Integrating Nonlinear Interval Regression Analysis with a Remnant Grey Prediction Model for Energy Demand Forecasting

Abstract

Energy demand forecasting is increasingly important for developing national energy policies. This study aims to apply the first order gray model with one variable (GM(1,1)) without following any statistical assumptions to energy demand forecasting. To boost the forecasting accuracy of GM(1,1), a problem arising from collected samples that are often derived from an uncertain assessment should be addressed. One way to deal with these uncertain and imprecise observations is by using nonlinear interval regression analysis with neural networks to generate upper and lower limits for individual samples. As a result, a nonlinear interval gray prediction model is constructed by applying the sequences of upper and lower limits to construct GM(1,1) with residual modification separately. By examining the forecasting performance of a nonlinear interval model by the best non-fuzzy performance values, the empirical results obtained based on real energy demand data show that the proposed models perform well compared with other interval gray prediction models. This study has shown the high applicability of the proposed model to energy demand forecasting.