DualDRNet: A Unified Deep Learning Framework for Customer Baseline Load Estimation and Demand Response Potential Forecasting for Load Aggregators

Accurately valuing demand-side flexibility hinges on two tightly coupled tasks: reconstructing the customer baseline load (CBL) that would have occurred in the absence of demand-response (DR) events and, from that baseline, forecasting the flexibility of load that can be curtailed. Existing studies usually treat these problems in isolation and often depend on appliance-level datasets that are rarely available at scale. This paper proposes DualDRNet, a unified deep learning framework that converts one-dimensional smart-meter load data into frequency-aware 2-D tensors, then captures intra-period and inter-period dynamics through a CNN–Transformer vision backbone. CBL estimation is cast as a masked-imputation task, where DR intervals are treated as missing values and the model learns the missing patterns mimicking CBL. DR Potential forecasting is addressed with the same network, trained autoregressively for multi-horizon prediction. The model is evaluated on the Low Carbon London dataset comprising half-hourly readings from 10% households exposed to 77 peak shaving and 81 valley filling DR events. DualDRNet lowers CBL root-mean-square error (RMSE) to $0.715~kW$ , mean absolute percentage error (MAPE) to 6.9%, outperforming both averaging and deep learning based CBL estimation baselines. For aggregated DR Potential forecasting, it sustains accurate performance from five- to nine-day horizons, delivering lowest RMSE and MAPE relative to state-of-the-art temporal convolutional, hierarchical interpolation and Transformer variants. By coupling baseline reconstruction and flexibility forecasting in a single architecture with near-identical hyper-parameters, DualDRNet offers load aggregators a practical, end-to-end solution for day-ahead market bidding without the need for intrusive sub-metering. Future work will extend the approach to mixed commercial–industrial portfolios and explore parameter-efficient CNN-Transformer variants suitable for edge deployment where computational requirements are more stringent.