Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014

Abstract

With the cost of consuming resources increasing (both economically and ecologically), homeowners need to find ways to curb consumption. The Almanac of Minutely Power dataset Version 2 (AMPds2) has been released to help computational sustainability researchers, power and energy engineers, building scientists and technologists, utility companies, and eco-feedback researchers test their models, systems, algorithms, or prototypes on real house data. In the vast majority of cases, real-world datasets lead to more accurate models and algorithms. AMPds2 is the first dataset to capture all three main types of consumption (electricity, water, and natural gas) over a long period of time (2 years) and provide 11 measurement characteristics for electricity. No other such datasets from Canada exist. Each meter has 730 days of captured data. We also include environmental and utility billing data for cost analysis. AMPds2 data has been pre-cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms.

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OPEN

SUBJECT CATEGORIES

Electrical and electronic

engineering

Sustainability

Scientic data

Energy modelling

Energy

Received: 28 August 2015

Accepted: 03 May 2016

Published: 07 June 2016

Data Descriptor: Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014

Stephen Makonin1,2, Bradley Ellert2, Ivan V. Baji1 & Fred Popowich2

With the cost of consuming resources increasing (both economically and ecologically), homeowners need to nd ways to curb consumption. The Almanac of Minutely Power dataset Version 2 (AMPds2) has been released to help computational sustainability researchers, power and energy engineers, building scientists and technologists, utility companies, and eco-feedback researchers test their models, systems, algorithms, or prototypes on real house data. In the vast majority of cases, real-world datasets lead to more accurate models and algorithms. AMPds2 is the rst dataset to capture all three main types of consumption (electricity, water, and natural gas) over a long period of time (2 years) and provide 11 measurement characteristics for electricity. No other such datasets from Canada exist. Each meter has 730 days of captured data. We also include environmental and utility billing data for cost analysis. AMPds2 data has been pre-cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms.

Design Type(s) observation design time series design data integration objective

Measurement Type(s)

electricity consumption natural gas consumption water consumption

weather record

Technology Type(s) electricity meter gas meter water meter weather station

Factor Type(s) energy supply function

Sample Characteristic(s) building Province of British Columbia

1Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, Canada V5A 1S6. 2Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, Canada V5A

1S6. Correspondence and requests for materials should be addressed to S.M. (email: mailto:[email protected]

Web End [email protected] ).

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Background & Summary

Currently, much of the world is focused on reducing electricity consumption; our increase in consumption is neither economically nor environmentally sustainable. Additionally, there is a growing consensus that environmental and economical sustainability are inextricably linked1. As the cost of power rises, we must nd technological solutions that help reduce and optimize energy use2,3. Residential homes contribute about 34% to the total power consumption in the USA and their consumption is projected to increase to 39% by 2030 (ref. 4). One way to help homeowners and occupants reduce their consumption is to monitor and present how much power their appliances are using through an effective eco-feedback device or display mechanism5,6.

The Almanac of Minutely Power dataset (AMPds) was initially released in 2013 with one year of meter data without environmental and utility billing data7 (Data Citation 1). The rst release also contained some data integrity issues: missing readings and a counter reset that happened with the water meters. With this second release (AMPds2) we increased the monitoring length to two years (730 days of captured data per meter). The integrity problems that existed in the rst release have been corrected. We have added historical climate data, two years of hourly weather data, and two years of utility billing data.

AMPds has been used (See Google Scholar: https://scholar.google.ca/scholar?cites=9977726888743581483

Web End =https://scholar.google.ca/scholar?cites = https://scholar.google.ca/scholar?cites=9977726888743581483

Web End =9977726888743581483 ) and can be used in research that looks at: non-intrusive load monitoring (NILM, a.k.a. load disaggregation)8, energy use behaviour, eco-feedback and eco-visualizations6,9, application and verication of theoretical algorithms/models, appliance studies, demand forecasting, smart home frameworks, grid distribution analysis, time-series data analysis, energy efciency studies, occupancy detection, energy polity and socio-economic frameworks10, and advanced metering infrastructure (AMI) analytics. Testing the accuracy performance with real-world datasets is crucial in these elds of research. Synthesized data does not realistically represent an actual dataset as a real-world dataset would normally have certain complexity that is harder predict and in many cases can be very difcult to deal with [ref. 11, p.114].

