Abstract
In this paper, a mathematical relationship between temperature and solar irradiance is established in order to reduce the sample space and provide joint probabilistic forecasts. These forecasts can then be used for the purpose of stochastic optimization in power systems. A Volterra system type of model is derived to characterize the dependence of temperature on solar irradiance. A dataset from NOAA weather station in California is used to validate the fit of the model. Using the model, probabilistic forecasts of both temperature and irradiance are provided and the performance of the forecasting technique highlights the efficacy of the proposed approach. Results are indicative of the fact that the underlying correlation between temperature and irradiance is well captured and will therefore be useful to produce future scenarios of temperature and irradiance while approximating the underlying sample space appropriately.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3819-3823 |
| Number of pages | 5 |
| Volume | 2018-April |
| ISBN (Print) | 9781538646588 |
| DOIs | |
| State | Published - Sep 10 2018 |
| Event | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: Apr 15 2018 → Apr 20 2018 |
Other
| Other | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 |
|---|---|
| Country | Canada |
| City | Calgary |
| Period | 4/15/18 → 4/20/18 |
Fingerprint
Keywords
- Probabilistic forecasts
- Solar irradiance
- Stochastic optimization
- Temperature
- Volterra system
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Joint probabilistic forecasts of temperature and solar irradiance. / Ramakrishna, Raksha; Bernstein, Andrey; Dall'Anese, Emiliano; Scaglione, Anna.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. p. 3819-3823 8462496.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Joint probabilistic forecasts of temperature and solar irradiance
AU - Ramakrishna, Raksha
AU - Bernstein, Andrey
AU - Dall'Anese, Emiliano
AU - Scaglione, Anna
PY - 2018/9/10
Y1 - 2018/9/10
N2 - In this paper, a mathematical relationship between temperature and solar irradiance is established in order to reduce the sample space and provide joint probabilistic forecasts. These forecasts can then be used for the purpose of stochastic optimization in power systems. A Volterra system type of model is derived to characterize the dependence of temperature on solar irradiance. A dataset from NOAA weather station in California is used to validate the fit of the model. Using the model, probabilistic forecasts of both temperature and irradiance are provided and the performance of the forecasting technique highlights the efficacy of the proposed approach. Results are indicative of the fact that the underlying correlation between temperature and irradiance is well captured and will therefore be useful to produce future scenarios of temperature and irradiance while approximating the underlying sample space appropriately.
AB - In this paper, a mathematical relationship between temperature and solar irradiance is established in order to reduce the sample space and provide joint probabilistic forecasts. These forecasts can then be used for the purpose of stochastic optimization in power systems. A Volterra system type of model is derived to characterize the dependence of temperature on solar irradiance. A dataset from NOAA weather station in California is used to validate the fit of the model. Using the model, probabilistic forecasts of both temperature and irradiance are provided and the performance of the forecasting technique highlights the efficacy of the proposed approach. Results are indicative of the fact that the underlying correlation between temperature and irradiance is well captured and will therefore be useful to produce future scenarios of temperature and irradiance while approximating the underlying sample space appropriately.
KW - Probabilistic forecasts
KW - Solar irradiance
KW - Stochastic optimization
KW - Temperature
KW - Volterra system
UR - http://www.scopus.com/inward/record.url?scp=85054223122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054223122&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8462496
DO - 10.1109/ICASSP.2018.8462496
M3 - Conference contribution
AN - SCOPUS:85054223122
SN - 9781538646588
VL - 2018-April
SP - 3819
EP - 3823
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -