{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:19:18Z","timestamp":1774120758093,"version":"3.50.1"},"reference-count":25,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>With the promotion of power market reform, accurate short-term load forecasting is of great significance for power systems to formulate reasonable production plans and ensure safe operation of the power grid. At present, the power grids have brought great challenges to load forecasting, and due to the need of production, a large number of nonlinear, asymmetric, and impact loads are connected to the power grid, so it is difficult for traditional power load forecasting methods to fully and accurately characterize the load characteristics. In order to learn the complex hidden deep relationship in nonlinear load data and improve the prediction accuracy, this paper proposed a method based on conditional generative adversarial networks (CGANs). This method used a convolutional neural network to construct a generative model and a discriminant model, took the load influencing factor as a condition. Through the game training of the confrontation network, the generation model can learn the mapping relationship between the noise and the real load data, to perform short-term load forecasting. The validation was conducted using a dataset from a certain power plant, and the experimental results showed that the trained CGAN has strong ability to learn load temporal features and has high prediction accuracy in different scenarios. The subsequent work will analyze the characteristics of different types of load data and consider the impact of real-time electricity prices and other factors on load prediction, so as to further improve the prediction accuracy and universality of the model.<\/jats:p>","DOI":"10.1177\/14727978241310754","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:16:21Z","timestamp":1740053781000},"page":"2185-2195","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Short-term load forecasting based on conditional generating adversarial networks"],"prefix":"10.1177","volume":"25","author":[{"given":"Douxing","family":"Chang","sequence":"first","affiliation":[{"name":"Ministry of Emergency Management"}]}],"member":"179","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"issue":"4","key":"e_1_3_2_2_2","first-page":"19","article-title":"Application of fuzzy linear regression to load forecasting","volume":"26","author":"Geng G","year":"2002","unstructured":"Geng G, Guo X. Application of fuzzy linear regression to load forecasting. Power Syst Technol 2002; 26(4): 19\u201321.","journal-title":"Power Syst Technol"},{"issue":"7","key":"e_1_3_2_3_2","first-page":"2240","article-title":"Short-term load forecasting model based on online sequential extreme support vector regression","volume":"42","author":"Jiang M","year":"2018","unstructured":"Jiang M, Gu D, Kong J, et al. Short-term load forecasting model based on online sequential extreme support vector regression. Power Syst Technol 2018; 42(7): 2240\u20132247.","journal-title":"Power Syst Technol"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.2478\/v10198-012-0034-2"},{"issue":"20","key":"e_1_3_2_5_2","first-page":"26","article-title":"Bidding strategy for time-shiftable loads based on autoregressive integrated moving average model","volume":"41","author":"Xin A","year":"2017","unstructured":"Xin A, Zhou Z, Wei Y, et al. Bidding strategy for time-shiftable loads based on autoregressive integrated moving average model. Autom Electr Power Syst 2017; 41(20): 26\u201331.","journal-title":"Autom Electr Power Syst"},{"issue":"3","key":"e_1_3_2_6_2","first-page":"91","article-title":"ARIMA-GRU short-term power load forecasting based on feature mining","volume":"34","author":"Junqi Y","year":"2022","unstructured":"Junqi Y, Nie J, Zhao A, et al. ARIMA-GRU short-term power load forecasting based on feature mining. Proc CSU-EPSA 2022; 34(3): 91\u201399.","journal-title":"Proc CSU-EPSA"},{"issue":"13","key":"e_1_3_2_7_2","first-page":"8","article-title":"Based on Bayesian theory and online learning svm for short term load forecasting","volume":"25","author":"Dengfu Z","year":"2005","unstructured":"Dengfu Z, Wenchen P, Jiangshe Z, et al. Based on Bayesian theory and online learning svm for short term load forecasting. Proceedings of the CSEE 2005; 25(13): 8\u201313.","journal-title":"Proceedings of the CSEE"},{"issue":"15","key":"e_1_3_2_8_2","first-page":"67","article-title":"Short-term load forecasting support vector machine algorithm based on multi-source heterogeneous fusion of load factors","volume":"40","author":"Wu Q","year":"2016","unstructured":"Wu Q, Gao J, Hou G, et al. Short-term load forecasting support vector machine algorithm based on multi-source heterogeneous fusion of load factors. Autom Electr Power Syst 2016; 40(15): 67\u201372.","journal-title":"Autom Electr Power Syst"},{"issue":"34","key":"e_1_3_2_9_2","first-page":"93","article-title":"Least squares-support vector machine load forecasting approach optimized by bacterial colony chemotaxis method","volume":"31","author":"Zeng M","year":"2011","unstructured":"Zeng M, L\u00fc C, Tian K, et al. Least squares-support vector machine load forecasting approach optimized by bacterial colony chemotaxis method. Proceedings of the CSEE 2011; 31(34): 93\u201399.","journal-title":"Proceedings of the CSEE"},{"issue":"3","key":"e_1_3_2_10_2","first-page":"768","article-title":"Short-term power load probability density forecasting method based on real time price and support vector quantile regression","volume":"37","author":"He Y","year":"2017","unstructured":"He Y, Liu R, Han A. Short-term power load probability density forecasting method based on real time price and support vector quantile