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Hybrid Day-ahead Load Forecasting with Atypical Residue based Gaussian Process Regression

Published: 12 June 2018 Publication History

Abstract

The prediction accuracy of electric power consumption plays a crucial role for the efficiency of a smart grid. Hybrid approaches that jointly account for the linear and nonlinear portions of the electric load have shown promising performance because of the mixture of memory effects and random environmental perturbations. Especially for day-ahead short-term prediction, the potentially long time gap between the measurements and prediction point degrades the linear prediction performance, while the nonlinear prediction based on the weather forecast may supplement the degradation. This paper proposes a residue-based hybrid model that uses linear prediction by auto-regressive modeling and nonlinear prediction by Gaussian process regression with atypical residue of the weather forecast, particularly the difference of weather station forecasted and linear predicted local temperatures. Since the typical memory effect of the temperature can be double counted by both models, atypical residue without its linear prediction contribution is employed for the Gaussian process regression step. To verify the performance of the proposed scheme, a GIST campus electric power consumption dataset is evaluated. As expected, the linear prediction residue shows larger correlation to the atypical residue of temperature than the temperature itself. Consequently, hybrid model with the atypical residue temperature based Gaussian process regression shows improved performance in the day ahead load prediction.

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Cited By

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  • (2020)Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption PredictionIEEE Access10.1109/ACCESS.2020.30341018(196274-196286)Online publication date: 2020

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Published In

cover image ACM Conferences
e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems
June 2018
657 pages
ISBN:9781450357678
DOI:10.1145/3208903
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 12 June 2018

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Author Tags

  1. Atypical residual temperature
  2. Day-ahead load prediction
  3. Gaussian process
  4. Hybrid model
  5. Linear prediction

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Overall Acceptance Rate 160 of 446 submissions, 36%

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  • (2020)Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption PredictionIEEE Access10.1109/ACCESS.2020.30341018(196274-196286)Online publication date: 2020

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