{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T11:27:34Z","timestamp":1783423654538,"version":"3.54.6"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The Natural Science Foundation of China","doi-asserted-by":"crossref","award":["71671029"],"award-info":[{"award-number":["71671029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Scientific Research Project of Shaanxi Education Department","award":["21JK0550"],"award-info":[{"award-number":["21JK0550"]}]},{"name":"The Major Key Project of PCL","award":["PCL2021A12"],"award-info":[{"award-number":["PCL2021A12"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10489-023-04599-0","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T16:01:50Z","timestamp":1686153710000},"page":"21606-21640","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Short-term load forecasting system based on sliding fuzzy granulation and equilibrium optimizer"],"prefix":"10.1007","volume":"53","author":[{"given":"Shoujiang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhou","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"4599_CR1","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.procs.2015.07.041","volume":"55","author":"GRT Esteves","year":"2015","unstructured":"Esteves GRT, Bastos BQ, Cyrino FL, Calili RF, Souza RC (2015) Long term electricity forecast: a systematic review. Procedia Computer Science 55:549\u2013558. https:\/\/doi.org\/10.1016\/j.procs.2015.07.041","journal-title":"Procedia Computer Science"},{"key":"4599_CR2","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.apenergy.2021.117911","volume":"305","author":"J Wang","year":"2022","unstructured":"Wang J, Zhang L, Li Z (2022) Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm. Appl Energy 305:117\u2013911. https:\/\/doi.org\/10.1016\/j.apenergy.2021.117911","journal-title":"Appl Energy"},{"key":"4599_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.106029","volume":"88","author":"W Yang","year":"2020","unstructured":"Yang W, Wang J, Niu T, Pei D (2020) A novel system for multi-step electricity price forecasting for electricity market management. Appl Soft Comput 88:106029. https:\/\/doi.org\/10.1016\/j.asoc.2019.106029","journal-title":"Appl Soft Comput"},{"key":"4599_CR4","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.energy.2021.121275","volume":"234","author":"S Wang","year":"2021","unstructured":"Wang S, Wang J, Haiyan L, Zhao W (2021) A novel combined model for wind speed prediction-combination of linear model, shallow neural networks, and deep learning approaches. Energy 234:121\u2013275. https:\/\/doi.org\/10.1016\/j.energy.2021.121275","journal-title":"Energy"},{"key":"4599_CR5","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.knosys.2021.107297","volume":"228","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Hong W-C (2021) Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl-Based Syst 228:107\u2013297. https:\/\/doi.org\/10.1016\/j.knosys.2021.107297","journal-title":"Knowl-Based Syst"},{"key":"4599_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.asoc.2018.07.022","volume":"72","author":"J Wang","year":"2018","unstructured":"Wang J, Pei D, Haiyan L, Yang W, Niu T (2018) An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting. Appl Soft Comput 72:321\u2013337. https:\/\/doi.org\/10.1016\/j.asoc.2018.07.022","journal-title":"Appl Soft Comput"},{"key":"4599_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2021.101940","volume":"314","author":"K Wang","year":"2022","unstructured":"Wang K, Wang J, Zeng B, Haiyan L (2022) An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization. Appl Energy 314:118938. https:\/\/doi.org\/10.1016\/j.seta.2021.101940","journal-title":"Appl Energy"},{"issue":"6","key":"4599_CR8","doi-asserted-by":"publisher","first-page":"1811","DOI":"10.1007\/s00521-016-2799-1","volume":"30","author":"H Zhao","year":"2018","unstructured":"Zhao H, Han X, Guo S (2018) DGM (1, 1) model optimized by mvo (multi-verse optimizer) for annual peak load forecasting. Neural Comput Appl 