FORECASTING OF RENEWABLE ENERGY USING ANN, GPANN AND ANFIS (A COMPARATIVE STUDY AND PERFORMANCE ANALYSIS)

Abstract

The task of forecasting and prediction is deemed to be a salient task in several problems and issues. The main concentration of this thesis is to forecast different types of data based on collected previous data obtained months ago. The data gathered are the global horizontal irradiance (GHI), direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), temperature, and wind speed. The two kinds of data used in this thesis are the wind speed and the temperature. Forecasting and prediction for wind speed and temperature as explored in the current work based on three several methods based on artificial neural network (ANN) and genetic programming fused with artificial neural network (GPANN) and adaptive neuro-fuzzy inference system (ANFIS).