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Accepted (AM)Open Access (License Unspecified), Open
Conference proceeding
Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs
2021 IEEE Madrid PowerTech, pp 1-6
28 Jun 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs
- Creators
- Miltiadis Alamaniotis - The University of Texas at San AntonioAntonio Martinez-Molina - The University of Texas at San AntonioGeorgios Karagiannis - Durham University
- Publication Details
- 2021 IEEE Madrid PowerTech, pp 1-6
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Architecture, Design, and Urbanism
- Web of Science ID
- WOS:000848778000009
- Scopus ID
- 2-s2.0-85112373773
- Other Identifier
- 991021889905104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Energy & Fuels
- Engineering, Electrical & Electronic
- Green & Sustainable Science & Technology