Open Access

ARTICLE

Coupled Meteorological-Electricity Behavior Analysis and Multi-Energy Load Forecasting Based on a Combined Model

Nantian Huang^*, Jingyuan Zhang, Shicheng Ren, Hao Zhang, Bingling Li, Yaoyao Wang

Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, 132012, China

* Corresponding Author: Nantian Huang. Email:

Energy Engineering 2026, 123(8), 22 https://doi.org/10.32604/ee.2025.072993

Received 08 September 2025; Accepted 17 October 2025; Issue published 12 July 2026

Abstract

User electricity consumption behavior analysis and multi-load forecasting in integrated energy systems are crucial for system operation and scheduling. Traditional user electricity consumption behavior analysis fails to adequately incorporate meteorological factors, limiting the accuracy of characterizing user electricity consumption patterns. Traditional multi-load forecasting models do not consider the differentiated coupling relationships with meteorological factors across different seasons, which restricts the improvement of forecasting accuracy. To address the above issues, a method integrating data cleaning and meteorological correlation for electricity consumption behavior and multi-dimensional forecasting analysis is proposed. First, the Akima interpolation method is used to repair anomalous points in the load data. Second, the BK-Means algorithm is employed to determine the optimal number of clusters and initial centers, achieving a coupled analysis of wind-solar power output, load-meteorology clustering, and user electricity consumption behavior. Subsequently, the Kendall rank correlation coefficient method is applied to analyze the correlation between multi-dimensional loads and meteorological factors, constructing differentiated input feature sets for various loads tailored to different seasons. Finally, a combined model is used to generate multi-load forecasting results. The results demonstrate that compared to single forecasting methods, the proposed method achieves higher forecasting accuracy for electricity, cool, and heating loads across different seasons.

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Keywords

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Supplementary Material

Supplementary Material File

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