A real-time monitoring system for energy equipment

Abstract

The present disclosure provides a real-time monitoring system for energy equipment, which includes a data recording terminal and a processing terminal. The data recording terminal collects multiple raw monitoring data during the operation of the energy equipment. The processing terminal processes the raw monitoring data to generate multiple pre-processed monitoring data and utilizes a deep neural network algorithm to process the pre-processed monitoring data to predict power consumption. Additionally, a one-dimensional convolutional automatic algorithm is employed to process the pre-processed monitoring data to determine whether the current state of the energy equipment is abnormal. If a discrepancy is detected, an alert signal is generated. The processing terminal enables a user terminal to remotely monitor and control, via a network, the raw monitoring data, the predicted power consumption, and the current status of the energy equipment to determine whether any abnormalities occur. Accordingly, the system allows real-time monitoring of the energy equipment's status and detection of any anomalies.

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