On a Fast and Explainable REC HEMS Based on Kolmogorov-Arnold Networks

The increasing demand for reliable AI models is also evident in Hierarchical Energy Management Systems (HEMSs). Especially in complex energy systems like Renewable Energy Communities (RECs), efficient cost minimization is a strong incentive for potential users to participate. Nevertheless, trust in AI stems not only from its performance but also from AI Explainability (XAI). In this regard, the new Kolmogorov-Arnold Network (KAN) XAI paradigm offers significant advantages over Multi-Layer Perceptrons (MLPs). In this work, a KAN is optimized by a Genetic Algorithm (GA) to serve as an inference engine in a realistic REC HEMS. The main goal is to validate KANs in that application domain. Secondly, a custom coefficient space quantization is proposed to enable efficient KAN-GA encoding. Specifically, the generic KAN model is encoded as a GA individual and optimized to minimize the operational cost of the REC. Then, an explainable AI model is extracted from the original KAN by fitting its connection splines with simpler function forms and by applying the Kolmogorov-Arnold Theorem to get the output. The results show a high precision of the KAN models with a low computational cost. In addition, the explainable model performance is very close to that of the original KAN in terms of precision, while being significantly better in terms of computational efficiency, with about 10% time-saving. Therefore, developing fast and explainable KAN-based models is worthwhile in that application field.