SYSTEMS AND METHODS FOR REAL-TIME FORECASTING AND PREDICTING OF ELECTRICAL PEAKS AND MANAGING THE ENERGY, HEALTH, RELIABILITY, AND PERFORMANCE OF ELECTRICAL POWER SYSTEMS BASED ON AN ARTIFICIAL ADAPTIVE NEURAL NETWORK
First Claim
1. A system for making real-time predictions about the health, reliability, and performance 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, the analytics engine further configured to initiate a calibration and synchronization operation to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold, andan adaptive prediction engine configured to forecast an aspect of the monitored system using a neural network algorithm, the adaptive prediction engine 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; and
a client terminal communicatively connected to the power analytics server, the client terminal configured to display the forecasted aspect.
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Accused Products
Abstract
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.
150 Citations
37 Claims
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1. A system for making real-time predictions about the health, reliability, and performance of a monitored system, comprising:
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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, the analytics engine further configured to initiate a calibration and synchronization operation to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold, and an adaptive prediction engine configured to forecast an aspect of the monitored system using a neural network algorithm, the adaptive prediction engine 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; and a client terminal communicatively connected to the power analytics server, the client terminal configured to display the forecasted aspect. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, comprising:
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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; calibrating 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; processing the real-time data output using a neural network algorithm; optimizing 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; and forecasting an aspect of the monitored system using the neural network algorithm. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37)
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Specification