Ensemble prediction model with expert selection for electricity price forecasting

Bijay Neupane, Wei LeeWoon, Zeyar Aung*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

23 Citations (Scopus)
213 Downloads (Pure)


Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the FixedWeight Method (FWM) and the VaryingWeight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA) method, the Pattern Sequence-based Forecasting (PSF) method and our previous work using Artificial Neural Networks (ANN) alone on the datasets for New York, Australian and Spanish electricity markets.

Original languageEnglish
Article number77
Issue number1
Number of pages27
Publication statusPublished - 2017


  • Electricity price forecasting
  • Ensemble model
  • Expert selection


Dive into the research topics of 'Ensemble prediction model with expert selection for electricity price forecasting'. Together they form a unique fingerprint.

Cite this