A generic methodology to efficiently integrate weather information in short-term Photovoltaic generation forecasting models. (15th September 2022) - Bellinguer, Kevin; Girard, Robin; Bontron, Guillaume; Kariniotakis, Georges
Abstract: The power output of Photovoltaic (PV) plants is weather-dependent, which results in inherent uncertainties about future production. This raises technical challenges for grid operators, especially in power systems with high PV penetration, and also financial losses when PV generation is traded on electricity markets. Accurate forecasts for the next hours or days contribute to alleviating these impacts. The literature features a plethora of forecasting models, among which outstanding approaches combine heterogeneous sources of inputs like measurements, weather forecasts and satellite images. The integration of such inputs into forecast models can take two forms: either as explanatory features, or as state features that condition the model training through a local regression approach. With the latter, physics-based information can be included within statistical regression tools to derive optimised models w.r.t. weather input. These models are then extended to integrate spatio-temporal information from satellite observations. We investigate these approaches with the objective of deriving the mathematical foundations of a generic methodology to integrate weather information into PV forecasting models. The paper assesses the influence of weather information integration strategies on forecasting performances for two state-of-the-art short-term forecasting models, belonging respectively to linear and non-linear families. Lastly, general guidelines for forecasters are derived regarding the procedure to follow when dealing with several sources of information. Evaluations are performed on real-world datasets composed of nine PV plants. Graphical abstract: Highlights: A weather conditioning methodology of PV power forecasting models is proposed. The coupling of spatio-temporal data and weather conditioning is investigated. Weather conditioning is adapted for linear forecasting models. Non-linear forecasting models perform better when fed with explanatory features. General guidelines to optimise forecasting performances are provided to forecasters.