There are indeed other datasets that exist from the USA1216, Europe1721, and Asia22. However, AMPds2 is unique for a number of different reasons. AMPds2 is the only dataset from Canada. It is the only dataset to include all three main types of consumption: electricity, water, and natural gas. Data is captured over a long period of time (two years) and is presented with minor amounts of missing data algorithmically lled in to maintain a continuous frequency of readings (once per minute). For electricity data we provide 11 measurement characteristics for each meter: voltage, current, frequency, displacement power factor, apparent power factor, real power, real energy, reactive power, reactive energy, apparent power, and apparent energy.

AMPds2 data has been cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms. Other datasets (e.g., REDD14) leave the onus of data cleaning on each researcher. This means that the same dataset can be cleaned very differently. This results in an inability to reproduce and compare algorithms published.

Methods

Residential house characteristics

Our data was collected from a house built in 1955 in the Greater Vancouver metropolitan area in British Columbia (Canada), which underwent major renovations in 2005 and 2006, receiving a Canadian Government EnerGuide23 rating of 82% (from 61%). The house is located in Burnaby, the municipality east of Vancouver. Elevation-wise, the house is 80 m above sea level and the front of the house faces south. The house has one level above grade and a basement making up a total of 2,140 ft2 (199 m2) of living space (1,070 ft2 or 99.5 m2 per oor). The main oor ceiling height is 8 ft (2.44 m) and the basement ceiling height is 7 ft (2.13 m). Within the house is a rental unit that takes up approximately half the basement (603 ft2 or 56 m2 of living space). The detached garage is approximately 161 ft2 (15 m2) and the overhead door faces the back alleyway (see Fig. 1).

The house has the original wood-frame construction. In 2006, all existing exterior wall stucco was removed. Proper vent covering was installed under the eaves and the exterior walls were re-stuccoed with a light green California nish. The previous stucco nished was removed. The house has an black asphalt shingle roof that was replaced in 2007. The new asphalt shingles are light brown. When the stucco and roof were replaced, 14-inch plywood was nailed to the existing shiplap boarding.

Originally, the above grade walls were insulated with batt insulation evaluated at R7 and the roof was insulated with blown-in insulation evaluated at R19. After renovations, R24 batt insulation was added on top of the existing ceiling insulation. The main oor wall insulation was not improved. For the basement, R24 was added to the ceiling and above grade walls. Below grade walls had R9 extruded polystyrene rigid insulation afxed to the concrete walls. The basement oor was upgraded to have DRIcore sub-ooring (see Manual_dricore.pdf, Data Citation 2) which is rated at R1.7.

Windows are double-pane low-e glass and were replaced in 2005 (see Table 1). All doors are insulated core metal and were replaced in 2005. The basement walls are approximately 25.4 cm thick with the South basement wall (front of house) almost completely below grade, while the North wall (back of house) is about 1 m below grade. The house has three full bathrooms (tub with shower, toilet, and sink) and a master bedroom ensuite (toilet and sink). Two of the bathrooms are in the basement; one is in the

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Figure 1. Test House Property. Surrounding area of house: (a) property survey and (b) location in surrounding block. Yellow lines show 1 m elevation contours.

