Name
Abstract | ||
|---|---|---|
Due to the inherent randomness of both solar power generation and residential electrical load, jointly sizing solar panel and storage capacity to meet a given quality-of-service (QoS) constraint is challenging. The challenge is greater when there is limited representative historical data. We therefore propose generating synthetic solar and load traces, corresponding to different realizations of the underlying stochastic processes. Specifically, we compare the effectiveness of three generative models: autoregressive moving-average (ARMA) models, Gaussian mixture models (GMMs), and generative adversarial networks (GANs) - as well as two direct sampling methods - for synthetic trace generation. These traces are then used for robust joint sizing by a technique described in recent work. Extensive experiments based on real data show that our approach finds robust sizing with only one year's worth of hourly trace data. Moreover, assuming that solar data are available, given a database of load traces, we demonstrate how to perform robust sizing with access to only twelve data points of load, one for each month of one year. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3307772.3328306 | E-ENERGY'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS |
Keywords | Field | DocType |
Robust sizing,Solar,Storage,Generative models | Data point,Autoregressive model,Mathematical optimization,Electrical load,Computer science,Stochastic process,Solar power,Sizing,Mixture model,Randomness | Conference |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sun Sun | 1 | 53 | 5.66 |
| Fiodar Kazhamiaka | 2 | 4 | 1.19 |
| Srinivasan Keshav | 3 | 3778 | 761.32 |
| Catherine Rosenberg | 4 | 1877 | 137.46 |