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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| Vydáno v: | IEEE transactions on power systems Ročník 35; číslo 6; s. 4916 - 4919 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
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| ISSN: | 0885-8950, 1558-0679 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Majumdar, Angshul Chouzenoux, Emilie Elvira, Victor Sharma, Shalini |
| Author_xml | – sequence: 1 givenname: Shalini orcidid: 0000-0002-0787-2755 surname: Sharma fullname: Sharma, Shalini email: shalinis@iiitd.ac.in organization: Indraprastha Institute of Information Technology, Delhi, India – sequence: 2 givenname: Angshul orcidid: 0000-0002-1065-3000 surname: Majumdar fullname: Majumdar, Angshul email: angshul@iiitd.ac.in organization: Indraprastha Institute of Information Technology, Delhi, India – sequence: 3 givenname: Victor orcidid: 0000-0002-8967-4866 surname: Elvira fullname: Elvira, Victor email: victor.elvira@ed.ac.uk organization: School of Mathematics, University of Edinburgh, Edinburgh, United Kingdom – sequence: 4 givenname: Emilie orcidid: 0000-0003-3631-6093 surname: Chouzenoux fullname: Chouzenoux, Emilie email: emilie.chouzenoux@centralesupelec.fr organization: CVN, Inria Saclay, CentraleSupélec, Univ. Paris Saclay, Saint-Aubin, France |
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| SubjectTerms | 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 |
| Title | Blind Kalman Filtering for Short-Term Load Forecasting |
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