# US 10,482,197 B2 · Solar irradiance modeling augmented with atmospheric water vapor data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,482,197 B2 - Document ID: US10482197B2 - Status: Granted - Kind: B2 - Application number: 15/196,519 - Filed: 2016-06-29 - Published: 2016-10-20 - Granted: 2019-11-19 - Earliest claimed: 2007-02-12 - Assignees: Locus Energy, LLC - Applicants: Locus Energy, Inc. - Inventors: Charles McBrearty; Shawn Kerrigan; Michael Herzig - Web page: https://patentgrep.com/patent/US10482197B2 ## CPC - H02J 13/1321: {using a wired telecommunication network or a data transmission bus} - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - H02J 3/381: {Dispersed generators} - H02S 50/10: Testing of PV devices, e.g. of PV modules or single PV cells (testing of semiconductor devices during manufacturing {H10P74/00}) - G06F 2111/10: Numerical modelling - H02J 2101/22: Solar energy - H02J 2101/24: Photovoltaics - Y02E 10/56: Power conversion systems, e.g. maximum power point trackers - Y02E 40/70: Smart grids as climate change mitigation technology in the energy generation sector - Y02E 60/00: Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation - Y04S 10/123: the energy generation units being or involving renewable energy sources - Y04S 40/124: using wired telecommunication networks or data transmission busses - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H02S: GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES (obtaining electrical energy from radioactive sources G21H1/12; light sensitive inorganic semiconductor devices H10F; light sensitive organic semiconductor devices H10K30/00; thermoelectric devices H10N10/00; pyroelectric devices H10N15/00) - Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION - Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS ## Abstract A computer implemented method of estimating solar irradiance, the method comprising: constructing a reference irradiance set of tuples; constructing a reference predictor set of tuples, merging the reference irradiance set of tuples and the reference predictor set of tuples by matching numerical identifiers for a location for which a global horizontal irradiance exists in the reference irradiance set of tuples and the timestamp with the numerical identifier for a particular location in the reference predictor set of tuples and the timestamp to provide a reference data set of tuples; and estimating the global horizontal irradiance for a specific location and a timestamp by minimizing the least squares error between the reference predictor set of tuples and the reference irradiance set of tuples to provide a set of estimated global horizontal irradiance values for a specific location and timestamp. ## Claims ### Claim 1 A computer implemented method of estimating solar irradiance, the method comprising: constructing, in a computing device, a reference irradiance set of tuples, the reference irradiance set of tuples each having at least a numerical identifier for a location for which a global horizontal irradiance exists, a timestamp and a measured global horizontal irradiance for a location and timestamp; constructing, in the computing device, a reference predictor set of tuples, the reference predictor set of tuples each having at least a numerical identifier for a particular location, a universal coordinated timestamp, a visible spectrum pixel intensity value from a satellite image for the particular location and a timestamp, an infrared pixel intensity value for spectral band m for the particular location and a timestamp, an elevation for a location with a numerical identifier for the particular location, snow cover for the particular location and a timestamp, atmospheric turbidity for the particular location and a timestamp, atmospheric water vapor content for the particular location and a timestamp, and a clear sky global horizontal irradiance for the particular location and a timestamp; merging, by the computing device, the reference irradiance set of tuples and the reference predictor set of tuples by matching the numerical identifiers for a location for which a global horizontal irradiance exists in the reference irradiance set of tuples and the timestamp with the numerical identifier for the particular location in the reference predictor set of tuples and the timestamp to provide a reference data set of tuples; and estimating, by the computing device, global horizontal irradiance for a specific location and a timestamp by minimizing the least squares error between the reference predictor set of tuples and the reference irradiance set of tuples to provide a set of estimated global horizontal irradiance values for the specific location and the timestamp that provides a forecast global horizontal irradiance map on a display, and wherein the set of estimated global horizontal irradiance values are used by the computing device to monitor performance of a photovoltaic system. ### Claim 2 (depends on 1) A method as in claim 1, wherein the timestamps are universal coordinated timestamps. ### Claim 3 (depends on 1) A method as in claim 1, wherein the visible spectrum pixel intensity values from the satellite image for the particular location are obtained from an internet geostationary satellite imagery feed, and wherein the infrared pixel intensity values for spectral band m for the particular location are obtained from an internet geostationary satellite imagery feed. ### Claim 4 (depends on 1) A method as in claim 1, wherein the atmospheric water vapor content is obtained by reconstruction from a raw data feed from a multispectral imager on an orbiting satellite. ### Claim 5 (depends on 1) A method as in claim 1, wherein the step of estimating global horizontal irradiance for a specific location and a timestamp is performed by optimizing parameters p1 . . . pn of a parametric function ƒ that estimates global horizontal irradiance in order to minimize the least squares error between the reference predictor set of tuples and the reference irradiance set of tuples to provide the set of estimated global horizontal irradiance values for the specific location and the timestamp. ### Claim 6 (depends on 5) A method as in claim 5, wherein the parametric function ƒ maps the reference predictor set of tuples and the reference irradiance set of tuples onto estimated global irradiance values. ### Claim 7 (depends on 1) A method as in claim 1, wherein the set of estimated global horizontal irradiance values are estimated ground level global horizontal irradiance values. ### Claim 8 (depends on 1) A method as in claim 1, wherein the set of estimated global horizontal irradiance values for the specific location and the timestamp are used by the computing device to estimate output of a photovoltaic system. ### Claim 9 (depends on 1) A