Non-parametric Regression Model for Continuous-time Day Ahead Load Forecasting with Bernstein Polynomial

Conference paper

Nikjoo, R., Estebsari, A. and Nazari, M. (2019). Non-parametric Regression Model for Continuous-time Day Ahead Load Forecasting with Bernstein Polynomial. 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. 11 - 14 Jun 2019 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/eeeic.2019.8783908
AuthorsNikjoo, R., Estebsari, A. and Nazari, M.
TypeConference paper
Abstract

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.

Year2019
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Digital Object Identifier (DOI)https://doi.org/10.1109/eeeic.2019.8783908
Web address (URL)http://www.scopus.com/inward/record.url?eid=2-s2.0-85070839924&partnerID=MN8TOARS
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Online01 Aug 2019
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Deposited30 Jun 2022
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