Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging. (December 2015)
- Record Type:
- Journal Article
- Title:
- Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging. (December 2015)
- Main Title:
- Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging
- Authors:
- Aryaputera, Aloysius W.
Yang, Dazhi
Zhao, Lu
Walsh, Wilfred M. - Abstract:
- Highlights: Very short-term irradiance forecasting on cloudy days is performed. Forecasting is done using kriging with empirical and fitted correlation matrices. Fitted correlation functions enable forecasting at unobserved locations. General stationary model includes wind information in the fitted correlation. Abstract: Several variants of spatio-temporal kriging are used to perform very short-term solar irradiance forecasting by utilizing data from a sensor network. Kriging can produce forecasts not only at the locations of the irradiance monitoring stations, but also at locations where sensors are not installed. Leave-one-out cross-validation is used to test the kriging performance at unobserved locations. Kriging weights are determined either empirically or using a correlation function. Four parametric correlation functions (correlograms) are herein considered, namely, separable, fully symmetric, and two polynomial-adjusted correlation functions. A dense 1 km × 1.2 km network of 17 stations located on Oahu island, Hawaii, is used in this paper. We find that kriging based on a polynomial-adjusted correlation function (the best among the parametric models) is able to obtain forecast skill up to 0.43 and 0.36 for observed and unobserved locations respectively, for a forecast horizon of 50 s. It is also found that empirical kriging performs better than parametric models at small forecast horizons (such as 30 s). However, it loses accuracy for forecast horizons longer thanHighlights: Very short-term irradiance forecasting on cloudy days is performed. Forecasting is done using kriging with empirical and fitted correlation matrices. Fitted correlation functions enable forecasting at unobserved locations. General stationary model includes wind information in the fitted correlation. Abstract: Several variants of spatio-temporal kriging are used to perform very short-term solar irradiance forecasting by utilizing data from a sensor network. Kriging can produce forecasts not only at the locations of the irradiance monitoring stations, but also at locations where sensors are not installed. Leave-one-out cross-validation is used to test the kriging performance at unobserved locations. Kriging weights are determined either empirically or using a correlation function. Four parametric correlation functions (correlograms) are herein considered, namely, separable, fully symmetric, and two polynomial-adjusted correlation functions. A dense 1 km × 1.2 km network of 17 stations located on Oahu island, Hawaii, is used in this paper. We find that kriging based on a polynomial-adjusted correlation function (the best among the parametric models) is able to obtain forecast skill up to 0.43 and 0.36 for observed and unobserved locations respectively, for a forecast horizon of 50 s. It is also found that empirical kriging performs better than parametric models at small forecast horizons (such as 30 s). However, it loses accuracy for forecast horizons longer than 100 s. … (more)
- Is Part Of:
- Solar energy. Volume 122(2015)
- Journal:
- Solar energy
- Issue:
- Volume 122(2015)
- Issue Display:
- Volume 122, Issue 2015 (2015)
- Year:
- 2015
- Volume:
- 122
- Issue:
- 2015
- Issue Sort Value:
- 2015-0122-2015-0000
- Page Start:
- 1266
- Page End:
- 1278
- Publication Date:
- 2015-12
- Subjects:
- Irradiance forecasting -- Spatio-temporal kriging -- Sensor network
Solar energy -- Periodicals
Solar engines -- Periodicals
621.47 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0038092X ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.solener.2015.10.023 ↗
- Languages:
- English
- ISSNs:
- 0038-092X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8327.200000
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