Summary of A Reinforcement Learning Approach to Dairy Farm Battery Management Using Q Learning, by Nawazish Ali et al.
A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning
by Nawazish Ali, Abdul Wahid, Rachael Shaw, Karl Mason
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting aims to improve the use of renewable energy in the sector. The algorithm reduces the cost of imported electricity from the grid by 13.41%, peak demand by 2%, and 24.49% when utilizing wind generation. This is achieved by effectively managing battery charging and discharging, which poses significant challenges due to fluctuations in electrical consumption, intermittent renewable energy generation, and energy prices. The study also explores the effect of adding wind generation data and considering additional case studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dairy farming uses a lot of energy, making it important to find ways to use more renewable energy. This paper looks at using artificial intelligence (AI) to help dairy farms use renewable energy better. One problem is that batteries need to be charged and discharged in the right way, which can be tricky because of changes in how much electricity is being used, when renewable energy is available, and what energy prices are like. The researchers created a new algorithm to solve this problem using something called Q-learning. They tested it on dairy farms in Ireland and found that it could save money by reducing the amount of electricity needed from the grid. |
