Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria
This paper presents load forecasting and optimal sizing for minimizing the Annualized Cost of the System (ACS) of a stand-alone photovoltaic (PV)/wind/battery hybrid renewable energy system. To achieve load forecasting, the Support Vector Regression (SVR) was integrated with the emerging Harris Hawk...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302100061X |
| _version_ | 1818402625685553152 |
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| author | S.I. Abba Bara'u Gafai Najashi Abdulazeez Rotimi Bashir Musa Nasser Yimen S.J. Kawu S.M. Lawan Mustafa Dagbasi |
| author_facet | S.I. Abba Bara'u Gafai Najashi Abdulazeez Rotimi Bashir Musa Nasser Yimen S.J. Kawu S.M. Lawan Mustafa Dagbasi |
| author_sort | S.I. Abba |
| collection | DOAJ |
| description | This paper presents load forecasting and optimal sizing for minimizing the Annualized Cost of the System (ACS) of a stand-alone photovoltaic (PV)/wind/battery hybrid renewable energy system. To achieve load forecasting, the Support Vector Regression (SVR) was integrated with the emerging Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) algorithms to form two hybrid SVR algorithms (SVR-HHO and SVR-PSO). The single SVR and the two obtained hybrid SVR algorithms were used to predict the load demand variability of remote areas in Kano and Abuja, Nigeria. For optimal sizing, a PSO algorithm was used. The prediction accuracy of the algorithms was evaluated using Correlation Coefficient (R), Coefficient of Determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The results show that both hybrid SVR algorithms outperformed the single SVR in terms of prediction accuracy. Furthermore, SVR-HHO has the highest goodness of fit and lowest prediction error. Besides, the SVR-HHO proved merit over SVR-PSO despite its reliability. These results concluded that metaheuristic algorithms are more promising in forecasting load demand and hence can serve as a reliable tool for decision making. |
| first_indexed | 2024-12-14T08:11:20Z |
| format | Article |
| id | doaj.art-ed81352a70bc4b2b8701d28229e3fb71 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-12-14T08:11:20Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-ed81352a70bc4b2b8701d28229e3fb712022-12-21T23:10:04ZengElsevierResults in Engineering2590-12302021-12-0112100260Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, NigeriaS.I. Abba0Bara'u Gafai Najashi1Abdulazeez Rotimi2Bashir Musa3Nasser Yimen4S.J. Kawu5S.M. Lawan6Mustafa Dagbasi7Department of Civil Engineering, Faculty of Engineering, Baze University, Abuja, 900108, Nigeria; Corresponding author.Department of Electrical Engineering, Faculty of Engineering, Baze University, Abuja, 900108, NigeriaDepartment of Civil Engineering, Faculty of Engineering, Baze University, Abuja, 900108, NigeriaDepartment of Energy Systems Engineering, Cyprus International University, Nicosia, 99258, CyprusNational Advanced School of Engineering, University of Yaoundé I, POB 8390, Yaoundé, CameroonDepartment of Mechanical Engineering, Faculty of Engineering, Baze University, Abuja, 900108, NigeriaDepartment of Electrical Engineering, Faculty of Engineering, Kano University of Science and Technology, Wudil, 713211, NigeriaNational Advanced School of Engineering, University of Yaoundé I, POB 8390, Yaoundé, Cameroon; Department of Energy Systems Engineering, Cyprus International University, Nicosia, 99258, CyprusThis paper presents load forecasting and optimal sizing for minimizing the Annualized Cost of the System (ACS) of a stand-alone photovoltaic (PV)/wind/battery hybrid renewable energy system. To achieve load forecasting, the Support Vector Regression (SVR) was integrated with the emerging Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) algorithms to form two hybrid SVR algorithms (SVR-HHO and SVR-PSO). The single SVR and the two obtained hybrid SVR algorithms were used to predict the load demand variability of remote areas in Kano and Abuja, Nigeria. For optimal sizing, a PSO algorithm was used. The prediction accuracy of the algorithms was evaluated using Correlation Coefficient (R), Coefficient of Determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The results show that both hybrid SVR algorithms outperformed the single SVR in terms of prediction accuracy. Furthermore, SVR-HHO has the highest goodness of fit and lowest prediction error. Besides, the SVR-HHO proved merit over SVR-PSO despite its reliability. These results concluded that metaheuristic algorithms are more promising in forecasting load demand and hence can serve as a reliable tool for decision making.http://www.sciencedirect.com/science/article/pii/S259012302100061XForecastingHarris-hawksLoad demandOptimal sizingParticle swarm optimizationAnnualized cost of the system |
| spellingShingle | S.I. Abba Bara'u Gafai Najashi Abdulazeez Rotimi Bashir Musa Nasser Yimen S.J. Kawu S.M. Lawan Mustafa Dagbasi Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria Results in Engineering Forecasting Harris-hawks Load demand Optimal sizing Particle swarm optimization Annualized cost of the system |
| title | Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria |
| title_full | Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria |
| title_fullStr | Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria |
| title_full_unstemmed | Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria |
| title_short | Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria |
| title_sort | emerging harris hawks optimization based load demand forecasting and optimal sizing of stand alone hybrid renewable energy systems a case study of kano and abuja nigeria |
| topic | Forecasting Harris-hawks Load demand Optimal sizing Particle swarm optimization Annualized cost of the system |
| url | http://www.sciencedirect.com/science/article/pii/S259012302100061X |
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