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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Veröffentlicht in:Natural resources research (New York, N.Y.) Jg. 31; H. 3; S. 1265 - 1287
Hauptverfasser: Ofori-Ntow Jnr, Eric, Ziggah, Yao Yevenyo, Rodrigues, Maria Joao, Relvas, Susana
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2022
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1520-7439
1573-8981
DOI:10.1007/s11053-022-10058-3