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Open AccessArticle

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Department of Computer Science, Aalborg University, Fredrik Bajers Vej 5, 9100 Aalborg, Denmark
Department of Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, Block 1A Masdar City, Abu Dhabi 54224, UAE
Author to whom correspondence should be addressed.
Academic Editor: Javier Contreras
Energies 2017, 10(1), 77;
Received: 7 September 2016 / Revised: 30 December 2016 / Accepted: 4 January 2017 / Published: 10 January 2017
(This article belongs to the Special Issue Forecasting Models of Electricity Prices)
PDF [1764 KB, uploaded 10 January 2017]


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 Fixed Weight Method (FWM) and the Varying Weight 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. View Full-Text
Keywords: electricity price forecasting; ensemble model; expert selection electricity price forecasting; ensemble model; expert selection

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Neupane, B.; Woon, W.L.; Aung, Z. Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting. Energies 2017, 10, 77.

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