# US 8,543,343 B2 · Method and apparatus for determining energy savings by using a baseline energy use model that incorporates an artificial intelligence algorithm > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,543,343 B2 - Document ID: US8543343B2 - Status: Granted - Kind: B2 - Application number: 13/017,773 - Filed: 2011-01-31 - Published: 2011-12-22 - Granted: 2013-09-24 - Earliest claimed: 2005-12-21 - Assignees: Sterling Planet, Inc. - Applicants: Melvin A. Jones; Alden M. Hathaway; Timothy J. Mattison; Kenneth B. Barclay; Paul MacGregor - Inventors: Melvin A. Jones; Alden M. Hathaway; Timothy J. Mattison; Kenneth B. Barclay; Paul MacGregor - Web page: https://patentgrep.com/patent/US8543343B2 ## CPC - G06F 30/13: Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G06F 2111/08: Probabilistic or stochastic CAD - G06F 2119/06: Power analysis or power optimisation - G06F 2119/08: Thermal analysis or thermal optimisation - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A computer-based system, computer-implemented method, and computer program product facilitate determining energy cost savings in an energy-consuming facility, such as a commercial building, using an artificial intelligence model, for example a neural network model, that projects or estimates the amount of energy that would have been consumed by the facility but for the implementation of energy efficiency or conservation measures. Energy savings are represented by the difference between the estimate of energy that would have been consumed but for the measures and the actual amount of energy consumed by the facility under actual conditions during a time interval after the measures have been implemented. ## Claims ### Claim 1 A computer-implemented method for determining energy savings in an energy-consuming facility, comprising: inputting baseline facility condition data to an artificial intelligence model generator, the baseline facility condition data representing baseline facility conditions during a first time interval before an energy conservation measure; inputting baseline energy consumed by the facility during the first time interval; the artificial intelligence model generator generating an artificial intelligence model in response to the baseline facility condition data and baseline energy consumed, the artificial intelligence model modeling how facility energy consumption responds to facility conditions; inputting actual facility condition data to the artificial intelligence model, the actual facility condition data representing actual facility conditions during a second time interval after the energy conservation measure; inputting actual energy consumed by the facility during the second time interval; the artificial intelligence model outputting, in response to the actual facility condition data, a projection of energy that would have been consumed under the baseline facility conditions but for the energy conservation measure; computing energy savings, wherein the energy savings are defined by a difference between the actual energy consumed during the second time interval and the estimate of energy that would have been consumed but for the energy conservation measure; and generating particulate reduction values (PRVs) corresponding to the computed energy savings. ### Claim 2 (depends on 1) The method claimed in claim 1, wherein the step of inputting baseline facility condition data comprises querying a weather agency database in response to a geographic location of the facility. ### Claim 3 (depends on 1) The method claimed in claim 1, wherein: the step of inputting baseline facility condition data comprises inputting baseline occupancy and production data and weather conditions experienced during the first time interval; and the step of inputting actual facility condition data comprises inputting actual occupancy and production data and weather conditions experienced during the second time interval. ### Claim 4 (depends on 1) The method claimed in claim 1, wherein: the step of inputting baseline facility condition data comprises inputting baseline data selected from the group consisting of occupancy, production data, number of shifts, hours of operation, sales units, and sales revenues; and the step of inputting actual facility condition data comprises inputting actual data selected from the group consisting of occupancy, production data, number of shifts, hours of operation, sales units, and sales revenues. ### Claim 5 (depends on 1) The method claimed in claim 1, wherein the step of generating PRVs comprises querying a database storing particulate factors by geographic region and applying to the computed energy savings the corresponding particulate factor for the geographic region where the facility is located. ### Claim 6 (depends on 1) The method claimed in claim 1, further comprising generating energy efficiency certificates (EECs) corresponding to the computed energy savings, wherein the PRVs correspond to the EECs. ### Claim 7 (depends on 1) A computer program product for determining energy savings in an energy-consuming facility, comprising a non-transitory computer-readable medium encoded with computer-executable instructions for performing the method of claim 1. ### Claim 8 A system for determining energy savings in an energy-consuming facility, comprising: a database for storing facility condition data representing facility conditions during a first time interval before energy conservation measures and during a second time interval after energy conservation measures, the database further for storing energy data representing energy consumed by the facility during each time interval; an artificial intelligence model generator for generating an artificial intelligence model modeling how facility energy consumption responds to facility conditions, wherein the artificial intelligence model generator generates the artificial intelligence model in response to baseline facility condition data representing facility conditions during the first time interval and in response to baseline energy consumed by the facility during the first time interval; an artificial intelligence engine for producing an estimate of energy that would have been consumed under the baseline facility conditions but for the energy conservation measures, the artificial intelligence engine producing the estimate in response to actual facility condition data representing actual facility conditions during the second time interval; and a user interface for outputting particulate reduction values (PRVs) corresponding to energy savings, wherein the energy savings are a difference between the actual energy consumed during the second time interval and the estimate of energy that would have been consumed but for the energy conservation measures. ### Claim 9 (depends on 8) The system claimed in claim 8, wherein the facility condition data comprises occupancy, production data, and weather conditions experienced by the facility during each time interval. ### Claim 10 (depends on 8) The system claimed in claim 8, wherein the facility condition data is selected from the group consisting of occupancy, production data, number of shifts, hours of operation, sales units, and sales revenues. ### Claim 11 (depends on 8) The system claimed in claim 8, wherein the database further stores particulate factors by geographic region for applying, to the computed energy savings, the corresponding