regression. Proceedings of the CSEE 2017; 37(3): 768\u2013776.","journal-title":"Proceedings of the CSEE"},{"issue":"3","key":"e_1_3_2_11_2","first-page":"59","article-title":"Short-term load forecasting based on recurrent neural network using ant colony optimization algorithm","volume":"29","author":"Zou Z","year":"2005","unstructured":"Zou Z, Sun Y, Zhang Z. Short-term load forecasting based on recurrent neural network using ant colony optimization algorithm. Power Syst Technol 2005; 29(3): 59\u201363.","journal-title":"Power Syst Technol"},{"issue":"2","key":"e_1_3_2_12_2","first-page":"437","article-title":"Ultra short-term power load forecasting based on randomly distributive embedded framework and BP neural network","volume":"44","author":"Guoqing L","year":"2020","unstructured":"Guoqing L, Liu Z, Jin G. Ultra short-term power load forecasting based on randomly distributive embedded framework and BP neural network. Power Syst Technol 2020; 44(2): 437\u2013445.","journal-title":"Power Syst Technol"},{"issue":"21","key":"e_1_3_2_13_2","first-page":"96","article-title":"High-performance back propagation neural network algorithm for classification of mass load data","volume":"42","author":"Yang L","year":"2018","unstructured":"Yang L, Yang L, Lixiong X. High-performance back propagation neural network algorithm for classification of mass load data. Autom Electr Power Syst 2018; 42(21): 96\u2013103.","journal-title":"Autom Electr Power Syst"},{"issue":"17","key":"e_1_3_2_14_2","first-page":"4966","article-title":"A multiple distributed BP neural networks approach for short-term load forecasting based on hadoop framework","volume":"37","author":"Xueneng S","year":"2017","unstructured":"Xueneng S, Liu T, Cao H. A multiple distributed BP neural networks approach for short-term load forecasting based on hadoop framework. Proceedings of the CSEE 2017; 37(17): 4966\u20134973.","journal-title":"Proceedings of the CSEE"},{"issue":"3","key":"e_1_3_2_15_2","first-page":"34","article-title":"A short-term load forecasting method based on recurrent and dilated mechanism of ConvGRU-transformer","volume":"49","author":"Baozhong T","year":"2022","unstructured":"Baozhong T, Gengyin L, Zhaoyuan W, et al. A short-term load forecasting method based on recurrent and dilated mechanism of ConvGRU-transformer. 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Autom Electr Power Syst 2019; 43(8): 131\u2013137.","journal-title":"Autom Electr Power Syst"},{"issue":"8","key":"e_1_3_2_18_2","first-page":"131","article-title":"Short-term load forecasting method based on CNN-LSTM hybrid neural network model","volume":"43","author":"Jixiang L","year":"2019","unstructured":"Jixiang L, Zhang Q, Yang Z, et al. Short-term load forecasting method based on CNN-LSTM hybrid neural network model. Autom Electr Power Syst 2019; 43(8): 131\u2013137.","journal-title":"Autom Electr Power Syst"},{"key":"e_1_3_2_19_2","unstructured":"Goodfellow I Pouget-Abadie J Mirza M et al. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems Montreal Canada 2014."},{"issue":"5","key":"e_1_3_2_20_2","first-page":"293","article-title":"Financial time serise prediction based on empirical mode decomposition to generate adversarial networks","volume":"37","author":"Wang J","year":"2020","unstructured":"Wang J, Zou H, Qu D, et al. Financial time serise prediction based on empirical mode decomposition to generate adversarial networks. Computer Applications and Software 2020; 37(5): 293\u2013297.","journal-title":"Computer Applications and Software"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.13334\/j.0258-8013.pcsee.191534"},{"issue":"18","key":"e_1_3_2_22_2","first-page":"46","article-title":"Reconstruction of missing measurement data in distribution network based on generative adversarial network and double semantic perception","volume":"44","author":"Yang Y","year":"2020","unstructured":"Yang Y, Qi L, Wang H, et al. Reconstruction of missing measurement data in distribution network based on generative adversarial network and double semantic perception. Autom Electr Power Syst 2020; 44(18): 46\u201354.","journal-title":"Autom Electr Power Syst"},{"issue":"2","key":"e_1_3_2_23_2","first-page":"45","article-title":"Prediction method for the behavior of substation staff based on generative adversarial network","volume":"13","author":"Wenqi H","year":"2019","unstructured":"Wenqi H, Xu A, Ming Z, et al. Prediction method for the behavior of substation staff based on generative adversarial network. Southern Power System Technology 2019; 13(2): 45\u201350.","journal-title":"Southern Power System Technology"},{"issue":"12","key":"e_1_3_2_24_2","first-page":"2113","article-title":"Technology of full crown restoration of defected teeth using conditional generative confrontation network","volume":"31","author":"Yuan F","year":"2019","unstructured":"Yuan F, Dai N, Tian S, et al. Technology of full crown restoration of defected teeth using conditional generative confrontation network. J Computer-Aided Des Comput Graph 2019; 31(12): 2113\u20132120.","journal-title":"J Computer-Aided Des Comput Graph"},{"issue":"1","key":"e_1_3_2_25_2","first-page":"17","article-title":"Ultra-short-term load forecasting method of LSTM neural network based on attention mechanism","volume":"36","author":"Zhaoyu L","year":"2019","unstructured":"Zhaoyu L, Ai Q, Zhang Y, et al. Ultra-short-term load forecasting method of LSTM neural network based on attention mechanism. Distrib Util 2019; 36(1): 17\u201322.","journal-title":"Distrib Util"},{"key":"e_1_3_2_26_2","unstructured":"Krizhevsky A Sutskever I Hinton G. ImageNet classification with deep convolutional neural networks. Nevada USA: ACM pp. 1097\u20131105. 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