Window Size (cm)

Location Sub-Location Direction Width Height Window Type NotesMain Floor Bathroom North 63.818 97.155 A/O obscure, top vent open outward Main Floor Bedroom South 121.603 117.475 XO 50% sliding ventsMain Floor Bedroom North 152.400 101.918 XO 50% sliding ventsMain Floor Dining Room North 184.785 132.398 XOX 18 sliding ventsMain Floor Master Ensuite East 63.818 97.155 A/O obscure, top vent open outward Main Floor Kitchen North 89.218 83.820 XO 50% sliding ventsMain Floor Master Bedroom South 200.025 101.918 XOX 18 sliding ventsMain Floor Stairwell/Kitchen North 150.178 117.475 XO 50% sliding ventsMain Floor Living Room South 306.705 132.398 XOX 24 sliding vents Basement Rec Room East 73.978 99.378 OX 50% sliding vents, laminated Basement Home Ofce East 199.708 99.060 XOX 18 sliding vents, laminated Rental Suite Bedroom North 73.978 89.218 OX 50% sliding vents, laminated Rental Suite Kitchen West 73.660 73.660 OX 50% sliding vents, laminated Rental Suite Living Room West 73.660 73.660 OX 50% sliding vents, laminated Houses Total Window Surface (m ) 19.738

Table 1. House Windows.

rec room, and the other is in the rental suite. Faucets and showerheads are restricted to a maximum ow of 9.5 l/min (2.6 GPM). All toilets have 6 l tanks and are dual-ush.

Occupancy elaboration

The main house has a family of three persons: a male and a female adult in their late 30 s and a daughter between the age of 5 and 6. The male adult is a full-time student at a local university, the female adult is self-employed, and the child attends full-time elementary school. A rental suite houses one male occupant in his early 20 s with full-time employment.

HVAC system elaboration

Our test house has a dual-fuel HVAC system where a heat pump is used alongside a forced air gas furnace. The heat pump cools the house in summer and heats the house in winter. The gas furnace is

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used, but only when it is too cold outside for the heat pump to operate effectively. When the outside ambient temperature is 2 C or lower, the HVAC system changes over from electric heating (heat pump) to natural gas heating using the furnace. At low temperatures, heat pumps are not efcient for heating and can strain the compressor.

During data collection, the HVAC thermostat was set to a constant heating set-point of 21 C and the cooling set-point ranged within 2426 C. The HVAC furnace fan was set to constantly run 24-hours to circulate the air. The furnace is 2-stage with a variable-speed fan and is rated as 93% efcient. The heat pump has a 2-stage compressor and is rated at 17 SEER. It is the central unit for air conditioning; there are no other air conditioning units in the house (besides the windows).

Data collection

Our main concern when designing the data collection system for AMPds2 was integrity and accuracy. For these reasons we chose to use industry-standard equipment for monitoring and acquisition. Data was stored off-site on a database server that was hosted at a co-location facility with proper power backup and network connection redundancy. Figure 2 depicts the setup of our data collection system. Table 2 summarizes the specications of the metering equipment used, including the accuracy standards each meter adheres to.

After two years of collection, only 2,029 electricity readings and 437 water and natural gas readings were missing from a total of 1,051,200 readings for each resource (discussed in more detail below for each resource). The missing readings were algorithmically created during the data cleaning process which is discussed in detail in the Dataset File Preparation Subsection24.

Electricity supply & metering

BC Hydro is the provincial utility that provides electricity to the house via a 240 V, 200A service. As with all Canadian homes, two 120 V lines enter the houseleg 1 (L1) and leg 2 (L2) of the same phase. There

water meters natural gas meters

Elster/Kent V100

Elster/Kent V100 Elster AC-250 Elster BK-G4

2 DENT PowerScout 18 power meters

wired Ethernet

AcquiSuite EMB A7810 data acquisition unit

access point

0.5L pulses

0.5L pulses

25dm3 pulses

1 ft3 pulses

wired Ethernet

AcquiSuite EMB A8810 data acquisition unit

network router

MODBUS over RS-485 serial

Dell PowerEdge 2970 cloud server

HTTP POSTs (sent 1/min) from both data acquisition units to a remote cloud server

the Internet

Serial SCSI RAID 1 storageLighttpd web server using Python 2.4 CGI scripts MySQL database server

Figure 2. Block Diagram of Data Collection System. Electricity, water, and natural gas were monitored using industrial meters. Data was collected using industrial data acquisition units and stored offsite on a database server.

