Name
Abstract | ||
|---|---|---|
Demand response (DR) is one of the most effective ways to maintain the reliability and improve the flexibility of power systems. Accurate forecasts of baseline loads are essential for DR programs. In the era of big data, machine learning-based approaches present a unique opportunity for baseline load forecasting. Thus, this paper presents a machine learning-based approach using a relatively less e... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/ANNSIM52504.2021.9552156 | 2021 Annual Modeling and Simulation Conference (ANNSIM) |
Keywords | DocType | ISBN |
aggregate baseline load forecasting,artificial neural network,demand response,Gaussian process regression,machine learning | Conference | 978-1-56555-375-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kadir Amasyali | 1 | 0 | 1.69 |
| Mohammed Olama | 2 | 0 | 2.03 |