SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions

Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model. The proposed model is also effective and practical in forecasting existing grid-connected PV power generation.