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Meter Resource Make Model Manuals/Specs (.pdf) Sample Rate Accuracy Standards Measurement Technology WHE (incl. all

sub-meters)

WHW (v1) water DJL Meter DLJSJ75C Metering_DLJSJ75C pulse/Gallon AWWA C712

ISO 4046 Class C

water Elster/Kent V100 Metering_KentV100 pulse/0.5 l UK WRc BS5728

ISO 4064 Class C/D

WHG natural gas Elster AC250 Metering_AC-250 pulse/25dm ANSI B109.1

Measurement Canada

Table 2. Metering Equipment Specications.

are pole transformers that convert the single phase into two legs. Each transformer services about ve homes.

Electricity measurements were taken by two DENT PowerScout 18 units metering 24 loads at the electrical circuit breaker panel. Only 21 loads were kept. The three loads that were removed were: the gas stovetop plug breaker, the microwave plug breaker, and a randomly chosen lighting breaker because no activity was recorded. All current and all current-based measurements were recorded as zero. The gas stovetop only used electricity to ignite the gas burners. The microwave had never been used and was removed at one point. The lighting breaker that was chosen was for a backyard outside light that was never usedthe bulb was burned out and not replaced.

Measurements were read over a RS-485/Modbus communication link by a Obvius AcquiSuite EMB A8810 data acquisition unit. During the data cleaning process for electricity, we found and corrected 55 readings where 1 of 21 meters had missing measurements and 2,029 readings where more than one of 21 meters had missing measurements (see Dataset File Preparation Subsection for more details).

Water supply & metering

Burnabys water distribution system is fed by four water pump stations, four water reservoirs, and twenty-one pressure reducing stations to control and regulate water pressure. Water pressure is produced by gravity from the higher elevation water reservoirs that Metro Vancouver manages.

Water service is via a 34-inch pipe at a pressure between 108118 psi (744.6813.6 kPa) [reported by Engineering Department]. A pressure regulator is used (see specications in Manual_WilkinsModel70.

pdf, Data Citation 2) to maintain water pressure in the house at 60 psi (413.7 kPa).

Water measurements were taken by 2 Elster/Kent V100 water meters, which also send pulses to a data acquisition unit. These water meters are volumetric cold water meters that measure water with a rotary piston. Before July 14, 2012 (timestamp 1342287780) the water main was metered by a DLJ 75C meter and hot water was metered by an Elster S130 meter. These meters pulse once per gallon which was too coarse of a measurement for the amount of water being consumed by the houses occupants. This was the reason for replacing these meters with ones that pulse more frequently. See Table 2 for details on these water meters (e.g., standards compliance and accuracy data).

Pulse data was collected using an Obvius AcquiSuite EMB A7810. To note, the Obvius AcquiSuite units have a per-minute sampling limitation. It is not possible to capture data at a faster rate, which is an acceptable cost for reliability. During the data cleaning process for water, we found and corrected 437 readings that were missing from both water meters.

Dishwasher water (DWW) consumption data was annotated by hand25,26. Having the electricity

consumption data and details in the appliance manual about how the dishwater used water made this task relatively easy. This is further discussed in the Technical Validation section.

Natural gas supply & metering

Natural gas is supplied to the house by FortisBC at a pressure of 1.75 kPa and is composed of methane, ethane, propane, and butane. FortisBC uses the Higher Heating Value (HHV) as the conversion factor when converting from gas volume to energy used in gigajoules (GJ). HHV is the total heat obtained from combustion. The heating value of the gas is measured daily by FortisBC (see le NaturalGas_HeatValues, Data Citation 2). For the Lower Mainland (Zone 24) the measurement energy desity values are in GJ/ 103m3. FortisBC assumes a temperature of 15 C and a pressure of 101.325 kPa for conversion of gas values into energy values.

