ANALYZING INPUTS TO AN ARTIFICIAL NEURAL NETWORK
Systems, methods, and associated software are described for receiving first inputs and first outputs, providing the first inputs and first outputs to an artificial neural network (ANN) for training, creating a boundary such that the first inputs fall within the boundary or on a boundary line defining the boundary, wherein additional inputs are considered to be valid if they fall within the boundary and are considered to be invalid if they fall outside the boundary, receiving second inputs, separating the valid second inputs from the invalid second inputs, determining a percentage of the second inputs that are invalid, and when the percentage exceeds a predetermined threshold, retraining the ANN and redefining the boundary such that the second inputs fall within the boundary.
1 . A computer-implemented method comprising:
receiving first inputs and first outputs;
providing the first inputs and first outputs to an artificial neural network (ANN) for training;
creating a boundary such that the first inputs fall within the boundary or on a boundary line defining the boundary, wherein additional inputs are considered to be valid if they fall within the boundary and are considered to be invalid if they fall outside the boundary;
receiving second inputs;
separating the valid second inputs from the invalid second inputs;
determining a percentage of the second inputs that are invalid; and
when the percentage exceeds a predetermined threshold, retraining the ANN and redefining the boundary such that the second inputs fall within the boundary.
2 . The computer-implemented method of claim 1 , further comprising:
disregarding the invalid inputs when the percentage does not exceed the predetermined threshold.
3 . The computer-implemented method of claim 1 , wherein defining the boundary further comprises using Pareto-optimality to reduce the number of points defining the boundary.
4 . The computer-implemented method of claim 3 , wherein Pareto-optimality eliminates points based on dominance between two potential solutions, whereby a non-dominated point dominates another point when it is at least equal in all ways and better in at least one way.
5 . The computer-implemented method of claim 1 , further comprising:
receiving a plurality of weather measurements of dry-bulb temperature, wet-bulb temperature, and solar radiation of a region in which an asset is located, wherein the plurality of weather measurements are taken during a baseline time period;
receiving energy consumption measurements indicating an amount of energy consumed by one or more systems of the asset, wherein the energy consumption measurements are taken during the baseline time period;
maintaining current time information, the current time information including at least time of day information and day of week information;
calculating an hourly energy usage amount from the energy consumption measurements and time of day information using valid inputs; and
providing the current time information and weather measurements to the ANN as inputs, and providing the hourly energy usage amount to the ANN as an output.
6 . The computer-implemented method of claim 1 , wherein providing the current time information, weather measurements, and hourly energy usage amount to the ANN is configured to train the ANN to predict hourly energy usage.
7 . The computer-implemented method of claim 6 , further comprising:
creating a graph showing a first plot of actual hourly energy usage and a second plot of predicted hourly energy usage.
8 . The computer-implemented method of claim 7 , further comprising:
observing whether a discrepancy between the actual hourly energy usage and the predicted hourly energy usage is greater than a predetermined amount; and
enabling communication with a manager of the asset to investigate a cause of the discrepancy.
9 . A system comprising:
a processing device configured to control the operations of the system;
an interface device configured to receive measurement inputs; and
a prediction program in communication with the processing device, the prediction program configured to enable the processing device to:
define a boundary within which first measurement inputs are considered to be valid;
determine whether a plurality of second measurement inputs are valid;
separate valid second measurement inputs from invalid second measurement inputs;
calculate an output value from the valid second measurement inputs; and
provide the valid second measurement inputs and output value to an artificial neural network (ANN) to train the ANN.
10 . The system of claim 9 , wherein the prediction program is further configured to enable the processing device to determine a percentage of the second measurement inputs that are invalid inputs, and, when the percentage exceeds a predetermined threshold, retrain the ANN and redefine the boundary within which inputs are considered to be valid.
11 . The system of claim 9 , wherein the prediction program enables the processing device to define the boundary by using Pareto-optimality to reduce the number of points defining the boundary, wherein Pareto optimality eliminates points based on dominance between two potential solutions, whereby a non-dominated point dominates another point when it is at least equal in all ways and better in at least one way.
