Name
Title | ||
|---|---|---|
An SVR-based Building-level Load Forecasting Method Considering Impact of HVAC Set Points |
Abstract | ||
|---|---|---|
This paper focuses on a technique to determine the impact of HVAC set point adjustments on building-level electrical load (kW) utilizing Support Vector Regression (SVR) with the minimum possible set of input variables. The paper uses two SVR-based forecasting methods, namely single-step and recursive models. These models are used to forecast hourly electrical loads of a commercial building in Chicago area for the summer period from 8AM to 8PM. The model accuracy is observed to be higher than 95% for hour-ahead load forecasts, and higher than 93% for 12-hour ahead load forecasts. The models presented in the paper can be used to quantify the reduction in electrical load (kW) based on HVAC set point adjustments during peak hours in buildings. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISGT.2019.8791649 | 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) |
Keywords | Field | DocType |
load forecasting,SVR,regression model,machine learning,demand response | Electrical load,Regression analysis,Computer science,HVAC,Support vector machine,Demand response,Load forecasting,Recursion,Reliability engineering | Conference |
ISSN | ISBN | Citations |
2167-9665 | 978-1-5386-8233-3 | 1 |
PageRank | References | Authors |
0.35 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashraful Haque | 1 | 1 | 0.35 |
| Manisa Pipattanasomporn | 2 | 328 | 29.79 |
| Saifur Rahman | 3 | 275 | 30.82 |
| Sri Raghavan Kothandaraman | 4 | 2 | 1.10 |
| Ahmadreza Malekpour | 5 | 2 | 1.10 |
| Esa Aleksi Paaso | 6 | 1 | 0.69 |
| Shay Bahramirad | 7 | 1 | 0.69 |