Natural gas measurements were taken by an Elster AC250 gas meter and a Elster BK-G4 gas meter; both send pulses to a data acquisition unit. These natural gas meters are diaphragm meters. See Table 2 for details on meter standards compliance and accuracy. Pulse data was collected using an Obvius

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electricity

DENT

PowerScout 18

1 Hz sampling

Metering_DENTps18

1% (o0.5% typical)

ANSI C12.20

IEC 62053

digital DSP

single-jet (inferential) impeller

HTW (v1) water Elster S130 Metering_S130hot pulse/Gallon unknown single-jet (inferential) impeller WHW (v2)

HTW (v2)

volumetric, grooved piston

exterior temperature

compensatedgas diaphragm

FRG natural gas Elster BK-G4 Metering_BK-G4 pulse/1 ft EN 1359 interior gas diaphragm Modbus data Obvius EMB-A8810 Metering_EMB-A8810 read/minutePulse data Obvius EMB-A7810 Metering_EMB-A7810 read/minute

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AcquiSuite EMB A7810. During the data cleaning process for gas, we found and corrected 437 readings that were missing from both gas meters.

Environmental & weather records

Hourly weather data was downloaded from the Environment Canadas Weather Ofce which has a weather station at YVR (Vancouver International Airport) located at latitude of 49.20, longitude of 123.18, and elevation of 4.30 m. Our test house is approximately 18 km from YVR with an elevation difference of approximately 75 m. YVR is located next to the water which might account for slight differences in outdoor temperature between the two locations. There is no precise method to determine this difference. Anecdotally we have seen up to 2 C. Date and times listed within this le are in Local Standard Time (LST). Add 1 h to adjust for Daylight Saving Time when it is observed. The Data Quality column (and other columns) may contain M (missing), E (estimated), NA (not available), or ** (Partner data that is not subject to review by the National Climate Archives).

Historical climate normals data (from 1981 to 2010) was downloaded from the Environment Canadas Weather Ofce which had a weather station at Burnaby Capitol Hill (latitude of 49.17, longitude of 122.59, and elevation of 182.9 m). This weather station was closer to our test house but closed down in 2010. Precipitation data about rainfall and snowfall is included.

Utility bills & invoice records

The billing data for all three forms of consumption was created from the values that exist on the included redacted utility billing statements. We were able to download 50% of the billing data from our account on the utility's website. The remaining data was manually entered in. All billing data was human veried for accuracy from each billing statement. Data entered by hand was rechecked for accuracy after the values of each bill were recorded.

Code availability

Code used to store data collected via the data acquisition units to the database server can be download from the online code repository GitHub24. The scripts used to convert the database tables to the nal dataset les can be downloaded from the same online code repository (see the Technical Validation section).

Data Records

AMPds2 is publicly available for download from Harvard Dataverse (Data Citation 2) in many different formats including: the original CSV, tab-delimited, and RData format. Table 3 lists a description of each le that is part of AMPds2. File names describe the contents by listing the type of data and the meter ID separated by an underscore. There are four types of data: Electricity, Water, NaturalGas, and Climate. For example, Electricity_CDE.csv would be electricity data from the clothes dryer (CDE) meter, NaturalGas_Billing.csv would be natural gas billing data. Refer to Table 3 for a description of all les included in the AMPds2 dataset. Refer to Table 4 (available online only) for a description of meter IDs and datale column names.

Each row in each of the dataset les represents a single meter reading once every minute with an associated unix timestamp. Each reading contains all the measurements and calculations provided by the meter. Refer to Table 4 (available online only) for specic information on each measurement provided. In the case of pulse metering, the data acquisition unit calculated the three measurements (counter, avg_rate, inst_rate) as pulses were received from each meter.