30(6):1811\u20131825. https:\/\/doi.org\/10.1007\/s00521-016-2799-1","journal-title":"Neural Comput Appl"},{"issue":"3","key":"4599_CR9","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.epsr.2009.09.006","volume":"80","author":"SS Pappas","year":"2010","unstructured":"Pappas SS, Ekonomou L, Karampelas P, Karamousantas DC, Katsikas SK, Chatzarakis GE, Skafidas PD (2010) Electricity demand load forecasting of the hellenic power system using an ARMA model. Electric Power Systems Research 80(3):256\u2013264. https:\/\/doi.org\/10.1016\/j.epsr.2009.09.006","journal-title":"Electric Power Systems Research"},{"key":"4599_CR10","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/7414318","author":"B Wang","year":"2019","unstructured":"Wang B, Zhang L, Ma H, Wang H, Wan S (2019) Parallel LSTM-based regional integrated energy system multienergy source-load information interactive energy prediction. Complexity. https:\/\/doi.org\/10.1155\/2019\/7414318","journal-title":"Complexity"},{"key":"4599_CR11","doi-asserted-by":"publisher","unstructured":"Kai Wang, Shuai\u00a0Bin Huang, and Yu\u00a0Le Ding. Application of GRNN neural network in short term load forecasting. In Advanced Materials Research, volume 971, pages 2242\u20132247. Trans Tech Publ, 2014. https:\/\/doi.org\/10.4028\/www.scientific.net\/AMR.971-973.2242","DOI":"10.4028\/www.scientific.net\/AMR.971-973.2242"},{"key":"4599_CR12","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.chaos.2022.111982","volume":"157","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Wang J, Haiyan L, Zhao W (2022) Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition. Chaos, Solitons & Fractals 157:111\u2013982. https:\/\/doi.org\/10.1016\/j.chaos.2022.111982","journal-title":"Chaos, Solitons & Fractals"},{"key":"4599_CR13","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1016\/j.apenergy.2018.11.034","volume":"235","author":"W Yang","year":"2019","unstructured":"Yang W, Wang J, Niu T, Pei D (2019) A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting. Appl Energy 235:1205\u20131225. https:\/\/doi.org\/10.1016\/j.apenergy.2018.11.034","journal-title":"Appl Energy"},{"issue":"16","key":"4599_CR14","doi-asserted-by":"publisher","first-page":"3388","DOI":"10.1049\/joe.2018.8389","volume":"2019","author":"X Xiao","year":"2019","unstructured":"Xiao X, Xie W, Zhou Y, Zhao W, Liu X, Zhang C (2019) Prediction and analysis of energy demand of high energy density AC\/DC park based on spatial static load forecasting method. The Journal of Engineering 2019(16):3388\u20133391. https:\/\/doi.org\/10.1049\/joe.2018.8389","journal-title":"The Journal of Engineering"},{"key":"4599_CR15","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.rcim.2014.12.015","volume":"34","author":"P Ramos","year":"2015","unstructured":"Ramos P, Santos N, Rebelo R (2015) Performance of state space and ARIMA models for consumer retail sales forecasting. Robotics and Computer-integrated Manufacturing 34:151\u2013163. https:\/\/doi.org\/10.1016\/j.rcim.2014.12.015","journal-title":"Robotics and Computer-integrated Manufacturing"},{"key":"4599_CR16","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/j.csda.2013.10.014","volume":"76","author":"H Nyberg","year":"2014","unstructured":"Nyberg H, Saikkonen P (2014) Forecasting with a noncausal var model. Computational Statistics & Data Analysis 76:536\u2013555. https:\/\/doi.org\/10.1016\/j.csda.2013.10.014","journal-title":"Computational Statistics & Data Analysis"},{"key":"4599_CR17","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.energy.2016.03.070","volume":"104","author":"H Takeda","year":"2016","unstructured":"Takeda H, Tamura Y, Sato S (2016) Using the ensemble Kalman filter for electricity load forecasting and analysis. Energy 104:184\u2013198. https:\/\/doi.org\/10.1016\/j.energy.2016.03.070","journal-title":"Energy"},{"issue":"2","key":"4599_CR18","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1109\/TPWRS.2005.846044","volume":"20","author":"RC Garcia","year":"2005","unstructured":"Garcia RC, Contreras J, Van Akkeren M, Garcia