method as in claim 1, wherein the set of estimated global horizontal irradiance values for the specific location and the timestamp are used by the computing device to determine a performance yield of a solar resource. ### Claim 10 (depends on 1) A method as in claim 1, wherein the set of estimated global horizontal irradiance values for the specific location and the timestamp are used by the computing device to evaluate an efficiency of a building by determining an environmental load of the building. ### Claim 11 (depends on 1) A method as in claim 1, wherein the set of estimated global horizontal irradiance values for the specific location and the timestamp are used to determine if the specific location is suitable for a photovoltaic system. ### Claim 12 (depends on 1) A method as in claim 1, wherein the set of estimated global horizontal irradiance values for the specific location and the timestamp are used to determine a degradation rate of a photovoltaic system. ### Claim 13 A computer implemented method of estimating solar irradiance, the method comprising: constructing, in a computing device, a reference irradiance set of tuples, the reference irradiance set of tuples each having at least a numerical identifier for a location for which a global horizontal irradiance exists, a timestamp and a measured global horizontal irradiance for a location and timestamp; constructing, in the computing device, a reference predictor set of tuples, the reference predictor set of tuples each having at least a numerical identifier for a particular location, a universal coordinated timestamp, a visible spectrum pixel intensity value from a satellite image for the particular location and a timestamp, an infrared pixel intensity value for spectral band m for the particular location and a timestamp, an elevation for a location with a numerical identifier for the particular location, snow cover for the particular location and a timestamp, atmospheric turbidity for the particular location and a timestamp, atmospheric water vapor content for the particular location and a timestamp, and a clear sky global horizontal irradiance for the particular location and a timestamp; merging, by the computing device, the reference irradiance set of tuples and the reference predictor set of tuples by matching the numerical identifiers for a location for which a global horizontal irradiance exists in the reference irradiance set of tuples and the timestamp with the numerical identifier for the particular location in the reference predictor set of tuples and the timestamp to provide a reference data set of tuples; and optimizing parameters p1 . . . pn of a parametric function ƒ that estimates global horizontal irradiance in order to minimize the least squares error between the reference predictor set of tuples and the reference irradiance set of tuples to provide a set of estimated global horizontal irradiance values for the specific location and the timestamp that provides a forecast global horizontal irradiance map on a display, wherein the set of estimated global horizontal irradiance values are used by the computing device to determine a performance yield of a solar resource. ### Claim 14 (depends on 13) A method as in claim 13, wherein the timestamps are universal coordinated timestamps. ### Claim 15 (depends on 13) A method as in claim 13, wherein the visible spectrum pixel intensity values from the satellite image for the particular location are obtained from an internet geostationary satellite imagery feed, and wherein the infrared pixel intensity values for spectral band m for the particular location are obtained from an internet geostationary satellite imagery feed. ### Claim 16 (depends on 13) A method as in claim 13, wherein the solar resource is a photovoltaic system. ### Claim 17 (depends on 13) A method as in claim 13, wherein the atmospheric water vapor content is obtained by reconstruction from a raw data feed from a multispectral imager on an orbiting satellite. ### Claim 18 (depends on 13) A method as in claim 13, wherein the parametric function ƒ maps the reference predictor set of tuples and the reference irradiance set of tuples onto estimated global irradiance values. ### Claim 19 (depends on 13) A method as in claim 13, wherein the set of estimated global horizontal irradiance values are estimated ground level global horizontal irradiance values. ### Claim 20 A computer implemented method of estimating solar irradiance, the method comprising: constructing, in a computing device, a reference irradiance set of tuples, the reference irradiance set of tuples each having at least a numerical identifier for a location for which a global horizontal irradiance exists, a timestamp and a measured global horizontal irradiance for a location and timestamp; constructing, in the computing device, a reference predictor set of tuples, the reference predictor set of tuples each having at least a numerical identifier for a particular location, a universal coordinated timestamp, a visible spectrum pixel intensity value from a satellite image for the particular location and a timestamp, an infrared pixel intensity value for spectral band m for the particular location and a timestamp, an elevation for a location with a numerical identifier for the particular location, snow cover for the particular location and a timestamp, atmospheric turbidity for the particular location and a timestamp, atmospheric water vapor content for the particular location and a timestamp, and a clear sky global horizontal irradiance for the particular location and a timestamp; merging, by the computing device, the reference irradiance set of tuples and the reference predictor set of tuples by matching the numerical identifiers for a location for which a global horizontal irradiance exists in the reference irradiance set of tuples and the timestamp with the numerical identifier for the particular location in the reference predictor set of tuples and the timestamp; and estimating, by the computing device, global horizontal irradiance for a specific location and a timestamp by minimizing the least squares error between the reference predictor set of tuples and the reference irradiance set of tuples to provide a set of estimated global horizontal irradiance values for the specific location and the timestamp, wherein the set of estimated global horizontal irradiance values are used by the computing device to at least one of: estimate output of a photovoltaic system; determine a performance yield of a photovoltaic system; determine a rate of degradation of a photovoltaic system; monitor performance of a photovoltaic system; evaluate efficiency of a building by determining an environmental load of the building; and determine if the specific location is suitable for a photovoltaic system.