Very short term irradiance forecasting using the lasso

Yang, D., Ye, Z., Lim, L. H. I. and Dong, Z. (2015) Very short term irradiance forecasting using the lasso. Solar Energy, 114, pp. 314-326. (doi:10.1016/j.solener.2015.01.016)

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Abstract

We find an application of the lasso (least absolute shrinkage and selection operator) in sub-5-min solar irradiance forecasting using a monitoring network. Lasso is a variable shrinkage and selection method for linear regression. In addition to the sum of squares error minimization, it considers the sum of ℓ1-norms of the regression coefficients as penalty. This bias–variance trade-off very often leads to better predictions.

One second irradiance time series data are collected using a dense monitoring network in Oahu, Hawaii. As clouds propagate over the network, highly correlated lagged time series can be observed among station pairs. Lasso is used to automatically shrink and select the most appropriate lagged time series for regression. Since only lagged time series are used as predictors, the regression provides true out-of-sample forecasts. It is found that the proposed model outperforms univariate time series models and ordinary least squares regression significantly, especially when training data are few and predictors are many. Very short-term irradiance forecasting is useful in managing the variability within a central PV power plant.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lim, Dr Li Hong Idris
Authors: Yang, D., Ye, Z., Lim, L. H. I., and Dong, Z.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:Solar Energy
Publisher:Elsevier Ltd.
ISSN:0038-092X
ISSN (Online):0038-092X
Copyright Holders:Copyright © 2015 Elsevier, Ltd.
First Published:First published in Solar Energy 114:314-326
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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