Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

Hayajneh, Ali M.; Alasali, Feras; Salama, Abdelaziz; Holderbaum, William

Abstract

The advancement of sustainable energy sources necessitates the development of robust forecasting tools for efficient energy management. A prominent player in this domain, solar power, heavily relies on accurate energy yield predictions to optimize production, minimize costs, and maintain grid stability. This paper explores an innovative application of tiny machine learning to provide real-time, low-cost forecasting of solar energy yield on resource-constrained edge internet of things devices, such as micro-controllers, for improved residential and industrial energy management. To further contribute to the domain, we conduct a comprehensive evaluation of four prominent machine learning models, namely unidirectional long short-term memory, bidirectional gated recurrent unit, bidirectional long short-term memory, and simple bidirectional recurrent neural network, for predicting solar farm energy yield. Our analysis delves into the impacts of tuning the machine learning model hyperparameters on the performance of these models, offering insights to improve prediction accuracy and stability. Additionally, we elaborate on the challenges and opportunities presented by the implementation of machine learning on low-cost energy management control systems, highlighting the benefits of reduced operational expenses and enhanced grid stability. The results derived from this study offer significant implications for energy management strategies at both household and industrial scales, contributing to a more sustainable future powered by accurate and efficient solar energy forecasting.

Citation

Hayajneh, A. M., Alasali, F., Salama, A., & Holderbaum, W. (2024). Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions. IEEE Access, 12, 10846-10864. https://doi.org/10.1109/access.2024.3354703

• Journal Article Type: Article
• Acceptance Date: Jan 12, 2024
• Publication Date: Feb 2, 2024
• Deposit Date: Jan 21, 2024
• Publicly Available Date: Jan 22, 2024
• Journal: IEEE Access
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Pages: 10846-10864
• DOI: https://doi.org/10.1109/access.2024.3354703
• Keywords: General Engineering, General Materials Science, General Computer Science, Electrical and Electronic Engineering