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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.19612 |
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| _version_ | 1866914008865439744 |
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| author | Dorji, Sonam Sun, Yongkang Zhang, Yuchen Nourbakhsh, Ghavameddin Mishra, Yateendra Xu, Yan |
| author_facet | Dorji, Sonam Sun, Yongkang Zhang, Yuchen Nourbakhsh, Ghavameddin Mishra, Yateendra Xu, Yan |
| contents | With increasing penetration of distributed energy resources installed behind the meter, there is a growing need for adequate modelling of composite loads to enable accurate power system simulation analysis. Existing measurement based load modeling methods either fit fixed-structure physical models, which limits adaptability to evolving load mixes, or employ flexible machine learning methods which are however black boxes and offer limited interpretability. This paper presents a new learning based load modelling method based on Kolmogorov Arnold Networks towards modelling flexibility and interpretability. By actively learning activation functions on edges, KANs automatically derive free form symbolic equations that capture nonlinear relationships among measured variables without prior assumptions about load structure. Case studies demonstrate that the proposed approach outperforms other methods in both accuracy and generalization ability, while uniquely representing composite loads into transparent, interpretable mathematical equations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19612 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Symbolic Equation Modeling of Composite Loads: A Kolmogorov-Arnold Network based Learning Approach Dorji, Sonam Sun, Yongkang Zhang, Yuchen Nourbakhsh, Ghavameddin Mishra, Yateendra Xu, Yan Systems and Control With increasing penetration of distributed energy resources installed behind the meter, there is a growing need for adequate modelling of composite loads to enable accurate power system simulation analysis. Existing measurement based load modeling methods either fit fixed-structure physical models, which limits adaptability to evolving load mixes, or employ flexible machine learning methods which are however black boxes and offer limited interpretability. This paper presents a new learning based load modelling method based on Kolmogorov Arnold Networks towards modelling flexibility and interpretability. By actively learning activation functions on edges, KANs automatically derive free form symbolic equations that capture nonlinear relationships among measured variables without prior assumptions about load structure. Case studies demonstrate that the proposed approach outperforms other methods in both accuracy and generalization ability, while uniquely representing composite loads into transparent, interpretable mathematical equations. |
| title | Symbolic Equation Modeling of Composite Loads: A Kolmogorov-Arnold Network based Learning Approach |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.19612 |