Year: 2024
Type: article
Abstract: 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.
Source: Frontiers in Energy Research
Authors: Wentao Feng, Bingyan Deng, Tailong Chen, Ziwen Zhang, Yuheng Fu, Yanxi Zheng, Le Zhang, Zhiyuan Jing
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,795
OpenAlex ID: W4402808131