Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach
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...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8735862/ |
| _version_ | 1818910091871518720 |
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| author | Alireza Pourdaryaei Hazlie Mokhlis Hazlee Azil Illias S. Hr. Aghay Kaboli Shameem Ahmad |
| author_facet | Alireza Pourdaryaei Hazlie Mokhlis Hazlee Azil Illias S. Hr. Aghay Kaboli Shameem Ahmad |
| author_sort | Alireza Pourdaryaei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T22:37:18Z |
| format | Article |
| id | doaj.art-01fefdae9b5245a6b47bf338dc8f6db4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T22:37:18Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-01fefdae9b5245a6b47bf338dc8f6db42022-12-21T20:03:09ZengIEEEIEEE Access2169-35362019-01-017776747769110.1109/ACCESS.2019.29224208735862Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS ApproachAlireza Pourdaryaei0Hazlie Mokhlis1https://orcid.org/0000-0002-1166-1934Hazlee Azil Illias2S. Hr. Aghay Kaboli3Shameem Ahmad4Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaElectrical Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, KuwaitElectrical Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, KuwaitDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaIn 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.https://ieeexplore.ieee.org/document/8735862/Adaptive neuro-fuzzy inference systembacktracking search algorithmelectricity price forecastingfeature selection |
| spellingShingle | Alireza Pourdaryaei Hazlie Mokhlis Hazlee Azil Illias S. Hr. Aghay Kaboli Shameem Ahmad Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach IEEE Access Adaptive neuro-fuzzy inference system backtracking search algorithm electricity price forecasting feature selection |
| title | Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach |
| title_full | Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach |
| title_fullStr | Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach |
| title_full_unstemmed | Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach |
| title_short | Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach |
| title_sort | short term electricity price forecasting via hybrid backtracking search algorithm and anfis approach |
| topic | Adaptive neuro-fuzzy inference system backtracking search algorithm electricity price forecasting feature selection |
| url | https://ieeexplore.ieee.org/document/8735862/ |
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