Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting

Abstract

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 few data points to construct a time series model without statistical assumptions. Residual modification is often considered as well to improve the accuracy of predictions. Several residual modification models 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 for each predicted residual. However, in the original FLN, an activation function with an inner product assumes that criteria are independent of each other, so additivity might influence the forecasting performance of FLNGM(1,1). Therefore, in this study, we employ the FLN with a fuzzy integral instead of an inner product to propose a nonadditive FLNGM(1,1). Experimental results based on real energy demand cases demonstrate that the proposed grey prediction model performs well compared with other grey residual modification models that use sign estimation and the additive FLNGM(1,1).

Analyzing China’s OFDI using a novel multivariate grey prediction model with Fourier series

“…The prediction accuracy of a grey prediction model can be improved by constructing a residual model (Liu and Lin, 2010), an approach that has been verified effective in the GM(1,1) model by many studies (Hu, 2017a, 2017b; Hu and Jiang, 2017). Some scholars applied the traditional GM(1,1) model (Pi et al , 2010), Markov-chain (Ye et al , 2018), Fourier series (Peng et al , 2017) and neural network (Hu and Jiang, 2017) as residual modification model to construct the residual GM(1,1) model.…”

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting

“…The forecasting results obtained by linear regression and the MLP are summarized in Table 2 . It is clear that the prediction accuracy values of linear regression on the training and testing data were 4.20% and 27.76%, respectively [ 13 ], whereas those of the MLP on the training and testing data were 3.85% and 18.30%, respectively [ 20 ]. Therefore, the proposed GARGM(1,1) outperforms linear regression and the MLP.…”

Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator

“…The grey forecasting model, as one of the significant constituents of grey system theory, has attracted attention and has been actively studied by many scholars. The GM(1,1) model is the most important part of grey prediction theory; however, many scholars discovered that the prediction precision of this model is unstable, and extensive research mainly on the following topics has been conducted: Converting an original sequence for improvement of smoothness [12]; enhancing the computational methods of parameters [13][14][15][16]; modifying residuals of models [17][18][19]; reforming the modelling fashion, and performing some preparations for expanding GM(1,1) models [20]; investigating the modelling conditions [21]; extending the structure of traditional grey prediction models [22]; optimizing the parameters of grey prediction models, such as initial value [23,24], background value [25,26], and the order of accumulating generation operators [27,28]; and combining the grey prediction model with other modelling methods [29,30].…”