Name
Abstract | ||
|---|---|---|
Battery-based energy storage has emerged as an enabling technology for a variety of grid energy optimizations, such as peak shaving and cost arbitrage. A key component of battery-driven peak shaving optimizations is peak forecasting, which predicts the hours of the day that see the greatest demand. While there has been significant prior work on load forecasting, we argue that the problem of predicting periods where the demand peaks for individual consumers or micro-grids is more challenging than forecasting load at a grid scale. We propose a new model for peak forecasting, based on deep learning, that predicts the k hours of each day with the highest and lowest demand. We evaluate our approach using a two year trace from a real micro-grid of 156 buildings and show that it outperforms the state of the art load forecasting techniques adapted for peak predictions by 11--32%. When used for battery-based peak shaving, our model yields annual savings of $496,320 for a 4 MWhr battery for this micro-grid.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3396851.3397751 | e-Energy '20: The Eleventh ACM International Conference on Future Energy Systems
Virtual Event
Australia
June, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-8009-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soman Akhil | 1 | 0 | 0.34 |
| Trivedi Amee | 2 | 0 | 0.34 |
| David E. Irwin | 3 | 899 | 98.12 |
| Kosanovic Beka | 4 | 0 | 0.34 |
| McDaniel Benjamin | 5 | 0 | 0.34 |
| Prashant J. Shenoy | 6 | 6386 | 521.30 |