Summary of A Survey Of Ai-powered Mini-grid Solutions For a Sustainable Future in Rural Communities, by Craig Pirie et al.
A Survey of AI-Powered Mini-Grid Solutions for a Sustainable Future in Rural Communities
by Craig Pirie, Harsha Kalutarage, Muhammad Shadi Hajar, Nirmalie Wiratunga, Subodha Charles, Geeth Sandaru Madhushan, Priyantha Buddhika, Supun Wijesiriwardana, Akila Dimantha, Kithdara Hansamal, Shalitha Pathiranage
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This comprehensive survey of AI-driven mini-grid solutions focuses on enhancing sustainable energy access by providing reliable and affordable electricity to remote communities. It highlights the potential of mini-grids to operate independently or in conjunction with national power grids, as well as the importance of accurate energy forecasting and management due to the inherent unpredictability of renewable energy sources. The 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how AI can help us get clean energy to people who need it most. Right now, many communities don’t have access to reliable electricity because they’re too far from the power grid or can’t afford expensive energy options. Mini-grids are a solution that can provide electricity independently or in combination with national grids. The problem is that renewable energy sources like solar and wind are unpredictable, so we need ways to forecast how much energy will be available and manage it effectively. This paper explores different AI techniques that can help us do just that. It also looks at public datasets and tools that can be used to test these ideas. |
Keywords
* Artificial intelligence * Machine learning
