A system comprising: at least one sensor wherein each sensor produces a load measurement indicating the amount of energy currently consumed in one of at least one regions ; means for determining independent variable values wherein the independent variables comprise; outdoor temperature, date, time of day, and region; a plurality of independent variable measurements indicating the values of the independent variables at different times; a plurality of load measurements indicating the values of the sensed load measurements at different times; a database storing the load measurements and the independent variable measurements as historical data ; a similar data selector that accepts a recent measurement and produces similar historical data from the historical data wherein the similar historical data comprises load measurements and independent variable measurements taken when the independent variable measurements were similar to the recent measurement; a baseline estimation module that accepts the historical load data set and produces at least one baseline estimate for the recent measurement; characterized by the following features: at least one drift detection rule wherein the drift detection module is a rule based drift detection module; at least one data selection rule wherein the similar data selector is a rule based similar data selector; and a graphical user interface that presents the at least one region with an indication of the difference between the load measurements and the baseline estimates for the at least one region and wherein the graphical user interface also indicates the presence of baseline drift in any of the at least one region; wherein the independent variables further comprise workday versus non-workday and building occupancy; and wherein the baseline estimation module comprises a linear regression model . at least one sensor wherein each sensor produces a load measurement indicating the amount of energy currently consumed in one of at least one regions ; means for determining independent variable values wherein the independent variables comprise; outdoor temperature, date, time of day, and region; a plurality of independent variable measurements indicating the values of the independent variables at different times; a plurality of load measurements indicating the values of the sensed load measurements at different times; outdoor temperature, date, time of day, and region; a plurality of independent variable measurements indicating the values of the independent variables at different times; a plurality of load measurements indicating the values of the sensed load measurements at different times; a database storing the load measurements and the independent variable measurements as historical data ; a similar data selector that accepts a recent measurement and produces similar historical data from the historical data wherein the similar historical data comprises load measurements and independent variable measurements taken when the independent variable measurements were similar to the recent measurement; a baseline estimation module that accepts the historical load data set and produces at least one baseline estimate for the recent measurement; at least one drift detection rule wherein the drift detection module is a rule based drift detection module; at least one data selection rule wherein the similar data selector is a rule based similar data selector; and a graphical user interface that presents the at least one region with an indication of the difference between the load measurements and the baseline estimates for the at least one region and wherein the graphical user interface also indicates the presence of baseline drift in any of the at least one region; wherein the independent variables further comprise workday versus non-workday and building occupancy; and wherein the baseline estimation module comprises a linear regression model . <EOS>