Ubiquitous Computing and Communication Journal
Disseminator of Knowledge
Home Home | About Us About Us | Contact Us Contact Us | Login Saturday, April 4, 2020
Title: Integration of Fuzzy Inference Engine with Radial Basis Function Neural Network for Short Term Load Forecasting
Authors: Mr. Ajay Shekhar Pandey, Dr. Sunil Kumar Sinha, Dr. Devender Singh
This paper proposes a fuzzy inference based neural network for the forecasting of short term loads. The forecasting model is the integration of fuzzy inference engine and the neural network, known as Fuzzy Inference Neural Network (FINN). A FINN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that the output of the FINN adequately matches the available historical load data. Results show that the FINN can forecast future loads with an accuracy comparable to that of neural networks, while its training is much faster than that of neural networks. Simulation results indicate that hybrid fuzzy neural network is one of the best candidates for the analysis and forecasting of electricity demand. Radial Basis Function Neural Network (RBFNN) integrated with Fuzzy Inference Engine has been used to create a Short Term Load Forecasting model.