JBC (2005) A Garch forecasting model to predict day-ahead electricity prices. IEEE Trans Power Syst 20(2):867\u2013874. https:\/\/doi.org\/10.1109\/TPWRS.2005.846044","journal-title":"IEEE Trans Power Syst"},{"key":"4599_CR19","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.enpol.2012.05.026","volume":"48","author":"Y Wang","year":"2012","unstructured":"Wang Y, Wang J, Zhao G, Dong Y (2012) Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: a case study of China. Energy Policy 48:284\u2013294. https:\/\/doi.org\/10.1016\/j.enpol.2012.05.026","journal-title":"Energy Policy"},{"issue":"3","key":"4599_CR20","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1016\/j.ejor.2018.12.013","volume":"275","author":"JF Rendon-Sanchez","year":"2019","unstructured":"Rendon-Sanchez JF, de Menezes LM (2019) Structural combination of seasonal exponential smoothing forecasts applied to load forecasting. Eur J Oper Res 275(3):916\u2013924. https:\/\/doi.org\/10.1016\/j.ejor.2018.12.013","journal-title":"Eur J Oper Res"},{"key":"4599_CR21","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.ijepes.2019.02.022","volume":"109","author":"S Wang","year":"2019","unstructured":"Wang S, Wang X, Wang S, Wang D (2019) Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting. International Journal of Electrical Power & Energy Systems 109:470\u2013479. https:\/\/doi.org\/10.1016\/j.ijepes.2019.02.022","journal-title":"International Journal of Electrical Power & Energy Systems"},{"issue":"3","key":"4599_CR22","doi-asserted-by":"publisher","first-page":"1984","DOI":"10.1109\/TPWRS.2020.3028133","volume":"36","author":"Y Wang","year":"2020","unstructured":"Wang Y, Chen J, Chen X, Zeng X, Kong Y, Sun S, Guo Y, Liu Y (2020) Short-term load forecasting for industrial customers based on TCN-LIGHTGBM. IEEE Trans Power Syst 36(3):1984\u20131997. https:\/\/doi.org\/10.1109\/TPWRS.2020.3028133","journal-title":"IEEE Trans Power Syst"},{"key":"4599_CR23","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.epsr.2017.01.035","volume":"146","author":"X Zhang","year":"2017","unstructured":"Zhang X, Wang J, Zhang K (2017) Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by cuckoo search algorithm. Electric Power Systems Research 146:270\u2013285","journal-title":"Electric Power Systems Research"},{"key":"4599_CR24","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.apenergy.2022.118796","volume":"313","author":"J Wang","year":"2022","unstructured":"Wang J, Wang S, Zeng B, Haiyan L (2022) A novel ensemble probabilistic forecasting system for uncertainty in wind speed. Appl Energy 313:118\u2013796","journal-title":"Appl Energy"},{"issue":"4","key":"4599_CR25","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intelligent Systems and Their Applications 13(4):18\u201328. https:\/\/doi.org\/10.1109\/5254.708428","journal-title":"IEEE Intelligent Systems and Their Applications"},{"key":"4599_CR26","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361. https:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Adv Eng Softw"},{"key":"4599_CR27","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.scs.2020.102311","volume":"61","author":"M Barman","year":"2020","unstructured":"Barman M, Choudhury NBD (2020) A similarity based hybrid GWO-SVM method of power system load forecasting for regional special event days in anomalous load situations in Assam. India. Sustainable Cities and Society 61:102\u2013311. https:\/\/doi.org\/10.1016\/j.scs.2020.102311","journal-title":"India. Sustainable Cities and Society"},{"key":"4599_CR28","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/j.energy.2018.09.027","volume":"164","author":"W-Q Li","year":"2018","unstructured":"Li W-Q, Chang L (2018) A combination model with variable weight optimization for short-term electrical load forecasting. Energy 164:575\u2013593. https:\/\/doi.org\/10.1016\/j.energy.2018.09.027","journal-title":"Energy"},{"key":"4599_CR29","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.apenergy.2019.01.055","volume":"237","author":"F