AFCANet: An Axis-Factorized Convolution–Attention Network for Portfolio-Level Customer Baseline Load Estimation

Abstract

In incentive-based demand response, an aggregator is paid for the gap between a customer’s metered load and the baseline load that would have occurred without a curtailment signal. This baseline is never recorded during the event, yet the settlement depends on it, so it must be reconstructed from the load observed before and after the curtailment window. At the portfolio level on which settlement is cleared, this amounts to filling a single contiguous gap, aligned with the daily peak, in an otherwise complete record. To estimate the portfolio-level customer baseline load (CBL), we propose AFCANet, which folds the one-dimensional CBL time series into a period-aligned two-dimensional tensor whose two axes describe different things. The intra-period axis traces the shape of a single daily cycle, which is locally smooth and strongly autocorrelated, while the inter-period axis links the same clock time across successive days, a longer-range and less locally smooth dependency. At the core of AFCANet is the Axis-Factorized Convolution–Attention (AFCA) block, which assigns a convolution to the intra-period axis, where its locality and weight-sharing suit the smooth daily shape, and self-attention to the inter-period axis, where its ability to link distant positions suits the cross-day dependency. Experiments use metered load from the Low Carbon London trial dataset with half-hourly resolution, evaluated under a control-group protocol in which the masked baseline is directly verifiable; AFCANet attains a MAE of 20.88 kWh, a MAPE of 1.50%, and a near-zero bias of 4.56 kWh, improving on averaging, regression, and learning-based imputation baselines. A controlled ablation that swaps the two operators confirms that the matched axis assignment is the source of the gain. Since the evaluation relies on synthetic curtailment windows in which no behavioral response is present, the reported accuracy should be read as an upper bound on the performance attainable in live demand-response events. The near-zero bias is of direct practical value to load aggregators, as a baseline free of systematic over- or under-estimation supports accurate curtailment measurement and fair financial settlement in incentive-based demand response.