Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California

Victor Oliveira Santos, Felipe Pinto Marinho, Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé and Bahram Gharabaghi ()
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Victor Oliveira Santos: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Felipe Pinto Marinho: Department of Teleinformatics Engineering, Technology Center, Federal University of Ceará, Fortaleza 60020-181, CE, Brazil
Paulo Alexandre Costa Rocha: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Jesse Van Griensven Thé: Lakes Environmental Research Inc., 170 Columbia St W, Waterloo, ON N2L 3L3, Canada
Bahram Gharabaghi: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada

Energies, 2024, vol. 17, issue 14, 1-26

Abstract: Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m 2 and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows.

Keywords: renewable energy; solar irradiance forecast; quantum machine learning; machine learning; Folsom dataset; Qiskit (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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