SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON SIMILAR DAY THEORY AND IPOA-ELM

Kong Linglian; Wang Haiyun; Huang Xiaofang

doi:10.19912/j.0254-0096.tynxb.2024-0743

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 463-473. DOI: 10.19912/j.0254-0096.tynxb.2024-0743

SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON SIMILAR DAY THEORY AND IPOA-ELM

Kong Linglian¹, Wang Haiyun¹, Huang Xiaofang²

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Abstract

Aiming at the problem of insufficient prediction accuracy of large-scale photovoltaic power generation system under non-ideal weather conditions, which in turn triggers the difficulties in the implementation of the power system dispatch plan, a prediction method of photovoltaic power generation is proposed based on the similar day theory and the improved pelican optimization algorithm （IPOA） extreme learning machine （ELM）. First, the Pearson correlation coefficient method filters out meteorological factors that exhibit a significant correlation with PV power generation; then the combined Euclidean distance and Mahalanobis distance evaluation indexes are used to calculate the combined distance between the historical days and the days to be predicted at each time point to determine the similar days. Then, the sample set of similar days is input into the constructed IPOA-ELM power prediction model for training, and based on the actual measurement data, the performance of the IPOA-ELM model is compared with that of the POA-ELM, SCSO-ELM, and GJO-ELM models in terms of prediction accuracy. After comparative analysis, it is concluded that the selection of similar days for the weighted composite index can more accurately reflect the distance and distribution characteristics between each moment point; the IPOA algorithm is optimal in terms of convergence speed and adaptability compared with POA, SCSO and GJO; and the root-mean-square errors of the IPOA-ELM model are lower than the other comparative models in the prediction of photovoltaic power generation under different weather conditions. It is worth noting that the model still shows good stability under rainy weather. It is fully proved that the applied IPOA-ELM prediction model has strong adaptive ability and prediction accuracy.

Key words

photovoltaic power generation / prediction / learning machine / pelican optimization algorithm

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Kong Linglian, Wang Haiyun, Huang Xiaofang. SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON SIMILAR DAY THEORY AND IPOA-ELM[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 463-473 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0743

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