METHODS AND SYSTEMS FOR HVAC INEFFICIENCY PREDICTION

• US 20200132327A1
• Filed: 06/05/2018
• Published: 04/30/2020
• Est. Priority Date: 06/05/2017
• Status: Active Grant

• Current Assignee: Grid4C Co.
• Original Assignee: Grid4C Co.
• Inventors: SAMUNI, Eran, COHEN, Eran, ZAK, Alexander, RIMINI, Noa
• Granted Patent: US 11,175,061 B2
• Time in Patent Office: Days
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/47 Responding to energy costs
• CPC Class Codes: F24F 11/49 ensuring correct operation, e.g. by trial operation or configuration checks
• CPC Class Codes: F24F 11/52 Indication arrangements, e.g. displays
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: F24F 2110/10 Temperature
• CPC Class Codes: F24F 2110/12 of the outside air
• CPC Class Codes: F24F 2130/00 Control inputs relating to environmental factors not covered by group F24F2110/00
• CPC Class Codes: F24F 2140/60 Energy consumption
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

Systems and methods are provided for predicting inefficient HVAC operation, by obtaining first training data for HVACs in a training set of households during a first period of moderate weather; obtaining second training data for HVACs in the training set of households during a subsequent period of harsher weather; generating classification labels of the household locations of the training set according to the second training data; applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of harsher weather conditions; obtaining operational data pertaining to HVACs in an operational set of households during a second period of moderate weather; and applying the HVAC classification model to predict inefficiency of HVACs at individual households in the operational set during a second subsequent period of harsher weather.

Claims

1. A method for monitoring a plurality of heating, ventilation, and air conditioning (HVAC) systems and predicting inefficient HVAC operation, implemented by one or more processors operatively coupled to a non-transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the following steps:

during a first period of moderate weather, obtaining first training data for HVACs in a training set of households;

during a subsequent period of harsher weather, obtaining second training data for HVACs in the training set of households;

generating classification labels of the household locations of the training set according to the second training data;

applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of harsher weather conditions;

during a second period of moderate weather, obtaining operational data pertaining to HVACs in an operational set of households; and

applying the HVAC classification model to predict inefficiency of HVACs during a second subsequent period of harsher weather, at individual households in the operational set.

2. The method of claim 1, wherein the first, second, and operational data include:

smart meter readings of overall household electricity consumption, readings of HVAC activation time, readings of HVAC thermostat settings, readings of indoor temperatures, and readings of outdoor temperature.

3. The method of claim 2, wherein the first, second, and operational data include at least one additional type of data from a set of data types including:

HVAC mode of operation readings, HVAC physical properties, household profile parameters, and resident profile parameters.

4. The method of claim 3, wherein the HVAC mode of operation is one of cooling or heating.

5. The method of claim 3, wherein the HVAC physical properties include one or more of make, model, nominal power consumption, and rated efficiency.

6. The method of claim 3, wherein the household profile parameters include at least one of:

house type, size, age, geographic location, regional climate, orientation and level.

7. The method of claim 3, wherein the resident profile parameters include at least one of:

number of residents, relationship of residents, and hours during which they occupy the residence.

8. The method of claim 1, wherein the first and second training data indicate a percentage of HVAC operating time that a thermostat setting temperature is not reached.

9. The method of claim 1, wherein applying the HVAC classification model further comprises generating a prediction of whether the inefficiency is due to HVAC malfunction, faulty maintenance, extreme thermostat settings, poor insulation, or poor sizing.

10. The method of claim 1, wherein applying the HVAC classification model further comprises generating an alert when the model predicts an HVAC inefficiency.

11. The method of claim 1, wherein training the supervised machine learning algorithm comprises preprocessing the first and second training data to generate derived parameters from each respective type of data, wherein the derived parameters include one or more of a “

household efficiency score”

, an “

HVAC linear coefficient”

, and a “

breakpoint temperature difference”

.

12. The method of claim 1, wherein the HVAC classification model blends, by a weighted average, a convolutional neural network along with a gradient boosted decision tree classifier.

13. The method of claim 1, wherein the first period of moderate weather is a first spring period of multiple days, wherein the subsequent period of harsher weather is a first summer period of multiple days following the first spring period, wherein the second period of moderate weather is a second spring period of multiple days, and wherein the HVAC classification model predicts from operational data acquired during the second spring period an inability to efficiently cool a household during a summer immediately following the second spring period.

14. The method of claim 1, wherein the first period of moderate weather is a first fall period of multiple days, wherein the subsequent period of harsher weather is a first winter period of multiple days immediately following the first fall period, wherein the second period of moderate weather is a second fall period of multiple days, and wherein the HVAC classification model predicts from operational data acquired during the second fall period an inability to efficiently warm a household during a winter immediately following the second spring period.