Version 1
: Received: 1 July 2024 / Approved: 1 July 2024 / Online: 2 July 2024 (02:44:16 CEST)
How to cite:
Oliveira Santos, V.; Marinho, F. P.; Costa Rocha, P. A.; Thé, J. V. G.; Gharabaghi, B. Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California. Preprints2024, 2024070109. https://doi.org/10.20944/preprints202407.0109.v1
Oliveira Santos, V.; Marinho, F. P.; Costa Rocha, P. A.; Thé, J. V. G.; Gharabaghi, B. Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California. Preprints 2024, 2024070109. https://doi.org/10.20944/preprints202407.0109.v1
Oliveira Santos, V.; Marinho, F. P.; Costa Rocha, P. A.; Thé, J. V. G.; Gharabaghi, B. Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California. Preprints2024, 2024070109. https://doi.org/10.20944/preprints202407.0109.v1
APA Style
Oliveira Santos, V., Marinho, F. P., Costa Rocha, P. A., Thé, J. V. G., & Gharabaghi, B. (2024). Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California. Preprints. https://doi.org/10.20944/preprints202407.0109.v1
Chicago/Turabian Style
Oliveira Santos, V., Jesse Van Griensven Thé and Bahram Gharabaghi. 2024 "Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California" Preprints. https://doi.org/10.20944/preprints202407.0109.v1
Abstract
Quantum machine learning applications have become viable with the recent advancements in quantum computing. Merging ML with the power of quantum computing holds great potential for data-driven decision-making, as well as the development of more powerful models capable of handling more complex datasets with faster processing time. This area offers potential for improving the accuracy of real-time forecasting renewable energy production. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to design, implement, and evaluate the performance of a quantum neural network forecast model for solar irradiance up to 3-hours ahead. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. Using the best quantum neural network configuration found, the proposed framework could provide competitive results when compared to its competitors, considering forecasting intervals of 5- to 120-minutes ahead, where it was the fourth best-performing paradigm. For 3-hours ahead predictions, the QNN was able to overcome its clas-sical counterparts, but the XGBoost. This fact can be an indication that the quantum model may identify and retrieve relevant spatiotemporal information from the input dataset in such a manner not attainable by the current classical approaches.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
