Renewable Energy Forecasting using Hybrid Artificial Neural Network Technique

Renewable energy forecasting presented a huge demand for better and accurate prediction models in the electric utilities market. Being able to predict future energy generation not only helps energy traders but also helps utility, schedulers and operators of electricity. This paper presents the application of an iterative hybrid Artificial Neural Network (ANN) method known as Genetic Algorithm based Back-Propagation (GA-BP) neural network on energy forecasting. For analyzing this model the data was taken from a hybrid renewable energy data monitoring system at Renewable Energy Laboratory (REL) of University of Massachusetts Lowell. The system is trained with the first 23 hrs of wind and solar data to predict the next 1 hr combined data, traditionally known as short term forecasting. The results were validated against the available data and other methods of forecasting. The proposed method was implemented in Matlab & Simulink. The proposed method shows a significant improvement in efficiency with a mean relative error of 14% which is better when compared to those other prediction models with Mean relative error of up to 30%.