He","year":"2019","unstructured":"He F, Zhou J, Feng Z, Liu G, Yang Y (2019) A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm. Appl Energy 237:103\u2013116. https:\/\/doi.org\/10.1016\/j.apenergy.2019.01.055","journal-title":"Appl Energy"},{"key":"4599_CR30","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.asoc.2019.105548","volume":"82","author":"R Wang","year":"2019","unstructured":"Wang R, Wang J, Yunzhen X (2019) A novel combined model based on hybrid optimization algorithm for electrical load forecasting. Appl Soft Comput 82:105\u2013548. https:\/\/doi.org\/10.1016\/j.asoc.2019.105548","journal-title":"Appl Soft Comput"},{"key":"4599_CR31","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.asoc.2018.01.017","volume":"65","author":"X Zhang","year":"2018","unstructured":"Zhang X, Wang J (2018) A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns. Appl Soft Comput 65:478\u2013494. https:\/\/doi.org\/10.1016\/j.asoc.2018.01.017","journal-title":"Appl Soft Comput"},{"key":"4599_CR32","unstructured":"Shaojie Bai, J\u00a0Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018. https:\/\/arxiv.org\/pdf\/1803.01271"},{"key":"4599_CR33","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014. https:\/\/arxiv.org\/abs\/1412.3555"},{"issue":"6","key":"4599_CR34","doi-asserted-by":"publisher","first-page":"6117","DOI":"10.1109\/TIA.2020.2992945","volume":"56","author":"Yu Yixiao","year":"2020","unstructured":"Yixiao Yu, Han X, Yang M, Yang J (2020) Probabilistic prediction of regional wind power based on spatiotemporal quantile regression. IEEE Trans Ind Appl 56(6):6117\u20136127. https:\/\/doi.org\/10.1109\/TIA.2020.2992945","journal-title":"IEEE Trans Ind Appl"},{"key":"4599_CR35","unstructured":"Boris\u00a0N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. N-beats: neural basis expansion analysis for interpretable time series forecasting. In International Conference on Learning Representations, 2020. https:\/\/openreview.net\/forum?id=r1ecqn4YwBhttps:\/\/openreview.net\/forum?id=r1ecqn4YwB"},{"key":"4599_CR36","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/j.apenergy.2019.05.102","volume":"250","author":"Z Zheng","year":"2019","unstructured":"Zheng Z, Chen H, Luo X (2019) A Kalman filter-based bottom-up approach for household short-term load forecast. Appl Energy 250:882\u2013894. https:\/\/doi.org\/10.1016\/j.apenergy.2019.05.102","journal-title":"Appl Energy"},{"key":"4599_CR37","doi-asserted-by":"crossref","unstructured":"Li\u00a0Xin Dai and Fang\u00a0Fang Hu. Application optimization of grey model in power load forecasting. In Advanced Materials Research, volume 347, pages 301\u2013305. Trans Tech Publ, 2012. https:\/\/doi.org\/10.4028\/www.scientific.net\/AMR.347-353.301","DOI":"10.4028\/www.scientific.net\/AMR.347-353.301"},{"key":"4599_CR38","doi-asserted-by":"publisher","unstructured":"Hou Bin, Yun\u00a0Xiao Zu, and Chao Zhang. A forecasting method of short-term electric power load based on BP neural network. In Applied Mechanics and Materials, volume 538, pages 247\u2013250. Trans Tech Publ, 2014. https:\/\/doi.org\/10.4028\/www.scientific.net\/AMM.538.247","DOI":"10.4028\/www.scientific.net\/AMM.538.247"},{"key":"4599_CR39","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.asoc.2019.03.035","volume":"80","author":"D Pei","year":"2019","unstructured":"Pei D, Wang J, Yang W, Niu T (2019) A novel hybrid model for short-term wind power forecasting. Appl Soft Comput 80:93\u2013106. https:\/\/doi.org\/10.1016\/j.asoc.2019.03.035","journal-title":"Appl Soft Comput"},{"key":"4599_CR40","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.apenergy.2018.02.070","volume":"215","author":"J Song","year":"2018","unstructured":"Song J, Wang J, Haiyan L (2018) A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting. Appl Energy 215:643\u2013658. https:\/\/doi.org\/10.1016\/j.apenergy.2018.02.070","journal-title":"Appl Energy"},{"key":"4599_CR41","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.seta.2021.101346","volume":"47","author":"L Zhang","year":"2021","unstructured":"Zhang