Name
Abstract | ||
|---|---|---|
Demand response (DR) programs provide incentives for load reductions during event periods. Measuring these reductions requires estimating load if there was no event, known as baselines. Differences in metered load below the baseline are assumed to be load reductions. Baselines are typically calculated from historical data, sometimes with adjustments from pre-event periods. This paper presents a cohort-based baseline method that utilizes event-day metered load. The cohorts, a group of consumers with a similar consumption pattern, are developed from correlations between normal nonevent day loads. Baselines for DR participants are established using event-day metered load of nonparticipating cohort members. This method implicitly incorporates weather and other complex factors into the baseline. Our method is compared with several traditional methods using actual utility meter data. Baselines are calculated for each nonevent day and compared with the actual meter data on these days. The results show that cohort-based baselines are significantly more accurate than traditional methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TSG.2015.2463755 | IEEE TRANSACTIONS ON SMART GRID |
Keywords | Field | DocType |
Baseline estimation, clustering method, demand response (DR), smart grid | Econometrics,Decision tree,Load modeling,Demand response,Baseline (configuration management),Load forecasting,Metre (music),Engineering,Cohort,Linear regression | Journal |
Volume | Issue | ISSN |
7 | 5 | 1949-3053 |
Citations | PageRank | References |
2 | 0.38 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zhang | 1 | 2 | 0.38 |
| Weiwei Chen | 2 | 125 | 12.21 |
| Rui Xu | 3 | 1128 | 47.68 |
| Jason Black | 4 | 10 | 1.36 |