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

被引:0
|
作者
Meng, Jie [1 ,2 ]
Yuan, Qing [1 ]
Zhang, Weiqi [3 ]
Yan, Tianjiao [4 ]
Kong, Fanqiu [5 ]
机构
[1] Harbin Inst Technol, Sch Architecture & Design, Key Lab Natl Terr Spatial Planning & Ecol Restorat, Minist Nat Resources, Harbin 150001, Peoples R China
[2] East Univ Heilongjiang, Coll Civil Engn & Architecture, Harbin 150066, Peoples R China
[3] Harbin Inst Technol, Sch Elect Engn & Automat, Harbin 150001, Peoples R China
[4] Jilin Jianzhu Univ, Sch Architecture & Urban Planning, Changchun 130119, Peoples R China
[5] Heilongjiang Univ Sci & Technol, Sch Architecture & Civil Engn, Harbin 150020, Peoples R China
关键词
photovoltaic power generation; short-term power prediction; variational mode decomposition; improved dung beetle optimization algorithm; kernel extreme learning machine; rural low carbon; VARIATIONAL MODE DECOMPOSITION; PARTICLE SWARM OPTIMIZATION;
D O I
10.3390/su16135467
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
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页数:26
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