Dictionary Learning for Short-term Prediction of Solar PV Production
Abstract
Prediction of power generated from renewable energy resources such as solar photo-voltaic (PV) is a crucial task for stabilization of grids with high renewable penetration levels. Short-term prediction of these resources allow for preemptive regulation of injected power fluctuations. In this paper, a new algorithm based on dictionary learning for prediction of solar power fluctuations is introduced. This algorithm is effective on systems with structural regularities. In this method, a dictionary is trained to carry various behaviors of the system. Prediction is performed by reconstructing the tail of the upcoming signal using this dictionary. After introduction of the proposed algorithm, experimental results are provided to evaluate the prediction mechanism.
Recommended Citation
P. Shamsi et al., "Dictionary Learning for Short-term Prediction of Solar PV Production," Proceedings of the IEEE Power and Energy Society General Meeting (2015, Denver, CO), pp. 1 - 5, IEEE Computer Society, Jul 2015.
The definitive version is available at https://doi.org/10.1109/PESGM.2015.7285999
Meeting Name
IEEE Power and Energy Society General Meeting (2015: Jul. 26-30, Denver, CO)
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Energy Resources; Renewable Energy Resources; Solar Energy; Dictionary Learning; Injected Power; Penetration Level; Photovoltaics; Power Fluctuations; Prediction Mechanisms; Short Term Prediction; Structural Regularity; Forecasting
International Standard Book Number (ISBN)
978-1467380409
International Standard Serial Number (ISSN)
1944-9925
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2015 IEEE Computer Society, All rights reserved.
Publication Date
01 Jul 2015
