A computer-implemented method for determining energy consumption for a residence, the method comprising: (a) acquiring a set of selected features associated with a collection of residences, wherein the set of selected features is acquired based on its potential impact the energy consumption of the collection of residences; (b) organizing the set of selected features into a first subset and a second subset, wherein the first subset of features characterize the collection of residences and includes at least one of a residence age feature, a residence location feature, a residence area metric feature, a room quantity feature, a building type feature, a cooling system feature, a water heating feature, a space heating feature, an occupancy quantity feature, an appliance quantity feature and a living situation feature and the second subset of features describe heating and/or cooling of the collection of residences during a period of time; (c) acquiring, for each residence in the collection of residences, data comprising a measured consumption value and a plurality of feature values, each feature value in the plurality of feature values corresponding to a respective feature in the set of selected features, wherein the measured consumption value for each residence corresponds to an amount of energy consumed at each residence, and comprises energy meter readings from energy meters for the collection of residences; (d) organizing the plurality of feature values into a first group and a second group, wherein each feature value in the first group corresponds to a respective feature in the first subset and each feature value in the second group corresponds to a respective feature in the second subset; (e) training a regression model based at least on the measured consumption values and the plurality of feature values for each residence in the collection of residences; (f) acquiring, for a query residence, a plurality of feature values associated with the query residence, wherein the query residence is not a residence in the collection of residences; and (g) using the regression model to determine at least one expected consumption value and at least one efficient consumption value for the query residence. (a) acquiring a set of selected features associated with a collection of residences, wherein the set of selected features is acquired based on its potential impact the energy consumption of the collection of residences; (b) organizing the set of selected features into a first subset and a second subset, wherein the first subset of features characterize the collection of residences and includes at least one of a residence age feature, a residence location feature, a residence area metric feature, a room quantity feature, a building type feature, a cooling system feature, a water heating feature, a space heating feature, an occupancy quantity feature, an appliance quantity feature and a living situation feature and the second subset of features describe heating and/or cooling of the collection of residences during a period of time; (c) acquiring, for each residence in the collection of residences, data comprising a measured consumption value and a plurality of feature values, each feature value in the plurality of feature values corresponding to a respective feature in the set of selected features, wherein the measured consumption value for each residence corresponds to an amount of energy consumed at each residence, and comprises energy meter readings from energy meters for the collection of residences; (d) organizing the plurality of feature values into a first group and a second group, wherein each feature value in the first group corresponds to a respective feature in the first subset and each feature value in the second group corresponds to a respective feature in the second subset; (e) training a regression model based at least on the measured consumption values and the plurality of feature values for each residence in the collection of residences; (f) acquiring, for a query residence, a plurality of feature values associated with the query residence, wherein the query residence is not a residence in the collection of residences; and (g) using the regression model to determine at least one expected consumption value and at least one efficient consumption value for the query residence.