A system and method are provided for physical data rectification using regression models. For example, the physical data may be energy infrastructure sensor data. The system may perform an estimation of sensor data during periods of data dropout using a regression model. The system may assess the accuracy of regression models through the comparison of probability distribution functions of physical data estimated using the regression model and actual physical data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for physical data rectification, the system comprising: a computer data repository configured to store a data set, the data set comprising actual physical data measured by a physical sensor; and a computing system comprising one or more computing devices, the computing system in communication with the computer data repository and programmed to implement: a historical data estimator configured to: retrieve the actual physical data from the computer data repository, wherein the actual physical data corresponds to a first time interval; determine a parameter that is correlated with the actual physical data; retrieve first measurements associated with the determined parameter and that correspond to the first time interval; generate a mapping of the retrieved first measurements to the retrieved actual physical data using machine learning; retrieve second measurements associated with the determined parameter and that correspond to a second time interval that is different than the first time interval; and estimate physical data for the second time interval using the retrieved second measurements and the generated mapping. 2. The system of claim 1, wherein the historical data estimator is further configured to: estimate second physical data for the first time interval using the retrieved first measurements and the generated mapping; compare the estimated second physical data and the retrieved actual physical data; and determine a performance benchmark associated with the physical sensor based on the comparison. 3. The system of claim 1, wherein the historical data estimator is further configured to: estimate second physical data for the first time interval using the retrieved first measurements and the generated mapping; compare the estimated second physical data and the retrieved actual physical data; determine a difference between the estimated second physical data and the retrieved actual physical data based on the comparison; and determine that a fault has occurred in response to a determination that the difference is greater than a threshold value. 4. The system of claim 3, wherein the historical data estimator is further configured to transmit an indication to a user device that the fault has occurred. 5. The system of claim 1, wherein the physical sensor is located in one of a building, an industrial process, a vehicle, a power grid, a renewable energy source, or a conventional energy source. 6. The system of claim 1, wherein the computer system is further programmed to implement a data forecaster configured to: generate a control sequence based on the estimated physical data; and transmit the control sequence to a control system such that the control system can adjust operation of the physical sensor. 7. The system of claim 6, wherein the control system is a supervisory control and data acquisition system. 8. The system of claim 1, wherein the parameter is at least one of hour of a day, day of a week, temperature, solar radiation, or relative humidity. 9. The system of claim 1, wherein the actual physical data comprises at least one of voltage, current, temperature, humidity, air flow, electric power usage, water usage, gas usage, occupancy, light, smoke, or network packets. 10. The system of claim 1, wherein the physical sensor comprises at least one of a thermostat, a humidistat, or a utility meter. 11. The system of claim 1, wherein the historical data estimator is further configured to generate the mapping using a regression model. 12. The system of claim 11, wherein the regression model comprises a support vector regression. 13. A method for rectifying physical data, the method comprising: as implemented by a computer system comprising one or more computing devices, the computer system configured with specific executable instructions, retrieving actual physical data measured by a physical sensor from a control system, wherein the actual physical data corresponds to a first time interval; determining a parameter that is correlated with the actual physical data; retrieving first measurements associated with the determined parameter and that correspond to the first time interval; generating a mapping of the retrieved first measurements to the retrieved actual physical data using machine learning; retrieving second measurements associated with the determined parameter and that correspond to a second time interval that is different than the first time interval; and estimating physical data for the second time interval using the retrieved second measurements and the generated mapping. 14. The method of claim 13, further comprising: estimating second physical data for the first time interval using the retrieved first measurements and the generated mapping; comparing the estimated second physical data and the retrieved actual physical data; and determining a performance benchmark associated with the physical sensor based on the comparison. 15. The method of claim 13, further comprising: estimating second physical data for the first time interval using the retrieved first measurements and the generated mapping; comparing the estimated second physical data and the retrieved actual physical data; determining a difference between the estimated second physical data and the retrieved actual physical data based on the comparison; and determining that a fault has occurred in response to a determination that the difference is greater than a threshold value. 16. The method of claim 15, further comprising transmitting an indication to a user device that the fault has occurred. 17. The method of claim 13, wherein the physical sensor is located in one of a building, an industrial process, a vehicle, a power grid, a renewable energy source, or a conventional energy source. 18. The method of claim 13, further comprising: generating a control sequence based on the estimated physical data; and transmitting the control sequence to a control system such that the control system can adjust operation of the physical sensor. 19. The method of claim 13, wherein generating a mapping comprises generating the mapping of the retrieved first measurements to the retrieved actual physical data using a regression model. 20. A non-transitory computer-readable medium having stored thereon a historical data estimator for using machine-learning techniques to rectify physical data, the historical data estimator comprising executable code that, when executed on a computing device, implements a process comprising: retrieving actual physical data measured by a physical sensor from a control system, wherein the actual physical data corresponds to a first time interval; determining a parameter that is correlated with the actual physical data; retrieving first measurements associated with the determined parameter and that correspond to the first time interval; generating a mapping of the retrieved first measurements to the retrieved actual physical data using machine learning; retrieving second measurements associated with the determined parameter and that correspond to a second time interval that is different than the first time interval; and estimating physical data for the second time interval using the retrieved second measurements and the generated mapping.