This integer timestamp is the amount of seconds since 1970-01-01 12:00:00am (UTC). Because each reading is one minute apart the timestamp number increases by 60 every reading. The two data acquisition units use the Network Time Protocol (NTP) for clock synchronization. There were records where the timestamp was off by 10 s. In these cases our data cleaning script24 corrected the timestamp to have zero-seconds. This slight variation in time was caused by having to download the readings of 24 loads over a limiting xed baud rate (of 9600 bps) used by the DENT meters.

Table 4 (available online only) describes the column names found within each le. No one le will contain all the column names listed. Figure 3, Fig. 4, and Fig. 5 give some insight as to how the house consumed resources over the two years. Additionally, Table 5 (available online only) gives detailed information about each of the major appliances that consumed resources in our test house.

Climate data les are kept in the original format provided by Environment Canada. Each row in Electricity_Billing.csv and NaturalGas_Billing.csv will match a utility billing statement found in Electricity_Statements.csv and NaturalGas_Statements.csv, respectively. Statements are not available for Water_Billing.csv data.

One-time events & oddities

On May 4, 2012 at 10:34am local time (timestamp 1336152840) the houses existing electro-mechanical meter was replaced with a digital smart meter. This explains why all electricity reading were recorded as zero.

On July 14, 2012 between 10:43am and 5:03pm local time the houses water supply was disconnected to perform a repair. The instantaneous hot water unit has internal leaking due to micro-imperfections in the

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File Name DescriptionClimate_HistoricalNormals.csv A summary of climate normals observed from the years between 1981 and 2010 measured from Environment Canadas

weather station at Burnaby Mountain (which closed in 2010).

Climate_HourlyWeather.csv Hourly weather data measured from Environment Canada's weather station at YVR (Vancouver International Airport). Electricity_???.csv There is a le of electricity measurements for each meter and sub-meter: B1E, B2E, BME, CDE, CWE, DNE, DWE,

EBE, EQE, FGE, FRE, GRE, HPE, HTE, OFE, OUE,RSE, TVE, UTE, WHE, and WOE (see Table 2). Electricity_?.csv There is a le for each instantaneous measurement (I, P, Q, and S) that has each meter, sub-meter, and soft-meter as

columns. These les are convenient for training and testing disaggregators and eco-visualizations. There is no need to

parse a specic column value for each of the sub-meter les which can be time consuming. Electricity_Billing.csv Data values collected from each power bill statement.

Electricity_Monthly.csv Monthly consumption data downloaded from utility used to create Fig. 4.

Electricity_Statements.pdf Redacted copies of each of the power bill statements received during the data collection period.

Manual_*.pdf Manuals for appliances listed in Table 3.

Metering_*.pdf Technical specications and documentation of the metering equipment used.

NaturalGas_Billing.csv Data values collected from each gas bill statement.

NaturalGas_FRG.csv Natural gas consumption measurements from the furnace gas sub-meter.

NaturalGas_HeatValues.csv The daily measured heat values downloaded from utility. Measurements in GJ/103 m . Our test house is in Zone 24

(Lower Mainland).

NaturalGas_Monthly.csv Monthly consumption data downloaded from utility used to create Fig. 4.

NaturalGas_Statements.pdf Redacted copies of each of the gas bill statements received during the data collection period. NaturalGas_WHG.csv Natural gas consumption measurements from the whole-house gas meter.

Water_Billing.csv Data values collected from each City of Burnaby annual utility bill statement.

Water_DWW.csv Water consumption measurements for the dishwasher. Annotated by hand.

Water_HTW.csv Water consumption measurements from the instant hot water unit sub-meter. Water_QualityReport_2012.pdf The City of Burnaby annual water quality report for 2012.

Water_QualityReport_2013.pdf The City of Burnaby annual water quality report for 2013.

Water_QualityReport_2014.pdf The City of Burnaby annual water quality report for 2014.

