Next Article in Journal
Design and Evaluation of a Photovoltaic/Thermal-Assisted Heat Pump Water Heating System
Previous Article in Journal
A Boiler Room in a 600-Bed Hospital Complex: Study, Analysis, and Implementation of Energy Efficiency Improvements
Article Menu

Export Article

Open AccessArticle
Energies 2014, 7(5), 3304-3318; doi:10.3390/en7053304

Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

HEP Trgovina d.o.o., Ulica grada Vukovara 37, Zagreb HR-10000, Croatia
Petrol d.o.o., Oreškovićeva 6/h, Zagreb HR-10010, Croatia
University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, Zagreb HR-10000, Croatia
Author to whom correspondence should be addressed.
Received: 14 April 2014 / Revised: 8 May 2014 / Accepted: 12 May 2014 / Published: 20 May 2014
View Full-Text   |   Download PDF [1198 KB, uploaded 17 March 2015]   |  


Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI) is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX) electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy. View Full-Text
Keywords: data mining; neural network; price volatility; short term electricity price forecasting; forecasting techniques; spot market; electricity price data mining; neural network; price volatility; short term electricity price forecasting; forecasting techniques; spot market; electricity price

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

Cerjan, M.; Matijaš, M.; Delimar, M. Dynamic Hybrid Model for Short-Term Electricity Price Forecasting. Energies 2014, 7, 3304-3318.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



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