Photovoltaic Knowledge-Informed Neural Network (PKINN): Interpretable power prediction model under Fluctuating Environmental Conditions

Fluctuating Environmental Conditions (FECs) are a critical barrier to accurate photovoltaic (PV) power forecasting. Existing models often fail to capture abrupt and stochastic fluctuations, leading to reduced forecasting reliability. To address this challenge, this study proposes an interpretable Photovoltaic Knowledge-Informed Neural Network (PKINN). The framework incorporates a Quadratic Explicit Model (QEM) to derive explicit expressions of PV power and transparently capture abrupt variations, while a Fluctuation Allocation Mechanism (FAM) employs a fluctuation sensitivity coefficient to quantify fluctuation intensity and allocate input data to specialized prediction branches. The proposed PKINN framework enables adaptive learning across diverse FECs and enhances forecasting performance. Experimental evaluations on two types of PV modules demonstrate that PKINN reduces the root mean square error by at least 8.73% compared with state-of-the-art models across diverse FECs. • PKINN adaptively learns fluctuation patterns under diverse environmental conditions. • QEM formulates explicit expressions to capture abrupt PV power fluctuations. • FAM quantifies stochastic fluctuations across conditions with different intensities. • Experiments demonstrate that PKINN reduces the RMSE of predictions by over 8.73%.