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 method of monitoring power systems over a wireless network protocol. The method includes receiving input waveform data containing a power event, capturing a pair of windows of the waveform data proximate to the power event, determining a hash for each of the pairs of window of waveform data, comparing each determined hash of each of the pair of windows to a library of hashes of previously encountered events, and based on the comparison of each determined hash of each pair of windows, transmitting the raw waveform of at least one of the pair windows over the wireless network protocol to an event detection system.

Abstract: A building management system including a local power system that delivers power to devices at a location, a data collector system coupled to the local power system, to monitor aggregate power used at a location, including at least one circuit based sensor configured to collect power usage data, each of the at least one sensors being clamped onto a circuit basis, and a data transmitter coupled to the at least one sensor and communicating the collected power usage data over a network to a cloud analyzer system, a tracker application configured to provide real-time reporting, alerts, and control via the network, and to be executed by a processor in an operating system (OS), in a native or virtual environment, wherein the tracker interacts with an analysis engine to map out energy use inside of a building on an individual circuit basis.

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.