A Model for joint probabilistic forecast of solar photovoltaic power and outdoor temperature

Raksha Ramakrishna, Anna Scaglione, Vijay Vittal, Emiliano Dall'Anese, Andrey Bernstein

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number8910466
Pages (from-to)6368-6383
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume67
Issue number24
DOIs
StatePublished - Dec 15 2019

Keywords

  • Dictionary learning
  • Hidden Markov Models
  • Probabilistic forecast solar power
  • Roof-Top solar panels
  • temperature forecast
  • Volterra system

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A Model for joint probabilistic forecast of solar photovoltaic power and outdoor temperature'. Together they form a unique fingerprint.

Cite this