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

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, and 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 based on an adaptive neural network algorithm, and automatically self-adjust weighting factors of the adaptive neural network algorithm to minimize a measure of error between the real-time data output and a corresponding forecasted data output by the adaptive prediction engine.