Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Sajjad Khan, Shahzad Aslam, Iqra Mustafa and Sheraz Aslam
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Sajjad Khan: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Shahzad Aslam: Department of Statistics and Mathematics, Institute of Southern Punjab, Multan 66000, Pakistan
Iqra Mustafa: Independent Researcher, Islamabad 44000, Pakistan
Sheraz Aslam: Department of Electrical Engineering, Computer Engineering and Informatics (EECEI), Cyprus University of Technology, Limassol 3036, Cyprus

Forecasting, 2021, vol. 3, issue 3, 1-18

Abstract: Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

Keywords: price forecasting; ensemble empirical mode decomposition; extreme learning machine; hybrid forecasting; smart grids (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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