Method and system for predicting potential future energy consumption of built environment

A computer system for predicting a potential future energy consumption of a plurality of energy loads in a built environment, the computer system comprising:

• a memory for storing instructions;
• a data collecting device associated with a plurality of sensors coupled to the memory and configured to
• collect, via the plurality of sensors, a first set of statistical data including a location, current energy consumption data, and past energy consumption data of a plurality of energy consuming devices present in the built environment based on a plurality of factors including current, voltage, power, thermal loss, device size, power rating, and duration of use for each of the plurality of electrical devices, and
• accumulate, at the collecting device, a second set of statistical data including a measure of usage patterns of the plurality of energy consuming devices and a physical location of a plurality of users present inside the built environment and operating the plurality of energy consuming devices,
• analyze the first set of statistical data and the second set of statistical data to determine a maximum consumption potential value of the built environment and generate a plurality of statistical results of energy consumption as a function of duration of operations and temperature, wherein the analyzing includes,
• aggregating the current energy consumption data of the first set of statistical data for the plurality of energy consuming devices into pre-defined bins of time to generate aggregated current energy consumption data,
• imputing one or more missing data points in the aggregated current energy consumption data by utilizing local polynomial interpolation, and
• estimating a residual standard deviation of the aggregated current energy consumption data for each pre-defined bin of time, and
• displaying a visible output to a user indicating the potential future energy consumption of each of the plurality of energy consuming devices by adding a multiplication factor to an estimated residual standard deviation for each pre-defined bin of time, wherein the set of predictions being predicted in the real time.

A computer-readable storage medium encoding computer executable instructions that, when executed by at least one processor, performs a method for predicting a potential future energy consumption of a plurality of energy loads in a built environment, the method comprising:

• collecting, collecting devices via a plurality of via a plurality of sensors of a data collecting device, a first set of statistical data including a location, current energy consumption data, and past energy consumption data of a plurality of energy consuming devices present in the built environment based on a plurality of factors including current, voltage, power, thermal loss, device size, power rating, and duration of use for each of the plurality of electrical devices;
• accumulating, via the plurality of sensors, a second set of statistical data including a measure of usage patterns of the plurality of energy consuming devices and a physical location of a plurality of users present inside the built environment and operating the plurality of energy consuming devices;
• analyzing the first set of statistical data and the second set of statistical data, by a prediction system communicatively coupled to the collecting device, to determine a maximum consumption potential value of the built environment and generate a plurality of statistical results of energy consumption as a function of duration of operations and temperature, wherein the analyzing includes
• aggregating the current energy consumption data of the first set of statistical data for the plurality of energy consuming devices into pre-defined bins of time to generate aggregated current energy consumption data,
• imputing one or more missing data points in the aggregated current energy consumption data by utilizing local polynomial interpolation, and
• estimating a residual standard deviation of the aggregated current energy consumption data for each pre-defined bin of time;
• and
• displaying, by a prediction system, a visible output to a user indicating the potential future energy consumption of each of the plurality of energy consuming devices by adding a multiplication factor to the estimated residual standard deviation for each pre-defined bin of time.