Short-term prediction of photoelectric power considering the influence of fog and haze

Abstract

Accurate photovoltaic (PV) power prediction is an important basis for ensuring the safe and stable operation of high-permeability PV power generation systems. There are many environmental factors affecting the output power of PV power generation, and the influence of fog and haze on PV power cannot be ignored. In this paper the main factors of affecting PV power and the necessity of accounting for fog and haze in PV power prediction are described. The Air Quality Index (AQI) is selected as an indicator to measure the severity of fog and haze, and it is included in the historical data source of PV power prediction together with weather conditions. Aiming at the complex nonlinear problem of PV power prediction, a PV power prediction model based on BP neural network algorithm in fog and haze weather is established. In order to ensure the accuracy of PV power prediction, a training sample selection method based on ”similar time” is proposed. The analysis of the example verifies the necessity of considering fog and haze in the PV power prediction and the effectiveness of the proposed method.