particulate factor for the geographic region where the facility is located to determine the PRVs for outputting by the user interface. ### Claim 12 (depends on 8) The system claimed in claim 8, wherein the wherein the PRVs correspond to energy efficiency certificates (EECs) that also correspond to the computed energy savings. ### Claim 13 A computer-implemented method for determining energy savings in an energy-consuming facility, comprising: inputting baseline facility condition data to an artificial intelligence model generator, the baseline facility condition data representing baseline facility conditions during a first time interval before an energy conservation measure; inputting baseline energy consumed by the facility on a less-than-monthly basis during the first time interval; the artificial intelligence model generator generating an artificial intelligence model in response to the baseline facility condition data and baseline energy consumed, the artificial intelligence model modeling how facility energy consumption responds to facility conditions; inputting actual facility condition data to the artificial intelligence model, the actual facility condition data representing actual facility conditions during a second time interval after the energy conservation measure; inputting actual energy consumed by the facility on a less-than-monthly basis during the second time interval; the artificial intelligence model outputting, in response to the actual facility condition data, a projection of energy that would have been consumed on a less-than-monthly basis under the baseline facility conditions but for the energy conservation measure; computing energy savings on a less-than-monthly basis, wherein the energy savings are defined by a difference between the actual energy consumed on a less-than-monthly basis during the second time interval and the estimate of energy that would have been consumed on a less-than-monthly basis but for the energy conservation measure; and generating energy efficiency certificates (EECs) corresponding to the computed energy savings, particulate reduction values (PRVs) corresponding to the energy savings, or both, on a less-than-monthly basis. ### Claim 14 (depends on 13) The method claimed in claim 13, wherein the step of inputting baseline facility condition data comprises querying a weather agency database in response to a geographic location of the facility. ### Claim 15 (depends on 13) The method claimed in claim 13, wherein: the step of inputting baseline facility condition data further comprises inputting baseline occupancy and production data and weather conditions experienced during the first time interval; and the step of inputting actual facility condition data further comprises inputting actual occupancy and production data and weather conditions experienced during the second time interval. ### Claim 16 (depends on 13) The method claimed in claim 13, wherein: the step of inputting baseline facility condition data further comprises inputting baseline data selected from the group consisting of occupancy, production data, number of shifts, hours of operation, sales units, and sales revenues; and the step of inputting actual facility condition data further comprises inputting actual data selected from the group consisting of occupancy, production data, number of shifts, hours of operation, sales units, and sales revenues. ### Claim 17 (depends on 13) The method claimed in claim 13, wherein the step of computing energy savings comprises computing energy savings on an hour-by-hour basis, wherein the energy savings are defined by a difference between the actual energy consumed on an hour-by-hour basis during the second time interval and the estimate of energy that would have been consumed on an hour-by-hour basis but for the energy conservation measure. ### Claim 18 (depends on 13) The method claimed in claim 13, wherein the step of generating EECs, PRVs, or both further comprises querying a database storing increased-demand times for the energy supplier and designating the EECs, PRVs, or both corresponding to the increased-demand times as increased-demand EECs, PRVs, or both. ### Claim 19 (depends on 13) A computer program product for determining energy savings in an energy-consuming facility, comprising a non-transitory computer-readable medium encoded with computer-executable instructions for performing the method of claim 13. ### Claim 20 A system for determining energy savings in an energy-consuming facility, comprising: a database for storing facility condition data representing facility conditions during a first time interval before energy conservation measures and during a second time interval after energy conservation measures, the database further for storing energy data representing energy consumed by the facility on a less-than-monthly basis during each time interval; an artificial intelligence model generator for generating an artificial intelligence model modeling how facility energy consumption responds to facility conditions, wherein the artificial intelligence model generator generates the artificial intelligence model in response to baseline facility condition data representing facility conditions during the first time interval and in response to baseline energy consumed by the facility on a less-than-monthly basis during the first time interval; an artificial intelligence engine for producing an estimate of energy that would have been consumed on a less-than-monthly basis under the baseline facility conditions but for the energy conservation measures, the artificial intelligence engine producing the estimate in response to actual facility condition data representing actual facility conditions during the second time interval; and a user interface for outputting energy efficiency certificates (EECs) corresponding to the energy savings, particulate reduction values (PRVs) corresponding to the energy savings, or both, on a less-than-monthly basis, wherein the less-than-monthly basis energy savings are a difference between the actual energy consumed on a less-than-monthly basis during the second time interval and the estimate of energy that would have been consumed on a less-than-monthly basis but for the energy conservation measures. ### Claim 21 (depends on 20) The system claimed in claim 20, wherein the facility condition data comprises occupancy, production data, and weather conditions experienced by the facility during each time interval. ### Claim 22 (depends on 20) The system claimed in claim 20, wherein the facility condition data is selected from the group consisting of occupancy, production data, number of shifts, hours of operation, sales units, and sales revenues. ### Claim 23 (depends on 20) The system claimed in claim 20, wherein: the artificial intelligence engine produces an estimate of energy that would have been consumed on an hour-by-hour basis under the baseline facility conditions but for the energy conservation measures; and the user interface outputs EECs corresponding to the energy savings, PRVs corresponding to the energy savings, or both, on an hour-by-hour basis. ### Claim 24 (depends on 20) The system claimed in claim 20, wherein the database further stores increased-demand times per supplier of energy to the facility for use in designating the EECs, PRVs, or both corresponding to the increased-demand times as increased-demand EECs, PRVs, or both.