Comprehensive evaluation methods for photovoltaic output anomalies based on weather classification

Abstract

This article analyzes the relationship between abnormal photovoltaic output events and different weather types based on the output data of distributed photovoltaic stations and meteorological reanalysis data of corresponding time periods using methods such as K-means clustering. The analysis finds that abnormal high output events in photovoltaics are related to high-temperature clear weather brought by stable low-pressure systems, and clear windy cooling weather processes controlled by high pressure. The abnormal low output events of photovoltaics are related to transitional weather processes such as cold waves, cloudy and precipitation free weather, and cloudy and rainy weather processes in low-pressure systems. Building upon this analysis, the study constructs a simple extreme output prediction model and examines the atmospheric circulation anomalies corresponding to extreme output events. Using a January 2023 photovoltaic low-output event as an example, the study validates both the extreme output model based on weather type classification and the subjective weather forecast method based on atmospheric circulation patterns, both of which show potential for improving photovoltaic extreme output predictions. The combination of the two can be used to comprehensively evaluate the photovoltaic extreme output events.