Short-term photovoltaic power forecasting based on the human body amenity and least squares support vector machine with fruit fly optimization algorithm

The human body amenity, which refers to take no effective measures under the premise of protection, can describe the degree of comfort by the person in the natural environment (Wu, 2003). This index can effectively use the various meteorological factors, reduce the input of the network, and improve the precision of forecasting. The Least Squares Support Vector Machine (LSSVM) extended by using the Support Vector Machine (SVM) is a powerful regression tool with a dynamic network structure (Cortes, 1995). The Fruit fly Optimization Algorithm (FOA) proposed by the scholar Pan (Pan, 2012) is a novel evolutionary computation and optimization technique. This new optimization algorithm has the advantages of being easy to understand and to be

1 INTRODUCTION

Along with our country paying more attention to environment and energy sources, PhotoVoltaic (PV) power generation becomes more important in theory and actual use because of little pollution and high energy utilization. Accurate PV power forecasting can relieve the conflict between electricity supply and demand. Short-term forecasting will contribute to arrange the output of the conventional electric power supply by using the electric power dispatching system (Damousis, 2004; Ai-Hamadi, 2005; Almonacid, 2009). Lu and Qin (Lu, 2011) applied metrical information including temperature, pressure, humidity, and solar irradiance as inputs to predict the day type, and then calculated the power output of PV power generation under day types. However, the cycle of this forecasting model is very shorter, only in per hour.