A Thermal Performance Forecast approach is described that can be used to forecast heating and cooling fuel consumption based on changes to user preferences and building-specific parameters that include indoor temperature, building insulation, HVAC system efficiency, and internal gains. A simplified version of the Thermal Performance Forecast approach, called the Approximated Thermal Performance Forecast, provides a single equation that accepts two fundamental input parameters and four ratios that express the relationship between the existing and post-change variables for the building properties to estimate future fuel consumption. The Approximated Thermal Performance Forecast approach marginally sacrifices accuracy for a simplified forecast. In addition, the thermal conductivity, effective window area, and thermal mass of a building can be determined using different combinations of utility consumption, outdoor temperature data, indoor temperature data, internal heating gains data, and HVAC system efficiency as inputs.

An energy dispatch system, apparatus, and method are disclosed. The energy dispatch system includes a server and multiple site controllers respectively corresponding to multiple users. Each user corresponds to an energy storage device, and each energy storage device has a stage of charge. The server determines a scheduled power consumption target of each scheduling period for each user according to a total support power, multiple power consumption references of the users, and the stages of charge. The server transmits each scheduled power consumption target corresponding to a first scheduling period of the scheduling periods to the corresponding site controller. Each site controller controls the corresponding energy storage device to charge or discharge according to the corresponding actual load and the corresponding state of charge in the first scheduling period so that the power consumption of the corresponding user during the first scheduling period meets the corresponding scheduled power consumption target.

A state monitor for monitoring the state of a system and including a calculator and a memory; the system being controlled by a command defining a plurality of modes of operation of the system, each mode of operation corresponding to applying a command of constant value; the memory containing a set of stored state matrices representing, for each mode of operation of the system, the value of the projection of its state in time; and the calculator being configured, during operation of the system, to determine estimated values for the state of the system at a given instant with the state functions and of its state at an earlier instant.

A system for automatically learning and adapting to the energy usage of an equipment installed at a facility or many pieces of equipment at a plurality of facilities, where the system is provided with an initial baseline energy usage signature for the equipment, which is modified by measured energy usage and by at least one peripheral sensor measurement data to create a modified energy usage signature. The system uses artificial intelligence to learn and adapt the baseline energy usage signature to learn the business operation and account for external variables such as temperature variance and increased business flow or an interaction between devices. The smart system can identify when a piece of equipment falls outside of “normal” operation and determines what automatic action is to be taken for that piece of equipment.

A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are 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 virtual system modeling engine, an analytics engine, an adaptive prediction engine. 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 adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm.

A control system for a gas turbine includes a processor. The processor configured to access one or more operating parameters of the gas turbine. The operating parameters are configured to specify how the gas turbine operates. The processor is configured to predict a rate of degradation to one or more parts of a compressor of the gas turbine due to one or more effects on the parts by operating the gas turbine according to the one or more operating parameters. The processor is configured to send an alert to an electronic device based at least in part on the rate of degradation of the compressor.

A control system for a gas turbine includes a processor. The processor configured to access data indicative of environmental conditions of a location of the gas turbine. The processor is configured to predict an occurrence of an event associated with the gas turbine based on the environmental conditions, wherein the event comprises a change in operation of the gas turbine due to the environmental conditions. The processor is configured to send a signal indicating the occurrence of the event to an electronic device.

An environmental sensor and an environmental parameter measurement and prediction method, the environmental sensor comprising: a sensing element configured to sense an instant sensing characteristic value, so as to apply sending; an integrated circuit used for continuously obtaining the instant sensing characteristic value and an instant clock signal and obtaining via calculation an instant environmental parameter, so as to apply storing and outputting. A physical and chemical properties function corresponding to the sensing element is provided for the integrated circuit. Under a prediction model, the integrated circuit utilizes the physical and chemical properties function to conduct prediction calculation to obtain an actual environmental parameter, so as to apply storing and sending according to a current instant environmental parameter and an instant clock signal and at least one set of pre-stored instant environmental parameter and instant clock signal.

A manufacturing cell includes an allowable range setting unit configured to set an allowable range for physical quantity data or statistical processing data, and data output unit configured to output, in a case where the physical quantity data or the statistical processing data deviates from the allowable range, output information. The manufacturing cell further includes an abnormality information determination unit configured to compare the physical quantity data or the statistical processing data of a manufacturing cell as an abnormality source, with the retained physical quantity data or the retained statistical processing data of the manufacturing cell, to determine whether or not the abnormality is inherent to the manufacturing cell as the abnormality source, and a determination result notification unit for notifying a determination result.

A method of operating an exoskeleton system that includes determining a first state estimate for a current classification program being implemented by the exoskeleton system; determining a second state estimate for a reference classification program; determining that a difference between the first and second state estimate is greater than a classification program replacement threshold; generating an updated classification program; and replacing the current classification program with the updated classification program based at least in part on the determining that the difference between the first and second state estimates is greater than the classification program replacement threshold.