EnerNOC is among the largest providers of energy intelligence software and services for commercial, institutional, and industrial customers, as well as electric power grid operators and utilities. It chiefly provides demand response services that maintain real-time balance between electricity supply and demand. Its energy management services provide solutions for energy conservation and efficiency, in addition to consulting services for energy supply management. These services may also reduce greenhouse gas emissions in many cases, also providing the opportunity for its clients to earn renewable energy credits. Wikipedia.

An apparatus for estimating a buildings energy consumption, including thermal response processor and a regression engine. The thermal response processor generates energy use data sets, each having energy consumption values along with corresponding time and outside temperature values. The consumption values within each of the data sets are shifted by one of a plurality of lag values relative to the time and temperature values, where each of the plurality of lag values is different from other lag values. The thermal response processor performs a regression analysis on each of the energy use data sets to yield corresponding regression model parameters and a corresponding residual. The thermal response processor determines a least valued residual from all residuals yielded by the regression engine, the least valued residual indicating an energy lag for the building, and regression model parameters that correspond to the least valued residual are employed to estimate the energy consumption.

A method for dispatching buildings participating in a demand response program including generating data sets for each of the buildings, each having energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each set are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each lag value; performing a regression analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued indicating a corresponding energy lag for each of the buildings; and using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict a dispatch order reception time for a demand response program event.

An apparatus including devices, a network operations center (NOC), and control nodes. Each of the devices consumes a portion of a resource when turned on, and performs a corresponding function within an acceptable operational margin by cycling on and off. The NOC is disposed external to a facility, and determines an energy lag for the facility based upon fine-grained energy consumption baseline data. The NOC employs the energy lag to generate a plurality of run time schedules that coordinates run times for the each of the devices to control the peak demand of the resource. Each of the plurality of control nodes is coupled to a corresponding one of the plurality of devices. The plurality of control nodes transmits sensor data and device status to the NOC via the demand coordination network for generation of the plurality of run time schedules, and executes selected ones of the run time schedules to cycle the plurality of devices on and off.

A method for characterizing buildings, including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each of the plurality of lag values is different from other ones of the plurality of lag values; performing a regression analysis on the each of the plurality of energy use data sets to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual from all residuals yielded by the regression engine, the least valued residual indicating a corresponding energy lag for the each of the buildings; and categorizing the buildings into types according to similar energy lags.

A method for characterizing buildings, including generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each of the plurality of lag values is different from other ones of the plurality of lag values; performing a regression analysis on the each of the plurality of energy use data sets to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual from all residuals yielded by the regression engine, the least valued residual indicating a corresponding energy lag for the each of the buildings; and categorizing the buildings into types according to similar energy lags.

A method for dispatching buildings in a demand response program event including generating data sets for each of the buildings, each set having energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each set are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each of the plurality of lag values is different from other ones of the plurality of lag values; performing a regression analysis on each set to yield regression model parameters and a residual; determining a least valued residual from all residuals yielded, the least valued residual indicating a corresponding energy lag for the each of the buildings; and using energy lags for all of the buildings to generate a dispatch schedule for the demand response program event according to a prioritization of the energy lags.

A method for dispatching buildings participating in a demand response program including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating data sets for each of the buildings, each having energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each set are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each lag value; performing a regression analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued indicating a corresponding energy lag for each of the buildings; and using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict a dispatch order reception time for a demand response program event.

A method for predicting when energy consumption on a grid will exceed normal production capacity for buildings within the grid including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating data sets for each of the buildings, each set comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each set are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each lag value is different; performing a regression analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; and using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption on the grid will exceed normal production capacity.

An apparatus for estimating a buildings energy consumption, including thermal response processor and a regression engine. The thermal response processor generates energy use data sets, each having energy consumption values along with corresponding time and outside temperature values. The consumption values within each of the data sets are shifted by one of a plurality of lag values relative to the time and temperature values, where each of the plurality of lag values is different from other lag values. The thermal response processor performs a regression analysis on each of the energy use data sets to yield corresponding regression model parameters and a corresponding residual. The thermal response processor determines a least valued residual from all residuals yielded by the regression engine, the least valued residual indicating an energy lag for the building, and regression model parameters that correspond to the least valued residual are employed to estimate the energy consumption.

An apparatus including devices, a network operations center (NOC), and control nodes. Each of the devices consumes a portion of a resource when turned on, and performs a corresponding function within an acceptable operational margin by cycling on and off. The NOC is disposed external to a facility, and determines an energy lag for the facility based upon fine-grained energy consumption baseline data. The NOC employs the energy lag to generate a plurality of run time schedules that coordinates run times for the each of the devices to control the peak demand of the resource. Each of the plurality of control nodes is coupled to a corresponding one of the plurality of devices. The plurality of control nodes transmits sensor data and device status to the NOC via the demand coordination network for generation of the plurality of run time schedules, and executes selected ones of the run time schedules to cycle the plurality of devices on and off.

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