Journal of Intelligent & Fuzzy Systems 31 (2016) 301–312 DOI:10.3233/IFS-162142 IOS Press 301 A novel intelligent strategy for probabilistic electricity price forecasting: Wavelet neural network based modified dolphin optimization algorithm Mehdi Rafiei, Taher Niknam ∗ and Mohammad Hassan Khooban Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran Abstract. To simplify decision making of market participants, a careful and reliable electricity market price forecasting method is indispensable. Nevertheless, due to the Instability in market clearing prices (MCPs), it is rather tough to forecast MCPs accurately. Using probabilistic forecasting is a new solution to overcome the low accuracy of forecast. Transformation from traditional point forecasts to probabilistic interval forecasts is too important to model the uncertainties of forecasts. Thus the decision making activities of market participants are supported against uncertainties and risks effectively. In this paper a hybrid approach to achieve prediction intervals (PIs) of MCPs is proposed that modified dolphin echolocation optimization algorithm (MDEOA) is applied to estimate point forecasts, model uncertainties, and noise variance. This proposed electricity price probabilistic forecasting method is evaluated by a generalized and comprehensive framework. To test the proposed hybrid method, real price data from Ontario, New England, and, Australian electricity markets are used and effectiveness of the method is validated. Keywords: Probabilistic forecasting, wavelet neural network, modified dolphin echolocation optimization algorithm, wavelet preprocessing, prediction intervals 1. Introduction Throughout the world, the traditional regulated and monopolistic electricity markets are transformed to deregulated and competitive markets. In these deregulated markets, an accurate electricity price forecasting is necessary for market participants in their decision makings such as bidding strate- gies and investment decisions. Nevertheless, due to time varying nature of power demand and the increasing renewable energies, the electricity price is ∗ Corresponding author. Taher Niknam, Ph.D, Professor of Electrical and Electronic Engineering, Department of Electrical and Electronic Engineering, Shiraz University of Technology, Modars Blvd., Shiraz, P.O. 71555-313, Iran. Tel.: +98 711 7264121; Fax: +98 711 7353502; E-mails: khooban@sutech.ac.ir; mhkhoban@gmail.com. greatly variable. Therefore, accurate electricity price forecasting is a very difficult work. According to this complex nature of electricity price forecasting, the importance of probabilistic electricity price fore- casting is so high. The probabilistic forecasts have ability to considering the inherent uncertainties in prices series so they help to overcome the forecasting risks. Hence, to simplify decision making activities, a careful and reliable probabilistic electricity price forecasting is helpful. Most research has focused on point electricity price forecasting. Some of before works are based on time series which can be traced autoregres- sive integrated moving average (ARIMA) [1] and generalized autoregressive conditional heteroscedas- ticity (GARCH) [2]. In [3], the wavelet method is used to improve performance of the ARIMA model. 1064-1246/16/$35.00 © 2016 – IOS Press and the authors. All rights reserved