Abstract: A data acquisition module including a connection panel subassembly having a housing, a computing device subassembly, electrically coupled to the connection panel and having a housing configured to mechanically couple to the housing of the connection panel assembly; and a user interface subassembly, electrically coupled to the computing device subassembly and having a housing configured to mechanically couple to the housing of the computing device assembly.

Abstract: A current transformer having a body having an upper half and a lower half hingedly connected to the upper half, a pair of ferrite cores located within one of the upper half and the lower half of the body, the pair of ferrite cores defining a gap formed between each ferrite core of the pair of ferrite cores, and a sensor located within the gap formed between each ferrite core of the pair of ferrite cores.

Abstract: A method of managing energy by use of processing logic that comprises a load processor as a cloud service is provided. The method includes receiving power load information from a data collection system located at a building and using a cloud analysis layer that employs machine-learning and artificial intelligence for optimization control, analyzing the received power load information to disaggregate load waveform signals and identify device-based power loads by use of a neural network to perform historical device demand and performance analysis to generate device-based demand forecasting, generating demand forecasts for the building to mitigate peak demand based on analysis of a power draw signal and the generated device-based demand forecasting, and determining whether the generated demand forecast for the building is to peak in a near future, based on threshold values of at least one of generated device-based demand forecasting, power price or cost information, and user behavior analysis.

Abstract: A system and system for motor fault detection are provided. The system includes a data collection sensor electrically coupled to a motor, and a processor functioning as a Motor Current Signature analyzer (MCSA) detecting performance conditions of the motor based on a measured power draw current data captured by the sensor and providing condition information indicative of a fault in the motor based on the detected performance conditions. The method includes measuring, by a data collection sensor, power draw current data from the motor, detecting, by a processor functioning as a Motor Current Signature analyzer (MCSA), performance conditions of the motor based on the measured power draw current data, and providing condition information indicative of a fault in the motor based on the detected performance conditions to a user.

Abstract: A method of facilitating configuration of at least one sensor with a data transmitter communicatively coupled to an analysis engine for a building management system, wherein the application is configured to be run on a mobile device and interact with the at least one sensor during the installation. The method includes communicating, by the application, with the at least one sensor to display a visual indicator, capturing, by a camera of the mobile device, visual data representative of the visual indicator, and associating the at least one sensor with a circuit of a building power system based on the captured visual data.

Abstract: Waveform analysis is performed to identify and characterize power-consuming devices operating on a building electrical circuit. Current waveforms are measured from the building circuit with electrical devices operating thereon. The waveforms are separated into wavelets and analyzed to identify a representative wavelet model which is transmitted to a server for analysis. The server compares the representative wavelet model to a predictive model built from waveform signatures of known electrical devices operating on a circuit. When the predictive model matches the representative wavelet model, the electrical devices contributing to the representative wavelet, their operating mode(s) (e.g., “on”, “off”, “paused”, “hibernating”) and/or their performance state(s) (e.g., normal operation, deterioration, or failure modes) can be identified. This information can be communicated as feedback to the consumer to facilitate more efficient and more cost-effective energy usage.

Abstract: Waveform analysis is performed to identify and characterize power-consuming devices operating on a building electrical circuit. Current waveforms are measured from the building circuit with electrical devices operating thereon. The waveforms are separated into wavelets and analyzed to identify a representative wavelet model which is transmitted to a server for analysis. The server compares the representative wavelet model to a predictive model built from waveform signatures of known electrical devices operating on a circuit. When the predictive model matches the representative wavelet model, the electrical devices contributing to the representative wavelet, their operating mode(s) (e.g., “on”, “off”, “paused”, “hibernating”) and/or their performance state(s) (e.g., normal operation, deterioration, or failure modes) can be identified. This information can be communicated as feedback to the consumer to facilitate more efficient and more cost-effective energy usage.