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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
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
Systems and methods for making real-time predictions about the health, reliability, and performance of a monitored system are disclosed. A data acquisition component acquires real-time data output from the monitored system. A power analytics server comprises a virtual system modeling engine, an analytics engine, and an adaptive prediction engine. The virtual system modeling engine is operable to generate predicted data output for the monitored system utilizing a virtual system model of the monitored system. An analytics engine is operable to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold. The adaptive prediction engine is operable to forecast an aspect of the monitored system based on an adaptive neural network algorithm and automatically minimize a measure of error between the real-time data output and a corresponding forecasted data output by the adaptive prediction engine.
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