Next Article in Journal
Assessing the Role of Policies on Land-Use/Cover Change from 1965 to 2015 in the Mu Us Sandy Land, Northern China
Previous Article in Journal
Climate Change Mitigation Pathways for Southeast Asia: CO2 Emissions Reduction Policies for the Energy and Transport Sectors
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(7), 1166; doi:10.3390/su9071166

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

1
College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2
Department of Business Administration, Chung Yuan Christian University, Chung Li Dist., Taoyuan 32023, Taiwan
Received: 2 June 2017 / Revised: 26 June 2017 / Accepted: 27 June 2017 / Published: 3 July 2017
(This article belongs to the Section Energy Sustainability)
View Full-Text   |   Download PDF [1310 KB, uploaded 4 July 2017]   |  

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). View Full-Text
Keywords: energy demand; grey prediction; neural network; fuzzy integral; residual modification energy demand; grey prediction; neural network; fuzzy integral; residual modification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Hu, Y.-C. Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting. Sustainability 2017, 9, 1166.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top