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; an adaptive prediction engine configured to generate an estimated data output corresponding to the real-time data output based on a neural network algorithm, and minimize a measure of error between the real-time data output and the estimated data output by automatically self-adjusting internal weighting factors of the neural network algorithm, wherein said adjusting includes utilizing a back-propagation algorithm by continually adjusting network weights to minimize a sum-squared error function using the following formulation: E ⁡ ( w ij ) = 1 2 ⁢ ∑ p ⁢ ∑ j ⁢ ( target i p - out j ( 2 ) ) 2 , the adaptive prediction engine further configured to forecast at least one aspect of the monitored system based on the neural network algorithm; a client terminal communicatively connected to the power analytics server, the client terminal configured to display the at least one forecasted aspect; and a central analytics server configured to control an aspect of a monitored system, when its corresponding analytics server is out of operation, wherein the central analytics server is further configured to take over functionality of the analytics server and to modify operating parameters of one or more sensors that are interfaced with the monitored system.