Alamaniotis, Miltiadis and Martinez-Molina, Antonio and Karagiannis, Georgios (2021) 'Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs.', 2021 IEEE Madrid PowerTech Madrid, Spain / Virtual, 28 Jun - 02 Jul 2021.
One of the pillars in developing smart power systems is the use of load forecasting methods. In particular load forecasting accommodates decision making pertained to the operation of power market. In this paper, a new method for real-time updating very short-term load forecasting is proposed. The goal of the method is to accurately predict the load demand value in the next 5 minutes and accordingly update the daily forecast. To that end, the proposed method implements an ensemble of homogeneous learning Gaussian processes which are trained on slightly different training datasets. The predicted values are then fused using a fuzzy inference system in order to obtain a single value which is used to correct the precomputed forecast. The proposed method is tested on a set of real-world data taken from a major US area and is benchmarked against the naïve forecasting method. Results highlight the superiority of our method against the benchmarked method exhibiting an increase in forecasted accuracy by 50% in most cases.
|Item Type:||Conference item (Paper)|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1109/PowerTech46648.2021.9494757|
|Publisher statement:||© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Date accepted:||02 July 2021|
|Date deposited:||12 August 2021|
|Date of first online publication:||29 July 2021|
|Date first made open access:||12 August 2021|
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