RESEARCH ON COMBINED PHOTOVOLTAIC OUTPUT PREDICTION MODEL BASED ON KPCA, HC AND IPSO-LSTM

Xu Chang; Xu Ye; Wang Xiaohui; Meng Yikang; Qin Yu; Li Wei

doi:10.19912/j.0254-0096.tynxb.2024-0159

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 362-374. DOI: 10.19912/j.0254-0096.tynxb.2024-0159

RESEARCH ON COMBINED PHOTOVOLTAIC OUTPUT PREDICTION MODEL BASED ON KPCA, HC AND IPSO-LSTM

Xu Chang¹, Xu Ye¹, Wang Xiaohui², Meng Yikang¹, Qin Yu¹, Li Wei¹

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Abstract

To improve the prediction accuracy of photovoltaic （PV） power generation, a combined PV output prediction model is established by incorporating kernel principal component analysis （KPCA）, hierarchical clustering （HC）, improved particle swarm optimization （IPSO） and long short-term memory （LSTM） into a general framework. The KPCA method is firstly used to reduce the dimension of meteorological factors affecting photovoltaic output and generate their principal component factors. Then, the HC algorithm and comprehensive similarity distance method are employed jointly to select historical similar days with the high internal correlation and similar meteorological characteristics to the predicted day. Next, the hyperparameter combination of LSTM neural network is optimized by the IPSO algorithm for establishing high-precision prediction model. Finally, the power generation of a photovoltaic plant in Yunnan province is accurately estimated. Compared with other prediction models, the combined forecasting method proposed in this study achieves better forecasting results under various weather types and has the broad application prospects.

Key words

kernel principal component analysis / photovoltaic output prediction / improved particle swarm optimization / hyperparameter optimization / comprehensive similarity distance

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Xu Chang, Xu Ye, Wang Xiaohui, Meng Yikang, Qin Yu, Li Wei. RESEARCH ON COMBINED PHOTOVOLTAIC OUTPUT PREDICTION MODEL BASED ON KPCA, HC AND IPSO-LSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 362-374 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0159

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