Summary of Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-series Forecasting, by Manuel Sage and Joshua Campbell and Yaoyao Fiona Zhao
Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series Forecasting
by Manuel Sage, Joshua Campbell, Yaoyao Fiona Zhao
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Operating Systems (cs.OS); Systems and Control (eess.SY)
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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 paper proposes a novel approach to energy arbitrage by combining deep reinforcement learning (DRL) with time-series forecasting methods from deep learning. The goal is to enhance the performance of energy arbitrage by training DRL agents to plan battery dispatch based on future electricity prices. The authors use price data from Alberta, Canada, which features irregular price spikes and non-stationary patterns. Despite imperfections in individual predictors, combining multiple predictions for the next 24-hour window improves accumulated rewards by 60% using deep Q-networks (DQN). This suggests that DRL agents can learn more profitable control policies by aggregating imperfect predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses artificial intelligence to help battery operators make money by buying and selling electricity at different prices. The challenge is predicting how much money they’ll make, which is hard because electricity prices are unpredictable. The authors combine two types of machine learning – deep reinforcement learning and time-series forecasting – to improve the predictions. They test their approach using real data from Alberta, Canada, where electricity prices can be very high or low at different times. By combining multiple predictions, they find that they can make more money than before. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Reinforcement learning » Time series