L, Wang J, Niu X (2021) Wind speed prediction system based on data pre-processing strategy and multi-objective dragonfly optimization algorithm. Sustainable Energy Technol Assess 47:101\u2013346. https:\/\/doi.org\/10.1016\/j.seta.2021.101346","journal-title":"Sustainable Energy Technol Assess"},{"key":"4599_CR42","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.energy.2015.08.045","volume":"93","author":"J Wang","year":"2015","unstructured":"Wang J, Jianming H (2015) A robust combination approach for short-term wind speed forecasting and analysis-combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and lssvm (least square SVM) forecasts using a GPR (Gaussian Process Regression) model. Energy 93:41\u201356. https:\/\/doi.org\/10.1016\/j.energy.2015.08.045","journal-title":"Energy"},{"key":"4599_CR43","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1016\/j.renene.2022.10.123","volume":"201","author":"J Li","year":"2022","unstructured":"Li J, Wang J, Zhang H, Li Z (2022) An innovative combined model based on multi-objective optimization approach for forecasting short-term wind speed: a case study in China. Renewable Energy 201:766\u2013779. https:\/\/doi.org\/10.1016\/j.renene.2022.10.123","journal-title":"Renewable Energy"},{"key":"4599_CR44","unstructured":"Lotfi\u00a0A Zadeh. Fuzzy sets and information granularity. Advances in fuzzy set theory and applications, 11:3\u201318, 1979"},{"key":"4599_CR45","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1016\/j.asoc.2018.09.032","volume":"73","author":"L Duan","year":"2018","unstructured":"Duan L, Fusheng Yu, Pedrycz W, Wang X, Yang X (2018) Time-series clustering based on linear fuzzy information granules. Appl Soft Comput 73:1053\u20131067. https:\/\/doi.org\/10.1016\/j.asoc.2018.09.032","journal-title":"Appl Soft Comput"},{"key":"4599_CR46","doi-asserted-by":"publisher","unstructured":"Anwen Zhu, Xiaohui Li, Zhiyong Mo, and Ruaren Wu. Wind power prediction based on a convolutional neural network. In 2017 International Conference on Circuits, Devices and Systems (ICCDS), pages 131\u2013135. IEEE, 2017. https:\/\/doi.org\/10.1109\/ICCDS.2017.8120465","DOI":"10.1109\/ICCDS.2017.8120465"},{"key":"4599_CR47","doi-asserted-by":"publisher","unstructured":"Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst 191:105190. https:\/\/doi.org\/10.1016\/j.knosys.2019.105190","DOI":"10.1016\/j.knosys.2019.105190"},{"issue":"3","key":"4599_CR48","doi-asserted-by":"publisher","first-page":"590","DOI":"10.3390\/econometrics3030590","volume":"3","author":"Hossein Hassani and Emmanuel Sirimal Silva","year":"2015","unstructured":"Hossein Hassani and Emmanuel Sirimal Silva (2015) A Kolmogorov-Smirnov based test for comparing the predictive accuracy of two sets of forecasts. Econometrics 3(3):590\u2013609. https:\/\/doi.org\/10.3390\/econometrics3030590","journal-title":"Econometrics"},{"key":"4599_CR49","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.ijepes.2022.108073","volume":"139","author":"G-F Fan","year":"2022","unstructured":"Fan G-F, Zhang L-Z, Meng Yu, Hong W-C, Dong S-Q (2022) Applications of random forest in multivariable response surface for short-term load forecasting. International Journal of Electrical Power & Energy Systems 139:10\u20138073. https:\/\/doi.org\/10.1016\/j.ijepes.2022.108073","journal-title":"International Journal of Electrical Power & Energy Systems"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04599-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04599-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04599-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:45:15Z","timestamp":1744159515000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04599-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,7]]},"references-count":49,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["4599"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04599-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,7]]},"assertion":[{"value":"29 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}