Electrical power networks are heavily monitored systems, requiring operators to perform intricate information synthesis before understanding the underlying network state. Our study aims at helping this synthesis step by automatically creating features from the sensor data. We propose a supervised feature extraction approach using a grammar-guided evolution, which outputs interpretable and dimensionally consistent features. Operations restrictions on dimensions are introduced in the learning process through context-free grammars. They ensure coherence with physical laws, dimensional-consistency, and also introduce technical expertise in the created features. We compare our approach to other state-of-theart feature extraction methods on a real dataset taken from the French electrical network sensors.

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In recent years, symbolic regression has been of wide interest to provide an interpretable symbolic representation of potentially large data relationships. Initially circled to genetic algorithms, symbolic regression methods now include a variety of Deep Learning based alternatives. However, these methods still do not generalize well to real-world data, mainly because they hardly include domain knowledge nor consider physical relationships between variables such as known equations and units. Regarding these issues, we propose a Reinforcement-Based Grammar-Guided Symbolic Regression (RBG2-SR) method that constrains the representational space with domain-knowledge using context-free grammar as reinforcement action space. We detail a Partially-Observable Markov Decision Process (POMDP) modeling of the problem and benchmark our approach against state-of-the-art methods. We also analyze the POMDP state definition and propose a physical equation search use case on which we compare our approach to grammar-based and non-grammarbased symbolic regression methods. The experiment results show that our method is competitive against other state-of-the-art methods on the benchmarks and offers the best error-complexity trade-off, highlighting the interest of using a grammar-based method in a real-world scenario.