"Power System and Clean Energy"},{"key":"10.1016\/j.cie.2026.111875_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.116688","article-title":"Advanced price forecasting in agent-based electricity market simulation","volume":"290","author":"Fraunholz","year":"2021","journal-title":"Applied Energy"},{"key":"10.1016\/j.cie.2026.111875_b0160","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.eneco.2014.08.007","article-title":"Can agent-based models forecast spot prices in electricity markets? Evidence from the New Zealand electricity market","volume":"45","author":"Young","year":"2014","journal-title":"Energy Economics"},{"key":"10.1016\/j.cie.2026.111875_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2024.114897","article-title":"Agent-based modeling framework for predicting regional electricity consumption considering occupant behavior shift and exogenous policy impact","volume":"324","author":"Chen","year":"2024","journal-title":"Energy and Buildings"},{"key":"10.1016\/j.cie.2026.111875_b0170","doi-asserted-by":"crossref","first-page":"89249","DOI":"10.1109\/ACCESS.2023.3306409","article-title":"Two-level optimal bidding strategy for load aggregator based on a data-driven approach combined with LSTM-based forecasting and agent-based models","volume":"11","author":"Ryu","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.cie.2026.111875_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.124657","article-title":"Strategic bidding in a competitive electricity market: An intelligent method using multi-agent transfer Learning based on reinforcement learning","volume":"256","author":"Wu","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.cie.2026.111875_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2020.110405","article-title":"Review and analysis of investment decision making algorithms in long-term agent-based electric power system simulation models","volume":"136","author":"Tao","year":"2021","journal-title":"Renewable and Sustainable Energy Reviews"},{"issue":"10","key":"10.1016\/j.cie.2026.111875_b0185","first-page":"44","article-title":"Analysis of interactions between carbon emission trading market and medium-and long-term electricity market","volume":"32","author":"Wang","year":"2020","journal-title":"Proceedings of the CSU-EPSA"},{"issue":"1","key":"10.1016\/j.cie.2026.111875_b0190","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/8007634","article-title":"Simulation of the bidding behavior of electricity retailers based on the Q\u2010learning algorithm","volume":"2022","author":"Xie","year":"2022","journal-title":"Wireless Communications and Mobile Computing"},{"issue":"6","key":"10.1016\/j.cie.2026.111875_b0195","doi-asserted-by":"crossref","first-page":"4180","DOI":"10.1109\/TPWRS.2020.2999536","article-title":"Agent-based modeling in electricity market using deep deterministic policy gradient algorithm","volume":"35","author":"Liang","year":"2020","journal-title":"IEEE Transactions on Power Systems"},{"key":"10.1016\/j.cie.2026.111875_b0200","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s10462-021-09996-w","article-title":"Multi-agent deep reinforcement learning: A survey","volume":"55","author":"Gronauer","year":"2022","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.cie.2026.111875_b0205","doi-asserted-by":"crossref","first-page":"177734","DOI":"10.1109\/ACCESS.2020.3020590","article-title":"An improved DDPG and its application based on the double-layer BP neural network","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.cie.2026.111875_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2024.111119","article-title":"Short-term load forecasting by GRU neural network and DDPG algorithm for adaptive optimization of hyperparameters","volume":"238","author":"He","year":"2025","journal-title":"Electric Power Systems Research"},{"key":"10.1016\/j.cie.2026.111875_b0215","doi-asserted-by":"crossref","unstructured":"Jan, F., Iftikhar, H., & Tahir, M., et al. (2025). Forecasting day-ahead electric power prices with functional data analysis[J]. Frontiers in Energy Research, 13: 1477248.","DOI":"10.3389\/fenrg.2025.1477248"}],"container-title":["Computers & Industrial Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360835226000768?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360835226000768?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:13:53Z","timestamp":1774019633000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0360835226000768"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":43,"alternative-id":["S0360835226000768"],"URL":"https:\/\/doi.org\/10.1016\/j.cie.2026.111875","relation":{},"ISSN":["0360-8352"],"issn-type":[{"value":"0360-8352","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A data-driven multi-agent simulation framework for improved electricity spot price forecasting in medium and short terms","name":"articletitle","label":"Article Title"},{"value":"Computers & Industrial Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cie.2026.111875","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111875"}}