Abstract
Given that building loads consume roughly 40% of the energy produced in developed countries, smart buildings with local renewable resources offer a viable alternative towards achieving a greener future. Building temperature control strategies typically employ detailed physical models which require a significant amount of time, information and finesse. Even then, due to unknown building parameters and related inaccuracies, future power demands by the building loads are difficult to estimate. This creates unique challenges in the domain of microgrid economic power dispatch for satisfying building power demands through efficient control and scheduling of renewable and non-renewable local resources in conjunction with supply from the main grid. In this work, we estimate the real-time uncertainties in building loads using Gaussian Process (GP) learning and establish the effectiveness of run time model correction in the context of microgrid economic dispatch.
Original language | English (US) |
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Title of host publication | Proceedings of the 2021 Design, Automation and Test in Europe, DATE 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 72-75 |
Number of pages | 4 |
ISBN (Electronic) | 9783981926354 |
DOIs | |
State | Published - Feb 1 2021 |
Event | 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 - Virtual, Online Duration: Feb 1 2021 → Feb 5 2021 |
Publication series
Name | Proceedings -Design, Automation and Test in Europe, DATE |
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Volume | 2021-February |
ISSN (Print) | 1530-1591 |
Conference
Conference | 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 |
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City | Virtual, Online |
Period | 2/1/21 → 2/5/21 |
Funding
The work was funded by MHRD and Department of Power, Govt. of India, under the IMPRINT project no. 6158.
Keywords
- Building thermal model
- Deep Reinforcement Learning
- Economic Dispatch
- Gaussian Process Learning
- Predictive Control
ASJC Scopus subject areas
- General Engineering