{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T05:06:56Z","timestamp":1783746416441,"version":"3.55.0"},"reference-count":70,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2021 Wuxi Science and Technology Innovation and Entrepreneurship Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units. Thus, it is imperative to present new incentive methods to motivate such power system operations for electricity management. This paper proposes an approach for short-term electric load forecasting using long short-term memory networks and an improved sine cosine algorithm called MetaREC. First, using long short-term memory networks for a special kind of recurrent neural network, the dispatching commands have the characteristics of storing and transmitting both long-term and short-term memories. Next, four important parameters are determined using the sine cosine algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the inaccuracy of long short-term memory networks prediction, in terms of the manual selection of parameter values. Moreover, the performance of the MetaREC method outperforms others with regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our analysis is extended to the scenario of the MetaREC_long short-term memory with back propagation neural network, long short-term memory networks with default parameters, long short-term memory networks with the conventional sine-cosine algorithm, and long short-term memory networks with whale optimization for power load forecasting on a real electric load dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term memory can effectively incentivize the high accuracy and stability for short-term power load forecasting.<\/jats:p>","DOI":"10.3390\/s22207900","type":"journal-article","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:31:01Z","timestamp":1666053061000},"page":"7900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Lichao","family":"Sun","sequence":"first","affiliation":[{"name":"Computer School, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Qin","sequence":"additional","affiliation":[{"name":"Computer School, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4361-2763","authenticated-orcid":false,"given":"Krzysztof","family":"Przystupa","sequence":"additional","affiliation":[{"name":"Department of Automation, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7153-040X","authenticated-orcid":false,"given":"Michal","family":"Majka","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Electrotechnology, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3164-3821","authenticated-orcid":false,"given":"Orest","family":"Kochan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"},{"name":"Department of Measuring Information Technologies, Institute of Computer Technologies, Automation and Metrology, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1016\/j.ejor.2009.01.062","article-title":"Electric Load Forecasting Methods: Tools for Decision Making","volume":"199","author":"Hahn","year":"2009","journal-title":"Eur. 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