Real-time predictive systems for intelligent energy monitoring and management of electrical power networks

摘要

A system for intelligent monitoring and management of an electrical system is 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 real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. 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 machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

主权项

1. A system for intelligent monitoring and management of an electrical system, the system comprising:
a data acquisition component communicatively connected to one or more sensors which acquire real-time data output from the electrical system, wherein the electrical system comprises a plurality of components; a power analytics server communicatively connected to the data acquisition component, wherein the power analytics sever comprises
a real-time energy pricing engine which generates real-time utility power pricing data using real-time dynamic utility power pricing data,a virtual system modeling engine which generates predicted data output for the electrical system utilizing a virtual system model of the electrical system, and generates predicted utility power pricing data using the virtual system model of the electrical system and the real-time dynamic utility power pricing data, wherein the virtual system model comprises virtual component data corresponding to the plurality of components of the electrical system and relationships between the plurality of components of the electrical system,an analytics engine configured to
monitor the real-time data output and the predicted data output of the electrical system,determine a difference between the real-time data output and the predicted data output,if the difference between the real-time data output and the predicted data output exceeds a first threshold but not a second threshold that is higher than the first threshold, initiate a calibration and synchronization operation to update the virtual system model,if the difference between the real-time data output and the predicted data output does not exceed the first threshold, not initiate the calibration and synchronization operation, and,if the difference between the real-time data output and the predicted data output exceeds the second threshold, generate an alarm;a machine learning engine configured to store and process patterns observed from the real-time data output and the predicted data output, the machine learning engine further configured to forecast an aspect of the electrical system, andan energy management system engine configured to process the real-time data output, the predicted data output, and the forecasted aspect to generate a user interface that conveys an operational state of the electrical system; and a client terminal communicatively connected to the power analytics server and configured to display the user interface.