Improving Load Forecasting and Renewable Energy Management for Green Computing using FIS, PSO and Catfish

Abstract

The development and design of energy systems as an integrated part of achieving future 100% Renewable Energy (RE). Therefore, renewable energy systems are investigated and comparatively assessed to solve global energy related issues in a sustainable manner. One of the objective of this work is to investigate the renewable energy systems. Therefore, an optimized Fuzzy Interference System (FIS) is proposed for short term forecasting intervals, which considers time, temperature, pressure and relative humidity. It helps to minimize the operational costs of energy sources. Further, FIS, particle swarm optimization (PSO) and Catfish algorithms have been applied on these parameters to calculate the %error between actual and forecasted load. The simulation results showed that the %error between actual and forecasted load lies under �3%, �5% and �6% for FIS, PSO and Catfish algorithms respectively. The proposed model is also compared with M. Rizwan et. al [75] where the relative error was 6%. The results showed that the proposed model has great potential for practical application in power systems.