Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions
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 stabil...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10400423/ |
| _version_ | 1797348039881392128 |
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| author | Ali M. Hayajneh Feras Alasali Abdelaziz Salama William Holderbaum |
| author_facet | Ali M. Hayajneh Feras Alasali Abdelaziz Salama William Holderbaum |
| author_sort | Ali M. Hayajneh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T11:57:43Z |
| format | Article |
| id | doaj.art-9f52c90469b44685aa6fe6be1c04aa39 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T11:57:43Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9f52c90469b44685aa6fe6be1c04aa392024-01-24T00:00:42ZengIEEEIEEE Access2169-35362024-01-0112108461086410.1109/ACCESS.2024.335470310400423Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield PredictionsAli M. Hayajneh0https://orcid.org/0000-0003-4238-181XFeras Alasali1https://orcid.org/0000-0002-1413-059XAbdelaziz Salama2https://orcid.org/0000-0002-3339-8292William Holderbaum3https://orcid.org/0000-0002-1677-9624Department of Electrical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, JordanDepartment of Electrical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, JordanDepartment of Electrical and Electronic Engineering, University of Leeds, Leeds, U.K.School of Science, Engineering and Environment, University of Salford, Salford, U.K.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.https://ieeexplore.ieee.org/document/10400423/Solar power forecastingtime series forecastingInternet of Thingsdeep neural networks |
| spellingShingle | Ali M. Hayajneh Feras Alasali Abdelaziz Salama William Holderbaum Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions IEEE Access Solar power forecasting time series forecasting Internet of Things deep neural networks |
| title | Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions |
| title_full | Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions |
| title_fullStr | Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions |
| title_full_unstemmed | Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions |
| title_short | Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions |
| title_sort | intelligent solar forecasts modern machine learning models and tinyml role for improved solar energy yield predictions |
| topic | Solar power forecasting time series forecasting Internet of Things deep neural networks |
| url | https://ieeexplore.ieee.org/document/10400423/ |
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