Low frequency energy disaggregation techniques

1. A method of performing appliance itemization based on consumption data for a whole house, comprising:

receiving at a processor the consumption data;

classifying by the processor the consumption data into times of active signals and times of inactive signals;

determining by the processor based at least in part on the inactive signals, estimated passive water heating consumption and estimated refrigerator consumption; and

determining, based at least in part on the inactive signals, at least one period of vacation.

2. The method of claim 1 , wherein the consumption data is received from a utility.

3. The method of claim 1 , wherein the consumption data is received from an advanced metering infrastructure (AMI) device.

4. The method of claim 1 , wherein the consumption data is of a low resolution comprising data received at fifteen (15) minute, thirty (30) minute, or sixty (60) minute intervals.

5. The method of claim 1 , wherein the processor classifies the consumption data based at least in part upon:

using a sliding window function and storing the energy consumption in a vector; and

subsequently comparing the vector with the lowest energy consumption windows to determine times of inactive signals.

6. A method of performing appliance itemization based on consumption data for a whole house, comprising:

receiving at a processor the consumption data;

classifying by the processor the consumption data into active signals or inactive signals and determining times of active signals and times of inactive signals:

upon detection of inactive signals, detecting by the processor, passive water heating, and if detected, estimating passive water heating consumption;

upon detection of an active signals:

detecting by the processor, based at least in part on the active signal, active water heating, and if active water heating is detected, estimating active water heating consumption; and

detecting by the processor, based at least in part on the inactive signals, water heating in vacation mode.

7. The method of claim 6 , wherein the processor detects passive water heating by frequency estimation, and temperature correlation.

8. The method of claim 6 , wherein the processor detects passive water heating by amplitude estimation, the amplitude estimation comprising detecting passive pulses comprising pairs of substantially matching transitions.

9. The method of claim 6 , wherein the processor estimates passive water heating consumption by estimating typical amplitude and frequency of passive pulses, determined by a power rating of a detected water heater and the typical duration of passive pulses, wherein the duration of passive pulses is a function of a temperature setting of the water heater, insulation of the water heater, and the outside temperature.

10. The method of claim 6 , wherein the processor estimates active water heater consumption by estimating a typical amplitude of a water heater, behavior modeling, and data smoothing.

11. The method of claim 10 , wherein behavior modeling comprises user-specific patterns are modeled, including attributes of time of day and day of week.

12. The method of claim 10 , wherein attributes may further include characteristics selected from the group consisting of temperature, season, number of occupants of the house, specific appliance usage, and demographic data including geographic region, age of occupants, gender of occupants, and/or socioeconomic status of occupants.

13. A method of performing appliance itemization based on consumption data for a whole house, comprising:

receiving at a processor the consumption data;

classifying by the processor the consumption data into times of active signals and times of inactive signals; and

upon detection of active signals detecting by the processor, based at least in part on the active signals, lighting, and if lighting is detected, estimating lighting consumption and active water heating consumption.

14. The method of claim 13 , wherein the processor detects lighting based at least in part by identifying arcs of consumption in time periods where lighting is expected to be used, wherein identifying arcs of consumption comprises:

removing unsuitable data points from the consumption data;

cleaning the consumption data using percentile filtering, minimum value removal, and/or smoothing noise removal;

detecting and removing seasonal loads by selecting time points showing seasonal characteristics including contiguous days of absence or presence when typically occurring lighting is absent; and

extracting a lighting band, comprising a collection of times in a day at which lighting is present.

15. The method of claim 13 , wherein the processor estimates lighting consumption at each data point along with lighting capacity parameters, timings of lighting, and seasonality.

16. A method of performing appliance itemization based on consumption data for a whole house, comprising:

receiving at a processor the consumption data;

classifying by the processor the consumption data into times of active signals and times of inactive signals; and

detecting by the processor, based at least in part on the inactive signals, refrigeration, and if refrigeration is detected, estimating refrigerator consumption.

17. The method of claim 16 , wherein the processor detects refrigeration by analyzing data during low activity periods in which regular pulses of a refrigeration compressor motor are detectable.

18. The method of claim 17 , wherein the processor estimates refrigerator consumption by:

analyzing several months of consumption data and identifying low activity periods;

scoring each low activity period for quality;

compiling low activity periods that meet a quality threshold; and

using a machine learning model to, based on the low activity periods that meet the quality threshold, predict refrigeration consumption.

19. The method of claim 18 , wherein the machine learning model is a regression model based at least in part on training data.