12 . The system of claim 9 , wherein the interface device is configured to receive a plurality of weather measurements of dry-bulb temperature, wet-bulb temperature, and solar radiation of a region in which an asset is located and a plurality of energy consumption measurements indicating amounts of energy consumed by one or more energy-consuming systems of the asset, wherein the plurality of weather measurements and energy consumption measurements are taken during a baseline time period.
13 . The system of claim 12 , wherein the prediction program is an energy use prediction program.
14 . The system of claim 13 , wherein the energy use prediction program is configured to enable the processing device to:
maintain current time information, the current time information including at least time of day information and day of week information;
calculate an hourly energy usage amount from the energy consumption measurements and time of day information using valid inputs; and
provide the current time information and weather measurements to the ANN as inputs, and provide the hourly energy usage amount to the ANN as an output.
15 . The system of claim 14 , wherein the processing device provides the current time information, weather measurements, and hourly energy usage amount to the ANN in order to train the ANN to predict hourly energy usage.
16 . The system of claim 15 , wherein the energy use prediction program further enables the processing device to create a graph showing a first plot of actual hourly energy usage and a second plot of predicted hourly energy usage and determine whether a discrepancy between the actual hourly energy usage and the predicted hourly energy usage is greater than a predetermined amount.
17 . A computer-readable medium configured to store instructions for predicting energy consumption, the instructions comprising:
logic adapted to receive a plurality of first inputs;
logic adapted to define a boundary within which the first inputs are considered to be valid;
logic adapted to calculate an output value from the first inputs;
logic adapted to provide the first inputs and output value to an artificial neural network (ANN) to train the ANN;
logic adapted to determine whether a plurality of second inputs are valid and to separate valid inputs from invalid inputs; and
logic adapted to retrain the ANN when a percentage of invalid second inputs exceeds a predetermined threshold.
18 . The computer-readable medium of claim 17 , wherein the logic adapted to determine whether the second inputs are valid is further adapted to disregard the invalid inputs when the percentage does not exceed the predetermined threshold.
19 . The computer-readable medium of claim 18 , wherein the logic adapted to define the boundary is further adapted to utilize Pareto optimality to reduce the number of points defining the boundary.
20 . The computer-readable medium of claim 19 , wherein Pareto optimality eliminates points based on dominance between two potential solutions, whereby a non-dominated point dominates another point when it is at least equal in all ways and better in at least one way.
21 . The computer-readable medium of claim 17 , further comprising logic adapted to redefine the boundary within which inputs are considered to be valid when the percentage exceeds the predetermined threshold.
22 . The computer-readable medium of claim 17 , further comprising:
logic adapted to receive a plurality of weather measurements of dry-bulb temperature, wet-bulb temperature, and solar radiation of a region in which an asset is located, wherein the plurality of weather measurements are taken during a baseline time period;
logic adapted to receive energy consumption measurements indicating an amount of energy consumed by one or more systems of the asset, wherein the energy consumption measurements are taken during the baseline time period;
logic adapted to maintain current time information, the current time information including at least time of day information and day of week information;
logic adapted to calculate an hourly energy usage amount from the energy consumption measurements and time of day information using valid inputs; and
logic adapted to provide the current time information and weather measurements to the ANN as inputs and to provide the hourly energy usage amount to the ANN as an output.
23 . The computer-readable medium of claim 22 , wherein the logic adapted to provide the current time information, weather measurements, and hourly energy usage amount to the ANN is further adapted to train the ANN to predict hourly energy usage.
24 . The computer-readable medium of claim 22 , further comprising logic adapted to create a graph showing a first plot of actual hourly energy usage and a second plot of predicted hourly energy usage and to observe whether a discrepancy between the actual hourly energy usage and the predicted hourly energy usage is greater than a predetermined amount.