A Chromatic-Feature-Augmented Optimization-Labelled Machine Learning Framework for Demand Response Strategy Selection in Distribution Networks

Selecting an appropriate demand response (DR) strategy for individual loads requires balancing network operating constraints, customer flexibility, and economic cost. This combinatorial decision problem becomes computationally expensive when it must be solved online for every operating condition. This paper proposes an optimization-labelled machine learning framework in which a constraint-aware Genetic Algorithm first solves the problem offline for a large set of simulated hourly operating scenarios on the IEEE 69-bus, 12.66 kV medium-voltage radial distribution test system. A Random Forest classifier is then trained to reproduce the resulting decisions almost instantaneously. In addition to standard electrical, network, economic, and customer-related descriptors, the framework introduces a chromatic feature-encoding scheme that maps each load’s active power, reactive power, and voltage stress onto Hue–Lightness–Saturation–Chroma components, analogous to combining channels in a colour image. This scheme provides the classifier with a compact, alternative representation of the load’s operating state, and a dedicated ablation study examines whether it adds predictive value beyond the raw electrical variables. The framework is validated through cross-validation, temporal (leave-future-days-out) testing, SHAP-based explainability, and a comparison against weighted-sum (PSO and MILP) and Pareto-based (NSGA-II and MOPSO) alternative optimizers. Its technical, economic, environmental, and illustrative reliability impacts are assessed after the decisions of the trained model are re-simulated on the network. The trained surrogate reproduces the optimizer’s decisions with a macro-F1 of about 0.50 while running roughly three orders of magnitude faster. The chromatic features yield a small improvement over the raw electrical feature set alone; however, the statistical significance of this improvement does not persist after correcting for the non-independence of repeated cross-validation folds and for testing multiple feature subsets against the same baseline. Overall, the results suggest that optimization-labelled surrogate learning may offer a practical route toward near-real-time, constraint-aware demand response decision support in medium-voltage distribution networks equipped with adequate metering infrastructure.