Short-Term Prediction of Rural Photovoltaic Power Generation Based on Improved Dung Beetle Optimization Algorithm

Jie Meng, Qing Yuan (), Weiqi Zhang (), Tianjiao Yan and Fanqiu Kong
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Jie Meng: School of Architecture and Design, Harbin Institute of Technology, Key Laboratory of National Territory Spatial Planning and Ecological Restoration in Cold Regions, Ministry of Natural Resources, Harbin 150001, China
Qing Yuan: School of Architecture and Design, Harbin Institute of Technology, Key Laboratory of National Territory Spatial Planning and Ecological Restoration in Cold Regions, Ministry of Natural Resources, Harbin 150001, China
Weiqi Zhang: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Tianjiao Yan: School of Architecture and Urban Planning, Jilin Jianzhu University, Changchun 130119, China
Fanqiu Kong: School of Architecture and Civil Engineering, Heilongjiang University of Science and Technology, Harbin 150020, China

Sustainability, 2024, vol. 16, issue 13, 1-26

Abstract: Addressing the challenges of randomness, volatility, and low prediction accuracy in rural low-carbon photovoltaic (PV) power generation, along with its unique characteristics, is crucial for the sustainable development of rural energy. This paper presents a forecasting model that combines variational mode decomposition (VMD) and an improved dung beetle optimization algorithm (IDBO) with the kernel extreme learning machine (KELM). Initially, a Gaussian mixture model (GMM) is used to categorize PV power data, separating analogous samples during different weather conditions. Afterwards, VMD is applied to stabilize the initial power sequence and extract numerous consistent subsequences. These subsequences are then employed to develop individual KELM prediction models, with their nuclear and regularization parameters optimized by IDBO. Finally, the predictions from the various subsequences are aggregated to produce the overall forecast. Empirical evidence via a case study indicates that the proposed VMD-IDBO-KELM model achieves commendable prediction accuracy across diverse weather conditions, surpassing existing models and affirming its efficacy and superiority. Compared with traditional VMD-DBO-KELM algorithms, the mean absolute percentage error of the VMD-IDBO-KELM model forecasting on sunny days, cloudy days and rainy days is reduced by 2.66%, 1.98% and 6.46%, respectively.

Keywords: photovoltaic power generation; short-term power prediction; variational mode decomposition; improved dung beetle optimization algorithm; kernel extreme learning machine; rural low carbon (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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