Nonadditive grey prediction using functional-link net for energy demand forecasting

Abstract

Energy demand prediction plays an important role in sustainable development. The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a fewdata points to construct a time series model without statistical assumptions. Residual modificationis often considered as well to improve the accuracy of predictions. Several residual modificationmodels have been proposed, but they focused on residual sign estimation, whereas the FLNGM(1,1)model using functional-link net (FLN) can estimate the sign as well as the modification range foreach predicted residual. However, in the original FLN, an activation function with an inner productassumes that criteria are independent of each other, so additivity might influence the forecastingperformance of FLNGM(1,1). Therefore, in this study, we employ the FLN with a fuzzy integralinstead of an inner product to propose a nonadditive FLNGM(1,1). Experimental results based on realenergy demand cases demonstrate that the proposed grey prediction model performs well comparedwith other grey residual modification models that use sign estimation and the additive FLNGM(1,1).