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

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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
    Department of Mechanical Engineering, Technology Center, Federal University of Ceará, Fortaleza 60020-181, CE, Brazil)

  • 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)

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.

Suggested Citation

  • Victor Oliveira Santos & Felipe Pinto Marinho & Paulo Alexandre Costa Rocha & Jesse Van Griensven Thé & Bahram Gharabaghi, 2024. "Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California," Energies, MDPI, vol. 17(14), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3580-:d:1439644
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    References listed on IDEAS

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    1. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
    2. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).
    3. Vojtěch Havlíček & Antonio D. Córcoles & Kristan Temme & Aram W. Harrow & Abhinav Kandala & Jerry M. Chow & Jay M. Gambetta, 2019. "Supervised learning with quantum-enhanced feature spaces," Nature, Nature, vol. 567(7747), pages 209-212, March.
    4. Gian-Reto Walther & Eric Post & Peter Convey & Annette Menzel & Camille Parmesan & Trevor J. C. Beebee & Jean-Marc Fromentin & Ove Hoegh-Guldberg & Franz Bairlein, 2002. "Ecological responses to recent climate change," Nature, Nature, vol. 416(6879), pages 389-395, March.
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