Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Energies 2012, 5(1), 1-21; doi:10.3390/en5010001
Article

Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models

1,2
, 2,3,*  and 2,3
Received: 29 September 2011; in revised form: 8 November 2011 / Accepted: 7 December 2011 / Published: 22 December 2011
(This article belongs to the Special Issue Intelligent Energy Demand Forecasting)
Download PDF [2330 KB, updated 28 December 2011; original version uploaded 22 December 2011]
Abstract: This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF) model as the forecaster and utilizes the Hodrick–Prescott (HP) filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI) is employed to select the most relevant input features with least possible redundancies for the forecast model. Each generated component by the HP filter is then modeled through an LLNF model. Starting from an optimal least square estimation, the local linear model tree (LOLIMOT) learning algorithm increases the complexity of the LLNF model as long as its performance is improved. The proposed HPLLNF model with MI-based input selection is applied to the problem of long-term energy forecasting in three different case studies, including forecasting of the gasoline, crude oil and natural gas demand over the next 12 months. The obtained forecasting results reveal the noteworthy performance of the proposed approach for long-term energy demand forecasting applications.
Keywords: local linear neuro-fuzzy (LLNF) model; Hodrick–Prescott (HP) filter; HPLLNF; mutual information (MI); energy demand forecasting local linear neuro-fuzzy (LLNF) model; Hodrick–Prescott (HP) filter; HPLLNF; mutual information (MI); energy demand forecasting
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

Iranmanesh, H.; Abdollahzade, M.; Miranian, A. Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models. Energies 2012, 5, 1-21.

AMA Style

Iranmanesh H, Abdollahzade M, Miranian A. Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models. Energies. 2012; 5(1):1-21.

Chicago/Turabian Style

Iranmanesh, Hossein; Abdollahzade, Majid; Miranian, Arash. 2012. "Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models." Energies 5, no. 1: 1-21.


Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert