Forecasting of global horizontal irradiance by exponential smoothing, using decompositions

Yang, D., Sharma, V., Ye, Z., Lim, L. H. I. , Zhao, L. and Aryaputera, A. W. (2015) Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy, 81, pp. 111-119. (doi: 10.1016/j.energy.2014.11.082)

Abstract

Time series methods are frequently used in solar irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. ETS (exponential smoothing) has received extensive attention in the recent years since the invention of its state space formulation. In this work, we combine these models with knowledge based heuristic time series decomposition methods to improve the forecasting accuracy and computational efficiency.<p></p> In particular, three decomposition methods are proposed. The first method implements an additive seasonal-trend decomposition as a preprocessing technique prior to ETS. This can reduce the state space thus improve the computational efficiency. The second method decomposes the GHI (global horizontal irradiance) time series into a direct component and a diffuse component. These two components are used as forecasting model inputs separately; and their corresponding results are recombined via the closure equation to obtain the GHI forecasts. In the third method, the time series of the cloud cover index is considered. ETS is applied to the cloud cover time series to obtain the cloud cover forecast thus the forecast GHI through polynomial regressions. The results show that the third method performs the best among three methods and all proposed methods outperform the persistence models.<p></p>

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Dr Li Hong Idris
• Authors: Yang, D., Sharma, V., Ye, Z., Lim, L. H. I., Zhao, L., and Aryaputera, A. W.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Energy
• Publisher: Elsevier
• ISSN: 0360-5442
• ISSN (Online): 1873-6785
• Published Online: 16 January 2015
• Copyright Holders: Copyright © 2014 Elsevier Ltd.
• First Published: First published in Energy
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.