Abstract
Smart grids offer better energy management at consumer premises as well as energy companies side using bi- directional communication and control. Energy companies can balance energy supply and demand to a large extent, with the advent of smart homes. They can also nudge consumers to shift their demands to off-peak hours for load balancing and monetary benefits. We propose a decentralized demand scheduling algorithm that minimizes consumer discomfort and electricity cost of a household. Our algorithm utilizes only aggregated energy consumption of a household to derive optimal appliance level demand schedules. Furthermore, a low-complexity energy disaggregation algorithm is proposed to derive fine- grained appliance information and consumer preferences. We propose three important coefficients related to energy usage of consumers. We utilize them to derive optimal day- ahead demand schedules. The decentralized algorithm is empirically evaluated using real-world energy usage data from open datasets, which include our own deployment. Our proposed scheduling algorithm saves up to 30% energy cost. This work is one of the first to derive day-ahead schedules using real-world data from multiple households.
Original language | English |
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Title of host publication | 2016 IEEE Glocal Communications Conference (GLOBECOM) |
Subtitle of host publication | Proceedings |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 1-7 |
Number of pages | 7 |
ISBN (Electronic) | 978-1-5090-1328-9 |
DOIs | |
Publication status | Published - Dec 2016 |
Keywords
- Home appliances
- Schedules
- Scheduling algorithms
- Energy consumption
- Aggregates
- Smart homes
- Pricing