# US 2018/0323994 A1 · Intelligent equipment sequencing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0323994 A1 - Document ID: US20180323994A1 - Status: Published application - Kind: A1 - Application number: 15/942,306 - Filed: 2018-03-30 - Published: 2018-11-08 - Earliest claimed: 2014-12-24 - Assignees: Optimum Energy, LLC - Applicants: Optimum Energy LLC - Inventors: Thomas Jones; Ian Dempster; Clark Matthys; Peng Chen - Web page: https://patentgrep.com/patent/US20180323994A1 ## CPC - H04L 12/2816: {Controlling appliance services of a home automation network by calling their functionalities (arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station; in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom H04Q9/00)} - F24F 11/30: for purposes related to the operation of the system, e.g. for safety or monitoring - F24F 11/46: Improving electric energy efficiency or saving - F24F 11/62: characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values - F24F 11/64: using pre-stored data - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - F24F: AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING (removing dirt or fumes from areas where they are produced B08B15/00; vertical ducts for carrying away waste gases from buildings E04F17/02; tops for chimneys or ventilating shafts, terminals for flues F23L17/02) ## Abstract Systems and methods for sequencing HVAC equipment of an HVAC system using data recorded in situ to build a model capable of making predictions about equipment efficiency and using that information, in combination with predictions about building load, to produce an operational sequence for the HVAC equipment that promotes an improved or optimized overall energy efficiency for the HVAC system. In one embodiment, the process is automated and utilizes Bayesian computational models or algorithms to generate an initial sequence. The process reduces engineering hours and may advantageously provide a means to predict potential sequencing problems for similar types of HVAC equipment. ## Claims ### Claim 1 A method for sequencing HVAC equipment in an HVAC system, the method comprising: acquiring a plurality of inputs including historical data points and predicted data points; determining energy efficiencies for the HVAC equipment; acquiring weather prediction data; using the inputs, energy efficiencies and weather prediction data, computing an expected future load for the HVAC equipment; using the inputs, energy efficiencies, weather prediction data and expected future load, employing an analytical computational model to determine predicted energy efficiencies for the HVAC equipment; determining an initial order of the HVAC equipment configured to provide an optimized energy efficiency for the HVAC system; filtering the initial order based on an operating status for one or more of the HVAC equipment; determining a final sequence of the HVAC equipment; and providing the final sequence to a building automation system for controlling the HVAC system in accordance with the final sequence. ### Claim 2 (depends on 1) The method of claim 1, wherein determining the energy efficiencies includes determining real-time energy efficiencies. ### Claim 3 (depends on 1) The method of claim 1, wherein determining the energy efficiencies includes determining historical energy efficiencies. ### Claim 4 (depends on 1) The method of claim 1, wherein acquiring the weather prediction data includes obtaining the weather prediction data from a third party source. ### Claim 5 (depends on 1) The method of claim 1, wherein the analytical computational model is a Bayesian based model using a linear regression analysis. ### Claim 6 (depends on 1) The method of claim 1, wherein the analytical computational model is a Bayesian based model using a k-nearest neighbors analysis. ### Claim 7 A method for sequencing HVAC equipment in an HVAC system, the method comprising: acquiring a plurality of inputs including historical data points and predicted data points; determining energy efficiencies for the HVAC equipment; acquiring weather prediction data; using the inputs, energy efficiencies and weather prediction data, computing an expected future load for the HVAC equipment; using the inputs, energy efficiencies, weather prediction data and expected future load, employing a linear regression model to determine predicted energy efficiencies for the HVAC equipment; determining an initial order of the HVAC equipment configured to provide an optimized energy efficiency for the HVAC system; filtering the initial order based on an operating status for one or more of the HVAC equipment; determining a final sequence of the HVAC equipment; and providing the final sequence to a building automation system for controlling the HVAC system in accordance with the final sequence. ### Claim 8 (depends on 7) The method of claim 7, wherein determining the energy efficiencies includes determining real-time energy efficiencies. ### Claim 9 (depends on 7) The method of claim 7, wherein determining the energy efficiencies includes determining historical energy efficiencies. ### Claim 10 (depends on 7) The method of claim 7, wherein acquiring the weather prediction data includes obtaining the weather prediction data from a third party source. ### Claim 11 A method for sequencing HVAC equipment in an HVAC system, the method comprising: acquiring a plurality of inputs including historical data points and predicted data points; determining energy efficiencies for the HVAC equipment; acquiring weather prediction data; using the inputs, energy efficiencies and weather prediction data, computing an expected future load for the HVAC equipment; using the inputs, energy efficiencies, weather prediction data and expected future load, employing a k-nearest neighbors model to determine predicted energy efficiencies for the HVAC equipment; determining an initial order of the HVAC equipment configured to provide an optimized energy efficiency for the HVAC system; filtering the initial order based on an operating status for one or more of the HVAC equipment; determining a final sequence of the HVAC equipment; and providing the final sequence to a building automation system for controlling the HVAC system in accordance with the final sequence. ### Claim 12 (depends on 11) The method of claim 11, wherein determining the energy efficiencies includes determining real-time energy efficiencies. ### Claim 13 (depends on 11) The method of claim 11, wherein determining the energy efficiencies includes determining historical energy efficiencies. ### Claim 14 (depends on 11) The method of claim 11, wherein acquiring the weather prediction data includes obtaining the weather prediction data from a third party source.