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Non-parametric Regression Model for Continuous-time Day Ahead Load Forecasting with Bernstein Polynomial
KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
KTH.
2019 (English)In: Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, article id 8783908Conference paper, Published paper (Refereed)
Abstract [en]

Growing perception of diverse generation resources and demand response operation of power system with high uncertainty has increased the attention to a more dynamic and accurate day-ahead load prediction. In this paper, we develop an stochastic model for short term load forecasting based on the Gaussian process, in which the non parametric estimator of the regression functions are obtained by using Bernstein polynomials. One of the major features of this model is its ability to predict a continuous load at any time of the day with a regression function. We use the historical data for training and the constrained marginal likelihood problem is optimized for finding the hyperparameters of the model. Real data sets from California ISO were used for training and testing the model. The results are compared to the day ahead piecewise constant load and the real time load. The common error measures are employed to infer the deviation of the load forecast from the real data.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. article id 8783908
Keywords [en]
Bernstein polynomials, Load Profile, Non-parametric, Regression Model
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-258157DOI: 10.1109/EEEIC.2019.8783908Scopus ID: 2-s2.0-85070839924ISBN: 9781728106526 (print)OAI: oai:DiVA.org:kth-258157DiVA, id: diva2:1356938
Conference
19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019; Genoa; Italy; 11 June 2019 through 14 June 2019
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

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Nikjoo, Roya

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf