SHORT-TERM LOAD POWER FORECASTING USING NST-IRN COMBINED MODEL BASED ON TEMPORAL FEATURE ANALYSIS

Wan Weixing; Xie Lirong; Zhang Longjun; Bian Yifan; Ma Lan

doi:10.19912/j.0254-0096.tynxb.2024-1869

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (7) : 62-72. DOI: 10.19912/j.0254-0096.tynxb.2024-1869

Special Topics of Academic Papers at the 33th Annual Meeting of the China Association for Science and Technology

SHORT-TERM LOAD POWER FORECASTING USING NST-IRN COMBINED MODEL BASED ON TEMPORAL FEATURE ANALYSIS

Wan Weixing¹, Xie Lirong¹, Zhang Longjun², Bian Yifan¹, Ma Lan¹

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Abstract

In the process of short-term load power forecasting, in view of the large fluctuation of load power, which leads to the low prediction accuracy of a single model, a short-term load power NST-IRN combined forecasting model based on time feature analysis is proposed. Firstly, the temporal characteristics of load variations are thoroughly analyzed, decomposing them into trend components to construct a new time series （NTS） model. Secondly, considering the impact of multi time scale input features and day types on load power, a improve residual neural （IRN） model with feature input structure and deep learning structure is constructed. Finally, the D-S evidence theory is used to weight and fuse the prediction results of the NTS and IRN models to obtain the final load forecasting results. Simulation experiments were conducted using real load data from ISO New England, and the results showed that the proposed model has high prediction accuracy and robustness.

Key words

electric load forecasting / time series analysis / residual neural networks / D-S evidence theory

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Wan Weixing, Xie Lirong, Zhang Longjun, Bian Yifan, Ma Lan. SHORT-TERM LOAD POWER FORECASTING USING NST-IRN COMBINED MODEL BASED ON TEMPORAL FEATURE ANALYSIS[J]. Acta Energiae Solaris Sinica. 2025, 46(7): 62-72 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1869

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