Prediction of Photovoltaic Power Using Nature-Inspired Computing

Prediction of photovoltaic (PV) energy is an important task. It allows grid operators to plan production of energy in order to secure stability of electrical grid. In this work we focus on improving prediction of PV energy using nature-inspired algorithms for optimization of Support Vector Regression (SVR) models. We propose method, which uses different models optimized for various types of weather in order to achieve higher overall accuracy compared to single optimized model. Each sample is classified by Multi-Layer Perceptron (MLP) into some weather class and then model is trained for each weather class. Our method achieved slightly better results compared to single optimized model.