Water_WHW.csv Water consumption measurements from the whole-house water meter.

Water_ZonesMap.pdf Map of the City of Burnaby shown the different water zones. Our test house is in Zone 585 (North Burnaby).

Table 3. File Descriptions.

copper pipe. During this time the existing water meters (which pulsed at a per gallon rate) were replaced with water meters that pulse per 0.5 l.

During the period of data collection, the main house family when on holidays/travel during the following periods: May 17, 2012; June 918, 2012; and, July 31August 8, 2013. Consumption during these times should be near zero. The rental occupant was not tracked, in terms of taking holidays/travel.

Supplementary dataset

Our test house was used in previous home occupancy research27,28. There is an additional dataset

available for download from Harvard Dataverse named ODD: Occupancy Detection Dataset (Data Citation 3) which contains power meter (mains and heat pump), ambient light and ambient temperature sensor readings (from 10 locations within the house), and outside weather and daylight data. Sensors communicated via a ZigBee mesh network and readings were captured in 15-minute intervals from January 22 to August 29, 2010.

Technical Validation

The meters and data acquisition equipment were manufactured by well known companies that produce meters for industrial and residential installations around the world. Meter calibration was done by the meter manufacture before shipping at the factory. The calibration process is proprietary and we were not privy to the process.

Dataset le preparation

Scripts were created to export the data from the database to nal comma separated values (CSV) les. During this process we checked the integrity of the data. If readings were missing they were algorithmically added24. To note these additions, a plus sign was added to the beginning of each timestamp, which does not affect the programatic conversion from a string to an integer. Our data cleaning scripts (make_AMPds2_power.py and make_AMPds2_pulse.py) work as follows:1. From MySQL export data into CSV les, 1 raw le/meter

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load: WHE

0 2000 6000 10000

load: RSE

0 2000 4000 6000

load: GRE

0 500 1000 1500

load: MHE

0 2000 6000 10000

load: B1E

0 200 400 600 800

load: BME

0 500 1000 1500

load: CWE

0 500 1000 1500

load: DWE

0 200 400 600 800

load: EQE

35 45 55 65

load: FRE

0 200 400 600

load: HPE

0 2000 4000 6000

load: OFE

0 200 400 600 800

load: UTE

20 40 60 80 100

load: WOE

0 1000 2000 3000 4000

load: B2E

0 200 400 600 800

load: CDE

0 1000 3000 5000

load: DNE

20 40 60 80 100

load: EBE

0 500 1000 1500

load: FGE

0 500 1000 1500

load: HTE

20 40 60 80

load: OUE

0 50 150 250

load: TVE

0 100 300 500

load: UNE

0 1000 3000 5000

Power (W)

Figure 3. Load Proles. Histograms showing the load prole for each of the electricity meters.

Monthly Electricity Consumption Over 2 Years

1,100kWh

20

825kWh

Consumption (kWh)

Mean Temp ( )

Step 2 Avg (kWh)

Similar Homes Nearby (kWh)

15

550kWh

10

(pre-smart meter, no data available)

275kWh

5

0kWh

0

Apr 2012 Jul 2012 Oct 2012 Jan 2013 Apr 2013 Jul 2013 Oct 2013 Jan 2014

Monthly Natural Gas Consumption Over 2 Years

11GJ

20

Consumption (GJ) Mean Temp ( )

8GJ

15

6GJ

10

3GJ

5

0GJ

0

Apr 2012 Jul 2012 Oct 2012 Jan 2013 Apr 2013 Jul 2013 Oct 2013 Jan 2014

Figure 4. Monthly Consumption Chart. Monthly consumption charts of both electricity (top) and natural gas (bottom) data received from each utility company.

2. Execute./make_AMPds2_power.py or make_AMPds2_pulse.py.3. Load all raw data CSV les into memory.4. Create empty records that will store clean data.5. For each meter and each CSV row.6. Zero out the seconds in the timestamp.7. Convert the timestamp to a record index i.8. If this record at index i is empty then.9. Convert each measurement to the proper data type.10. Add the measurements to this record.11. For each record in the CDE meter.

