Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting

Mathematics

Wenbao Wang¹  Yi-Chung Hu²  Geng Wu²  Hang Jiang³  Peng Jiang⁴ 

[1] College of Civil Engineering, Yango University, Fuzhou 350015, China;Department of Business Administration, Chung Yuan Christian University, Taoyuan City 32023, Taiwan;School of Business Administration, Jimei University, Xiamen 361021, China;School of Business, Shandong University, Weihai 264209, China;

关键词: forest combination;    neural network;    interval regression;    grey number;    energy demand;   

DOI  :  10.3390/math8060960

来源: DOAJ

【 摘 要 】

Time series data for decision problems such as energy demand forecasting are often derived from uncertain assessments, and do not meet any statistical assumptions. The interval grey number becomes an appropriate representation for an uncertain and imprecise observation. In order to obtain nonlinear interval grey numbers with better forecasting accuracy, this study proposes a combined model by fusing interval grey numbers estimated by neural networks (NNs) and the grey prediction models. The proposed model first uses interval regression analysis using NNs to estimate interval grey numbers for a real valued sequence; and then a grey residual modification model is constructed using the upper and lower wrapping sequences obtained by NNs. It turns out that two different kinds of interval grey numbers can be estimated by nonlinear interval regression analysis. Forecasting accuracy on real data sequences was then examined by the best non-fuzzy performance values of the combined model. The proposed combined model performed well compared with the other interval grey prediction models considered.

【 授权许可】

Unknown