Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach

Pourdaryaei, Alireza and Mokhlis, Hazlie and Illias, Hazlee Azil and Kaboli, S. Hr. Aghay and Ahmad, Shameem (2019) Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach. IEEE Access, 7. pp. 77674-77691. ISSN 2169-3536, DOI

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In this paper, a hybrid electricity price forecasting method which is composed of two-stage feature selection method and optimized adaptive neuro-fuzzy inference system (ANFIS) technique as a forecasting engine is proposed to accurately forecast electricity price. A multi-objective feature selection approach comprises of multi-objective binary-valued backtracking search algorithm (MOBBSA) as an efficient evolutionary search algorithm and ANFIS method is developed in this paper to extract the most influential subsets of input variables with maximum relevancy and minimum redundancy. Through the combination of backtracking search algorithm (BSA) in learning process of ANFIS approach, a hybrid machine learning algorithm has been developed to forecast the electricity price more accurately. Real-world electricity demand and price dataset from Ontario power market; which is reported as among the most volatile market worldwide, has been used as case study to validate the performance of the proposed approach. From the simulation results, it has been seen that the proposed hybrid forecasting method was effective in accurately forecast the Ontario electricity price. In addition, to prove the superiority of the proposed hybrid forecasting method the simulation results obtained using ANN and ANFIS models optimized by other well-known optimization methods have been compared with that of proposed method. © 2019 IEEE.

Item Type: Article
Funders: University of Malaya (UM) under postgraduate research under Grant (PPP): PG323-2016A
Uncontrolled Keywords: Adaptive neuro-fuzzy inference system; backtracking search algorithm; electricity price forecasting; feature selection
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 12 Feb 2020 00:51
Last Modified: 12 Feb 2020 00:51

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