Hybrid Random Forest and Particle Swarm Optimization Algorithm for Solar Radiation Prediction

摘要

Due to increased pollution, greenhouse effect and global warming resulting from power production using fossil fuels, there is increased penetration of renewable energy sources into the power system. The concept of microgrids has made solar radiation an indispensable source of power in the distribution system. But the power production using solar energy is highly variable and weather dependent which creates a power imbalance into the system when it is penetrated without forecasting. Therefore, solar power prediction plays a critical role in the proper usage of solar energy while keeping the system stable. For automating the power system, the forecast needs to be very accurate and thus, it is needed to improve the existing forecasting techniques. In this study, we have proposed a solar radiation scheme based on various meteorological factors, including temperature, humidity, wind speed, and others. We have introduced a hybrid model for prediction which optimizes the parameters of Random Forest using Particle Swarm Optimization technique.