Next Article in Journal
Hysteresis Characteristic in the Hump Region of a Pump-Turbine Model
Next Article in Special Issue
Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting
Previous Article in Journal
Some Models for Determination of Parameters of the Soil Oscillation Law during Blasting Operations
Previous Article in Special Issue
Market Equilibrium and Impact of Market Mechanism Parameters on the Electricity Price in Yunnan’s Electricity Market
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Energies 2016, 9(8), 618; doi:10.3390/en9080618

A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection

School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Contreras
Received: 5 May 2016 / Revised: 24 July 2016 / Accepted: 27 July 2016 / Published: 4 August 2016
(This article belongs to the Special Issue Forecasting Models of Electricity Prices)

Abstract

The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist in the literature, there have been few investigations on improving the forecasting effectiveness of electricity price from the perspective of reducing the volatility of data with satisfactory accuracy. Based on reducing the volatility of the electricity price and the forecasting nature of the radial basis function network (RBFN), this paper successfully develops a two-stage model to forecast the day-ahead electricity price, of which the first stage is particle swarm optimization (PSO)-core mapping (CM) with self-organizing-map and fuzzy set (PCMwSF), and the second stage is selection rule (SR). The PCMwSF stage applies CM, fuzzy set and optimized weights to obtain the future price, and the SR stage is inspired by the forecasting nature of RBFN and effectively selects the best forecast during the test period. The proposed model, i.e., CM-PCMwSF-SR, not only overcomes the difficulty of reducing the high volatility of the electricity price but also leads to a superior forecasting effectiveness than benchmarks. View Full-Text
Keywords: selection rule (SR); reducing volatility; self-organizing-map; fuzzy logic; particle swarm optimization (PSO); forecasting selection rule (SR); reducing volatility; self-organizing-map; fuzzy logic; particle swarm optimization (PSO); forecasting
Figures

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

Jiang, P.; Liu, F.; Song, Y. A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection. Energies 2016, 9, 618.

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