Blind Kalman Filtering for Short-Term Load Forecasting

In this work we address the problem of short-term load forecasting. We propose a generalization of the linear state-space model where the evolution of the state and the observation matrices is unknown. The proposed blind Kalman filter algorithm proceeds via alternating the estimation of these unknow...

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Veröffentlicht in:IEEE transactions on power systems Jg. 35; H. 6; S. 4916 - 4919
Hauptverfasser: Sharma, Shalini, Majumdar, Angshul, Elvira, Victor, Chouzenoux, Emilie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
Algorithms
Batch processing
Computer Science
Electric power
Engineering Sciences
expectation-minimization algorithm
Filtering algorithms
Forecasting
Kalman filtering
Kalman filters
Load forecasting
Load modeling
Machine Learning
Peak load
Signal and Image processing
State space models
State-space methods
state-space model
state-space model
Kalman filtering
expectation-minimization algorithm
load forecasting
ISSN:0885-8950, 1558-0679
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Beschreibung
Zusammenfassung:In this work we address the problem of short-term load forecasting. We propose a generalization of the linear state-space model where the evolution of the state and the observation matrices is unknown. The proposed blind Kalman filter algorithm proceeds via alternating the estimation of these unknown matrices and the inference of the state, within the framework of expectation-maximization. A mini-batch processing strategy is introduced to allow on-the-fly forecasting. The experimental results show that the proposed method outperforms the state-of-the-art techniques by a considerable margin, both on load profile estimation and peak load forecast problems.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2020.3018623