A New Long-Term Photovoltaic Power Forecasting Model Based on Stacking Generalization Methodology

In recent times, solar energy has become a highly promising source of energy and one of the most regular types of sustainable energy. Forecasting the availability of solar energy has become a concern of many studies because of the intermittent characteristics of solar power. This study proposes a ne...

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Published in:	Natural resources research (New York, N.Y.) Vol. 31; no. 3; pp. 1265 - 1287
Main Authors:	Ofori-Ntow Jnr, Eric, Ziggah, Yao Yevenyo, Rodrigues, Maria Joao, Relvas, Susana
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.06.2022 Springer Nature B.V
Subjects:	Artificial neural networks Back propagation networks Bayesian analysis Chemistry and Earth Sciences Computer Science Earth and Environmental Science Earth Sciences Energy sources Errors Forecasting Fossil Fuels (incl. Carbon Capture) Geography Group method of data handling Mathematical Modeling and Industrial Mathematics Mathematical models Methodology Mineral Resources Neural networks Original Paper Photovoltaics Physics Radial basis function Solar energy Solar power Statistics for Engineering Support vector machines Sustainable Development Sustainable energy Long-term forecasting Photovoltaic power Stacked generalization methodology Solar energy
ISSN:	1520-7439, 1573-8981
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Abstract	In recent times, solar energy has become a highly promising source of energy and one of the most regular types of sustainable energy. Forecasting the availability of solar energy has become a concern of many studies because of the intermittent characteristics of solar power. This study proposes a new stacked generalization methodology for predicting long-term photovoltaic power. In the proposed methodology, the base learners used consisted of group method of data handling (GMDH), least squares support vector machine (LSSVM), emotional neural network (ENN), and radial basis function neural network (RBFNN). The backpropagation neural network (BPNN) served as the meta-learner in the stacked approach. The proposed stacked generalization method showed superiority over the four standalone state-of-the-art methods (GMDH, LSSVM, ENN, and RBFNN) when tested with real data using performance indicators such as Bayesian information criteria (BIC), percent mean average relative error (PMARE), Legates and McCabe index ( LM ), mean absolute error, and root mean square error. The stacked model had the lowest BIC and PMARE values of 10,417.54 and 0.3617% for testing results. It also had the highest LM score of 0.996711 as compared with the benchmark standalone models, confirming its strength in forecasting photovoltaic power.
AbstractList	In recent times, solar energy has become a highly promising source of energy and one of the most regular types of sustainable energy. Forecasting the availability of solar energy has become a concern of many studies because of the intermittent characteristics of solar power. This study proposes a new stacked generalization methodology for predicting long-term photovoltaic power. In the proposed methodology, the base learners used consisted of group method of data handling (GMDH), least squares support vector machine (LSSVM), emotional neural network (ENN), and radial basis function neural network (RBFNN). The backpropagation neural network (BPNN) served as the meta-learner in the stacked approach. The proposed stacked generalization method showed superiority over the four standalone state-of-the-art methods (GMDH, LSSVM, ENN, and RBFNN) when tested with real data using performance indicators such as Bayesian information criteria (BIC), percent mean average relative error (PMARE), Legates and McCabe index ( LM ), mean absolute error, and root mean square error. The stacked model had the lowest BIC and PMARE values of 10,417.54 and 0.3617% for testing results. It also had the highest LM score of 0.996711 as compared with the benchmark standalone models, confirming its strength in forecasting photovoltaic power. In recent times, solar energy has become a highly promising source of energy and one of the most regular types of sustainable energy. Forecasting the availability of solar energy has become a concern of many studies because of the intermittent characteristics of solar power. This study proposes a new stacked generalization methodology for predicting long-term photovoltaic power. In the proposed methodology, the base learners used consisted of group method of data handling (GMDH), least squares support vector machine (LSSVM), emotional neural network (ENN), and radial basis function neural network (RBFNN). The backpropagation neural network (BPNN) served as the meta-learner in the stacked approach. The proposed stacked generalization method showed superiority over the four standalone state-of-the-art methods (GMDH, LSSVM, ENN, and RBFNN) when tested with real data using performance indicators such as Bayesian information criteria (BIC), percent mean average relative error (PMARE), Legates and McCabe index (LM), mean absolute error, and root mean square error. The stacked model had the lowest BIC and PMARE values of 10,417.54 and 0.3617% for testing results. It also had the highest LM score of 0.996711 as compared with the benchmark standalone models, confirming its strength in forecasting photovoltaic power.
Author	Ofori-Ntow Jnr, Eric Ziggah, Yao Yevenyo Relvas, Susana Rodrigues, Maria Joao
Author_xml	– sequence: 1 givenname: Eric orcidid: 0000-0003-3021-2437 surname: Ofori-Ntow Jnr fullname: Ofori-Ntow Jnr, Eric email: eric.jnr@tecnico.ulisboa.pt organization: CEG-IST, Instituto Superior Tecnico, Universidade de Lisboa, Faculty of Engineering, University of Mines and Technology – sequence: 2 givenname: Yao Yevenyo surname: Ziggah fullname: Ziggah, Yao Yevenyo organization: Faculty of Geosciences and Environmental Studies, University of Mines and Technology – sequence: 3 givenname: Maria Joao surname: Rodrigues fullname: Rodrigues, Maria Joao organization: Lisboa E-Nova and Instituto Superior Tecnico, Universidade de Lisboa – sequence: 4 givenname: Susana surname: Relvas fullname: Relvas, Susana organization: CEG-IST, Instituto Superior Tecnico, Universidade de Lisboa
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SubjectTerms	Artificial neural networks Back propagation networks Bayesian analysis Chemistry and Earth Sciences Computer Science Earth and Environmental Science Earth Sciences Energy sources Errors Forecasting Fossil Fuels (incl. Carbon Capture) Geography Group method of data handling Mathematical Modeling and Industrial Mathematics Mathematical models Methodology Mineral Resources Neural networks Original Paper Photovoltaics Physics Radial basis function Solar energy Solar power Statistics for Engineering Support vector machines Sustainable Development Sustainable energy
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Title	A New Long-Term Photovoltaic Power Forecasting Model Based on Stacking Generalization Methodology
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