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ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 |
doi: 10.3389/fenrg.2024.1429241
This article is part of the Research Topic Emerging Technologies for the Construction of Renewable Energy-Dominated Power System View all 29 articles
Probabilistic Net Load Forecasting based on Sparse Variational Gaussian Process Regression
Provisionally accepted- 1 State Grid Sichuan Electric Power Company, Chengdu, China
- 2 University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
The integration of stochastic and intermittent distributed PVs brings great challenges for power system operation. Precise net load forecasting performs a critical factor in dependable operation and dispensing. An approach to probabilistic net load prediction is introduced for sparse variant Gaussian process based algorithms. The forecasting of the net load is transferred to a regression problem and solved by the sparse variational Gaussian process (SVPG) method to provide uncertainty quantification results. The proposed method can capture the uncertainties caused by the customer and PVs and provide effective inductive reasoning. The results obtained using real-world data show that the proposed method outperforms other best-of-breed algorithms.
Keywords: Net load forecasting, Power system, Gaussian process, Uncertainties, Probabilistic forecasting
Received: 07 May 2024; Accepted: 12 Jun 2024.
Copyright: © 2024 Feng, Deng, Zhang, Li, Chen, Zhang, Fu, Zheng, Jiang, Peng and Jing. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Wentao Feng, State Grid Sichuan Electric Power Company, Chengdu, China
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