Forecasting Residential Demand Response Potential Using Thermal-Response-Derived Targets and a Mixture of KAN Experts

Accurate day-ahead estimation of residential demand response (DR) potential is essential for load aggregators participating in electricity markets. It is also difficult to estimate because public residential datasets rarely contain observed DR event labels and household flexibility is shaped by heterogeneous, weather-sensitive consumption behavior. This paper proposes an appliance-agnostic two-stage framework for forecasting residential DR potential from aggregate hourly load and weather data. In the first stage, a thermal-response model estimates household heating and cooling sensitivities and converts thermostat-setback assumptions into synthetic DR-potential targets. Because these targets are model-derived proxies rather than measured DR events, the reported forecasting errors should be interpreted in terms of accuracy against a physically motivated synthetic target. In the second stage, the synthetic target sequence is forecast using a mixture of KAN experts (MoKE). The architecture combines Wavelet-KAN, Fourier-KAN, and RBF-KAN experts through sparse top-k routing with reversible instance normalization, allowing the model to represent local irregularities, recurrent daily/seasonal structure, and smooth nonlinear response regimes in the same forecasting layer and these forecasting characteristics are absent from traditional deep learning forecasting models. The framework is evaluated on the UMass residential dataset, which contains hourly electricity and meteorological measurements from 114 apartments collected during 2015 and 2016, using a 24 h day-ahead forecasting horizon. Across both winter and summer evaluation windows, the proposed model achieves the lowest error among all benchmark methods, outperforming TimesNet, Informer, N-HiTS, FEDformer, PatchTST, and TCN across MAE, MAPE, RMSE, and sMAPE. In particular, MoKE attains MAE values of 3.19 in winter and 3.18 in summer, demonstrating stable predictive accuracy under seasonally distinct operating conditions. These results show that heterogeneous KAN experts offer a feasible method for residential DR forecasting when appliance-level metering and observed event-level DR measurements are unavailable.