Design and Development of Hybrid Forecasting Model Using Artificial Neural Networks and Arima Methods for Sustainable Energy Management Systems: a Case Study in Tobacco Industry

Abstract

This study presents a design and development of hybrid forecasting model by using ARIMA and artificial neural networks for short-term energy forecasting processes in energy management systems. Proposed model is applied into a company operating in the tobacco products manufacturing industry and reliability of the model is tested by using real-life data set in illustrative cases. In line with the results obtained from ARIMA method, some of the factors affecting electricity consumption are taken into consideration as input data for artificial neural network model. After considering the correlation between solar energy generation, working hours, production quantities and past electricity consumption data, various number of neurons and different training algorithms are tested to design the optimal system for the company. The proposed hybrid model provides around 39.9% improvement compared to forecast data obtained by using only ARIMA model.