Title

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.

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)

9781467380409

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.

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