TY - JOUR
T1 - A Model for joint probabilistic forecast of solar photovoltaic power and outdoor temperature
AU - Ramakrishna, Raksha
AU - Scaglione, Anna
AU - Vittal, Vijay
AU - Dall'Anese, Emiliano
AU - Bernstein, Andrey
N1 - Funding Information:
Manuscript received August 22, 2018; revised June 4, 2019 and November 3, 2019; accepted November 4, 2019. Date of publication November 22, 2019; date of current version December 11, 2019. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Pierre Borgnat. This work was supported in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award DE-AR0000696, in part by National Science Foundation under Grant CPS-1549923, and in part by Laboratory Directed Research and Development at the National Renewable Energy Laboratory. This paper was presented in part at the 50th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2016 [1] and in part at the IEEE International Conference on Acoustics, Speech, and Signal Processing, Calgary, AB, April 2018 [2]. (Corresponding author: Raksha Ramakrishna.) R. Ramakrishna, A. Scaglione, and V. Vittal are with the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281 USA (e-mail: raksha.ramakrishna@asu.edu; anna.scaglione@asu.edu; vijay.vittal@asu.edu).
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2019/12/15
Y1 - 2019/12/15
N2 - In this paper, a stochastic model is proposed for a joint statistical description of solar photovoltaic (PV) power and outdoor temperature. The underlying correlation emerges from solar irradiance that is responsible in part for both the variability in solar PV power and temperature. The proposed model can be used to capture the uncertainty in solar PV power and its correlation with the electric power consumption of thermostatically controlled loads. First, a model for solar PV power that explicitly incorporates the stochasticity due to clouds via a regime-switching process between the three classes of sunny, overcast and partly cloudy is proposed. Then, the relationship between temperature and solar power is postulated using a second-order Volterra model. This joint modeling is leveraged to develop a joint probabilistic forecasting method for solar PV power and temperature. Real-world datasets that include solar PV power and temperature measurements in California are analyzed and the effectiveness of the joint model in providing probabilistic forecasts is verified. The proposed forecasting methodology outperforms several reference methods thus portraying that the underlying correlation between temperature and solar PV power is well defined and only requires a simple lower-complexity sampling space.
AB - In this paper, a stochastic model is proposed for a joint statistical description of solar photovoltaic (PV) power and outdoor temperature. The underlying correlation emerges from solar irradiance that is responsible in part for both the variability in solar PV power and temperature. The proposed model can be used to capture the uncertainty in solar PV power and its correlation with the electric power consumption of thermostatically controlled loads. First, a model for solar PV power that explicitly incorporates the stochasticity due to clouds via a regime-switching process between the three classes of sunny, overcast and partly cloudy is proposed. Then, the relationship between temperature and solar power is postulated using a second-order Volterra model. This joint modeling is leveraged to develop a joint probabilistic forecasting method for solar PV power and temperature. Real-world datasets that include solar PV power and temperature measurements in California are analyzed and the effectiveness of the joint model in providing probabilistic forecasts is verified. The proposed forecasting methodology outperforms several reference methods thus portraying that the underlying correlation between temperature and solar PV power is well defined and only requires a simple lower-complexity sampling space.
KW - Dictionary learning
KW - Hidden Markov Models
KW - Probabilistic forecast solar power
KW - Roof-Top solar panels
KW - Volterra system
KW - temperature forecast
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U2 - 10.1109/TSP.2019.2954973
DO - 10.1109/TSP.2019.2954973
M3 - Article
AN - SCOPUS:85076813122
SN - 1053-587X
VL - 67
SP - 6368
EP - 6383
JO - IRE Transactions on Audio
JF - IRE Transactions on Audio
IS - 24
M1 - 8910466
ER -