Name
Abstract | ||
|---|---|---|
During peak energy demand periods, demand response programs offer incentives to consumers who are willing to shift some of their energy consumption into later hours. In price-based demand response programs, energy pricing is considered an effective control signal for utility companies to reschedule electricity demand during peak hours. In this paper, a real-time closed-loop residential electricity price-based demand response system is proposed. Support vector machines are utilized to forecast the energy demand for each individual household participating in the system via a developed cloud application. An aggregator then accumulates the predicted demand for a local micro-grid to determine peak demand. The hourly electricity prices are then estimated and sent to the consumers to affect their electricity usage during peak hours. The consumer's response to the real time energy price is observed through meter readings using Green Button API. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/PERCOMW.2015.7134054 | PerCom Workshops |
Keywords | Field | DocType |
Demand rescheduling, demand response, deregulated electric market, residential electricity pricing, support vector machine (SVM) | Electricity market,Derived demand,Demand forecasting,Computer science,Demand response,Demand curve,Peak demand,Market demand schedule,Energy consumption,Environmental economics,Distributed computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xing Yan | 1 | 2 | 0.70 |
| Dustin Wright | 2 | 2 | 3.40 |
| S. Kumar | 3 | 257 | 28.32 |
| Gordon K. Lee | 4 | 96 | 29.59 |
| Yusuf Öztürk | 5 | 25 | 2.64 |