A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, an adaptive prediction engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for making real-time forecasts of a monitored system, comprising: a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the monitored system; a power analytics server communicatively connected to the data acquisition component, comprising, a virtual system modeling engine configured to generate predicted data output for the monitored system utilizing a virtual system model of the monitored system; an analytics engine configured to monitor the real-time data output and the predicted data output of the monitored system, and update the virtual system model based on the difference between the real-time data output and the predicted data output of the monitored system; and an adaptive prediction engine configured to forecast at least one aspect of the monitored system, the adaptive prediction engine further configured to minimize a measure of error between the real-time data output and a corresponding forecasted data output predicted; and a client terminal communicatively connected to the power analytics server, the client terminal configured to display the at least one forecasted aspect. 2. The system of claim 1, wherein the adaptive prediction engine is further configured to forecast an aspect of the monitored system using a neutral network algorithm. 3. The system of claim 2, wherein the adaptive prediction engine is further configured to optimize the neural network algorithm. 4. The system of claim 1, wherein the analytics engine is further configured to synchronize the real-time data output and the predicted data output of the monitored system. 5. The system of claim 1, wherein the analytics engine is further configured to generate a virtual model calibration request whenever the difference between the real-time data output and the predicted system data falls between a set value and an alarm condition value. 6. The system of claim 1, wherein the analytics engine is further configured to update the virtual system model by adjusting operational parameters of the virtual system model. 7. The system of claim 1, wherein the analytics engine is further configured to generate a real-time report, wherein the report comprises system summary, total and detailed system security and stability, identification of system weaknesses and insure contingency conditions. 8. The system of claim 1, wherein the monitored system is an electrical system. 9. The system of claim 8, wherein the monitored system is a mission critical electrical system. 10. The system of claim 8, wherein the at least one forecasted aspect is related to at least one from the group consisting of: system health and performance, ability of the electrical system to resist system output variations or deviations from defined tolerance limits of the monitored system; incorporation of performance and behavioral specifications for all the equipment and components of the electrical system into a real-time management environment; system reliability and availability; reliability indices as a function of different system, process and load points; implementation of different technological solutions to achieve reliability centered maintenance targets and goals; electrical system capacity levels; as-designed total power capacity of the electrical system; ability of the electrical system to main availability of its total power capacity; present utilized power capacity; electrical system strength and resilience; dynamic stability predictions across all contingency events; determination of protection system stress and withstand status; and determination of system security and stability. 11. A method for making real-time forecasts of a monitored system, comprising: receiving real-time data output from one or more sensors interfaced to the monitored system; generating predicted data output for the one or more sensors interfaced to the monitored system utilizing a virtual system model of the monitored system; updating the virtual system model of the monitored system when a difference between the real-time data output and the predicted data output exceeds a threshold; forecasting at least one aspect of the monitored system; and minimizing a measure of error between the real-time data output and a corresponding forecasted data output. 12. The method of claim 11, further comprises forecasting at least one aspect of the monitored system using a neutral network algorithm. 13. The method of claim 12, further comprises optimizing the neural network algorithm. 14. The method of claim 11, further comprising synchronizing the real-time data output and the predicted data output of the monitored system. 15. The method of claim 11, further comprising generating a virtual model calibration request whenever the difference between the real-time data output and the predicted system data falls between a set value and an alarm condition value. 16. The method of claim 11, further comprising updating the virtual system model by adjusting operational parameters of the virtual system model. 17. The method of claim 11, generating and displaying a real-time report of the monitored system, wherein the report comprises system summary, total and detailed system security and stability, identification of system weaknesses and insure contingency conditions. 18. The method of claim 11, wherein the monitored system is an electrical system. 19. The method of claim 18, wherein the monitored system is a mission critical electrical system. 20. The method of claim 18, wherein the at least one forecasted aspect is related to at least one from the group consisting of: system health and performance, ability of the electrical system to resist system output variations or deviations from defined tolerance limits of the monitored system; incorporation of performance and behavioral specifications for all the equipment and components of the electrical system into a real-time management environment; system reliability and availability; reliability indices as a function of different system, process and load points; implementation of different technological solutions to achieve reliability centered maintenance targets and goals; electrical system capacity levels; as-designed total power capacity of the electrical system; ability of the electrical system to main availability of its total power capacity; present utilized power capacity; electrical system strength and resilience; dynamic stability predictions across all contingency events; determination of protection system stress and withstand status; and determination of system security and stability.