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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.15865 |
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| _version_ | 1866916332572770304 |
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| author | Pirie, Craig Kalutarage, Harsha Hajar, Muhammad Shadi Wiratunga, Nirmalie Charles, Subodha Madhushan, Geeth Sandaru Buddhika, Priyantha Wijesiriwardana, Supun Dimantha, Akila Hansamal, Kithdara Pathiranage, Shalitha |
| author_facet | Pirie, Craig Kalutarage, Harsha Hajar, Muhammad Shadi Wiratunga, Nirmalie Charles, Subodha Madhushan, Geeth Sandaru Buddhika, Priyantha Wijesiriwardana, Supun Dimantha, Akila Hansamal, Kithdara Pathiranage, Shalitha |
| contents | This paper presents a comprehensive survey of AI-driven mini-grid solutions aimed at enhancing sustainable energy access. It emphasises the potential of mini-grids, which can operate independently or in conjunction with national power grids, to provide reliable and affordable electricity to remote communities. Given the inherent unpredictability of renewable energy sources such as solar and wind, the necessity for accurate energy forecasting and management is discussed, highlighting the role of advanced AI techniques in forecasting energy supply and demand, optimising grid operations, and ensuring sustainable energy distribution. This paper reviews various forecasting models, including statistical methods, machine learning algorithms, and hybrid approaches, evaluating their effectiveness for both short-term and long-term predictions. Additionally, it explores public datasets and tools such as Prophet, NeuralProphet, and N-BEATS for model implementation and validation. The survey concludes with recommendations for future research, addressing challenges in model adaptation and optimisation for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey of AI-Powered Mini-Grid Solutions for a Sustainable Future in Rural Communities Pirie, Craig Kalutarage, Harsha Hajar, Muhammad Shadi Wiratunga, Nirmalie Charles, Subodha Madhushan, Geeth Sandaru Buddhika, Priyantha Wijesiriwardana, Supun Dimantha, Akila Hansamal, Kithdara Pathiranage, Shalitha Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science This paper presents a comprehensive survey of AI-driven mini-grid solutions aimed at enhancing sustainable energy access. It emphasises the potential of mini-grids, which can operate independently or in conjunction with national power grids, to provide reliable and affordable electricity to remote communities. Given the inherent unpredictability of renewable energy sources such as solar and wind, the necessity for accurate energy forecasting and management is discussed, highlighting the role of advanced AI techniques in forecasting energy supply and demand, optimising grid operations, and ensuring sustainable energy distribution. This paper reviews various forecasting models, including statistical methods, machine learning algorithms, and hybrid approaches, evaluating their effectiveness for both short-term and long-term predictions. Additionally, it explores public datasets and tools such as Prophet, NeuralProphet, and N-BEATS for model implementation and validation. The survey concludes with recommendations for future research, addressing challenges in model adaptation and optimisation for real-world applications. |
| title | A Survey of AI-Powered Mini-Grid Solutions for a Sustainable Future in Rural Communities |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2407.15865 |