Research on short-term photovoltaic power prediction based on LMD-IPSO-LSVM
DOI:
https://doi.org/10.4108/ew.11823Keywords:
Photovoltaic power prediction, Local Mean Decomposition, Least Square Support Vector Machine, Particle Swarm OptimizationAbstract
With the rapid development of human society, resource shortages and environmental degradation have become increasingly pressing issues. To address these challenges, solar energy has garnered significant attention due to its high efficiency, safety, and pollution-free nature. This paper proposes a novel short-term photovoltaic power prediction framework based on an integrated LMD-IPSO-LSVM approach. The model's key innovation lies in its hierarchical decomposition-optimization architecture: First, Local Mean Decomposition (LMD) addresses the non-stationary and nonlinear characteristics of PV power data by decomposing original signals into physically meaningful Product Functions (PFs). Second, an Improved Particle Swarm Optimization (IPSO) algorithm featuring an adaptive inertia weight mechanism is developed to optimize LSVM hyperparameters for each PF component. This strategic integration enables the model to simultaneously capture complex temporal patterns while maintaining superior generalization capability. Experimental validation demonstrates that our IPSO achieves significantly faster convergence (46.3% improvement in convergence speed) and enhanced optimization precision compared to standard PSO, providing a solid foundation for accurate power forecasting. In order to evaluate the proposed methodology, comparative models including standalone LSVM and PSO-LSVM are also established and tested on the same dataset. Experimental results demonstrate that the proposed hybrid model called LMD-IPSO-LSVM achieves high prediction accuracy and better performance compared with other algorithms.
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[1] Research on short-term photovoltaic power prediction ZHOU Y, ZHOU N R, GONG L H, et al. Prediction of photovoltaic power output based on similar day analysis, genetic algorithm, and extreme learning machine[J]. Energy, 2020, 204: 117894.
[2] Duan H M, Luo X L.A novel multivariable grey prediction model and its application in forecasting coal consumption[J]. ISA Transactions,2022,120:110-127.
[3] Zhou W, Ding S.Anovel discrete grey seasonal model and its applications[J]. Communications in Nonlinear Science and Numerical Simulation, 2021, 93:105493.
[4] Zhang Yujin, Yang Lingfan, Ge Shuangzhi, et al. Short-term photovoltaic power forecasting based on K-means algorithm and support vector machine[J]. Power System Protection and Control, 2018, 46 (21): 118-124.
[5] National Energy Administration. The 13th year plan for solar energy development [J] Solar energy, 2016 (12): 5-14.
[6] SHANGGUAN Xin, QIN Wenping, XIA Fuliang, et al. Pole-to-ground fault protection scheme for MMC multi-terminal flexible DC distribution network based on Pearson correlation of transient voltage[J]. High Voltage Engineering, 2020, 46(5): 1740-1749.
[7] Zheng S , Li D , Li Y ,et al.An adaptive weighted stacking ensemble framework for photovoltaic power generation forecasting with joint optimization of features and hyperparameters[J].Engineering Applications of Artificial Intelligence, 2025, 144(000). DOI:10.1016/j.engappai.2025.110075.
[8] Jing Bo Research on short-term power prediction method of photovoltaic power station [D] Jiangsu University, 2017.
[9] Yongqian L, Hao Z, Jic Y, etc. Hybrid relevance vector machine model or wind power forecasting[C]. International Conference on Renewable Power Generation. 1ET, 2016.
[10] Zhang Y,Li S,Li J,et al.A time power-based grey model with Caputo fractional derivative and its application to the prediction of renewable energy consumption[J]. Chaos, Solitons & Fractals, 2022, 164:112750.
[11] Zhou W, Wu X,10Ding S, et al.Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China[J]. Energy, 2020, 200: 117443.
[12] Malvoni M, Giorgi M G D, Congedo P M. Data on photovoltaic power forecasting models for Mediterranean climate[J]. Data in Brief, 2016, 7(C): 1639-1642.
[13] Xu Z C, Dun M, Wu LFE. Evaluating the effect of sample length on forecasting validity of FGM(1,1)[J]. Alexandria Engineering Journal, 2020, 59:4687-4698.
[14] Quaiyum S, Khan Y, Rahman S. Artificial neural network-based short-term load forecasting of power system[J]. International Journal of Computer Applications, 2011, 30(4): 1-7.
[15] Huang R,Wei C,Wang B,et al.Well performance prediction based on Long Short-Term Memory (LSTM)neural network[J]. Journal of Petroleum Science and Engineering, 2022, 208: 109686.
[16] Zhang J, Xia P. An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models [J]. Journal of Sound and Vibration, 2017(5): 153-167.
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Copyright (c) 2026 Yanjuan Ma, Mai Jiang, Limin Zhang
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