Statistical Estimation of the Residential Baseline

Demand response on the residential market is becoming a solution to adapt customer consumption to the offer available and therefore lower the electricity peak prices. Tariff incentives and direct load control of residential air-conditioners and electric heaters are flexible solutions to reduce the peak demand. To include residential demand response resources in planning operators, quantifying the demand reduction is becoming a major issue for all electrical stakeholders. Current methods are based on day or weather matching, regressions and control group approaches. In general, methods using available data from a control group give more accurate results. With the introduction of smart meters, the electric utilities generate a large amount of quality data, available almost in real time. In this paper, we suggest using these available residential load curves to select a control group based on individual load curves. One of the advantages of our method is that the selected control group could adapt at anytime to the number of individuals belonging to the demand reduction program, as this number evolves with customers entering and leaving the program. Constrained regression methods and an algorithm are developed and evaluated on real data, providing a reliable solution for an operational use.