METHOD AND APPARATUS FOR DETERMINING ENERGY SAVINGS BY USING A BASELINE ENERGY USE MODEL THAT INCORPORATES A NEURAL NETWORK ALGORITHM
First Claim
1. A computer-implemented method for determining energy savings in an energy-consuming facility, comprising:
- inputting baseline facility condition data to a neural network model generator, the baseline facility condition data representing baseline facility conditions during a first time interval before energy conservation measures and comprising weather conditions experienced by the facility during the time interval;
inputting baseline energy consumed by the facility during the first time interval;
the neural network model generator generating a neural network model in response to the baseline facility condition data and baseline energy consumed, the neural network model modeling how facility energy consumption responds to facility conditions;
inputting actual facility condition data to the neural network model, the actual facility condition data representing actual facility conditions during a second time interval after the energy conservation measures and comprising weather conditions experienced during the second time interval;
inputting actual energy consumed by the facility during the second time interval;
the neural network model outputting, in response to the actual facility condition data, an estimate of energy that would have been consumed under the baseline facility conditions but for the energy conservation measures; and
computing energy savings, wherein the energy savings are defined by a difference between the actual energy consumed during the second time interval and the estimate of energy that would have been consumed but for the energy conservation measures.
1 Assignment
0 Petitions
Accused Products
Abstract
A computer-based system, computer-implemented method and computer program product facilitate determining energy cost savings in an energy-consuming facility, such as a commercial building, using a neural network model that projects or estimates the amount of energy that would have been consumed by the facility but for the implementation of energy efficiency or conservation measures. Energy savings are represented by the difference between the estimate of energy that would have been consumed but for the measures and the actual amount of energy consumed by the facility under actual conditions during a time interval after the measures have been implemented.
-
Citations
23 Claims
-
1. A computer-implemented method for determining energy savings in an energy-consuming facility, comprising:
-
inputting baseline facility condition data to a neural network model generator, the baseline facility condition data representing baseline facility conditions during a first time interval before energy conservation measures and comprising weather conditions experienced by the facility during the time interval;
inputting baseline energy consumed by the facility during the first time interval;
the neural network model generator generating a neural network model in response to the baseline facility condition data and baseline energy consumed, the neural network model modeling how facility energy consumption responds to facility conditions;
inputting actual facility condition data to the neural network model, the actual facility condition data representing actual facility conditions during a second time interval after the energy conservation measures and comprising weather conditions experienced during the second time interval;
inputting actual energy consumed by the facility during the second time interval;
the neural network model outputting, in response to the actual facility condition data, an estimate of energy that would have been consumed under the baseline facility conditions but for the energy conservation measures; and
computing energy savings, wherein the energy savings are defined by a difference between the actual energy consumed during the second time interval and the estimate of energy that would have been consumed but for the energy conservation measures. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
-
9. A computer program product for determining energy savings in an energy-consuming facility, comprising a computer-readable medium encoded with computer-executable instructions for:
-
inputting baseline facility condition data to a neural network model generator, the baseline facility condition data representing baseline facility conditions during a first time interval before energy conservation measures and comprising weather conditions experienced by the facility during the time interval;
inputting baseline energy consumed by the facility during the first time interval;
the neural network model generator generating a neural network model in response to the baseline facility condition data and baseline energy consumed, the neural network model modeling how facility energy consumption responds to facility conditions;
inputting actual facility condition data to the neural network model, the actual facility condition data representing actual facility conditions during a second time interval after the energy conservation measures and comprising weather conditions experienced during the second time interval;
inputting actual energy consumed by the facility during the second time interval;
the neural network model outputting an estimate of energy that would have been consumed under the baseline facility conditions but for the energy conservation measures; and
computing energy savings, wherein the energy savings are defined by a difference between the actual energy consumed during the second time interval and the estimate of energy that would have been consumed but for the energy conservation measures. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
-
-
17. A system for determining energy savings in an energy-consuming facility, comprising:
-
a database for storing facility condition data representing facility conditions during a first time interval before energy conservation measures and during a second time interval after energy conservation measures, the facility condition data comprising weather conditions experienced by the facility during each time interval, the database further for storing energy data representing energy consumed by the facility during each time interval;
a neural network model generator for generating a neural network model modeling how facility energy consumption responds to facility conditions, wherein the neural network model generator generates the neural network model in response to baseline facility condition data representing facility conditions during the first time interval and in response to baseline energy consumed by the facility during the first time interval;
a neural network engine for producing an estimate of energy that would have been consumed under the baseline facility conditions but for the energy conservation measures, the neural network engine producing the estimate in response to actual facility condition data representing actual facility conditions during the second time interval; and
a user interface for outputting energy savings, wherein the energy savings are a difference between the actual energy consumed during the second time interval and the estimate of energy that would have been consumed but for the energy conservation measures. - View Dependent Claims (18, 19, 20, 21, 22, 23)
-
Specification