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B1E (93 kWh)0.88%

B2E (282 kWh) 2.67% BME (363 kWh) 3.44%CDE (482 kWh) 4.57%

CWE (59 kWh) 0.56% DNE (13 kWh) 0.12% DWE (133 kWh) 1.26%

EBE (168 kWh) 1.59%

EQE (470 kWh) 4.45%

FGE (505 kWh) 4.79%

FRE (1496 kWh) 14.18%

WOE (96 kWh) 0.91% UNE (89 kWh) 0.85%

UTE (619 kWh) 5.86%

Dishwasher (3 kL)1.05%

Furnace (395 m2)32.02%

TVE (476 kWh) 4.51%

Hot Water (132 kL)47.18%

Cold Water (142 kL)51.77%

RSE (2454 kWh) 23.26%

OUE (8 kWh) 0.07%

OFE (421 kWh) 3.99%

Other (839 m2)67.98%

HTE (117 kWh) 1.11%

GRE (568 kWh) 5.38%

HPE (1638 kWh) 15.53%

Figure 5. Yearly Load Consumption Breakdown. The amount of yearly consumption for metered appliances averaged over the two year period of (a) electricity (total of 10,550 kWh), (b) water (total of 277 kl), and(c) natural gas (total of 1235 m3).

12. Fix record by removing phantom 0.4A and 2730VA.13. For timestamp and each meter.14. Use equations (1) and (2) so WHE > = MHE+RSE+GRE.15. If the previous records was missing data then.16. Fill in the missing measurement data.17. Event distribute the accumulation for Pt, Qt, St.18. Save clean records for each meter

Soft-meter calculations

Soft-meter data was calculated during this process. Figure 6 shows how each meter is related to each other and which meters are soft-meters. The main house electricity soft-meter (MHE) is calculated by the formula

MHE WHE - RSE GRE: 1

The unmetered electricity soft-meter (UNE) is calculated by the formula

UNE MHE - B1E B2E BME CDE CWE DNE DWE EBE EQE FGE FRE HPE HTE OFE OUE TVE UTE WOE: 2

To calculate cold water consumption use the formula

CTW WHW - HTW: 3

The calculation of cold water will work over longer periods of time (say one day). Equation (3) will not work over shorter periods of time, because the water meters are pulsing at coarse values of 0.5 l where the time between pulses may cross over multiple minutes where small amounts of water are used.

The dishwasher water soft-meter (DWW) was manually annotated as discussed previously25,26. DWW

consumption followed a very specic pattern of 3 l spurts of water correlating to patterns in the electrical data. In most cases, this was the only water being consumed in the house, making the annotation as simple as copying these readings. When there was simultaneous water use, usually the signal could easily be visually decomposed and/or a nearby reading could be used to infer the proper labelling. There were very few cases where an arbitrary choice between two equally likely labellings had to be made.

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rental suite

RSE

240V 100A bedroom B1E

120V 15A

bedrooms

B2E

120V 15A

basement

BME

120V 15A

clothes dryer

CDE

240V 30A

clothes washer

CWE

120V 15A

dining room

DNE

120V 15A

dishwasher

DWE

120V 15A

workbench

EBE

120V 15A

equipment

EQE

120V 15A

whole-house

WHE

240V 200A

main house

MHE

soft-meter

fridge

FGE

120V 15A

furnace

FRE

120V 15A

heat pump

HPE

240V 40A

hot water

HTE

120V 15A

home office

OFE

120V 15A

outside

OUE

120V 15A

TV, PVR, etc.

TVE

120V 15A

utility

UTE

120V 15A

wall oven

WOE

240V 30A

unmetered

UNE

soft-meter

garage

GRE

240V 60A

hot water

HTW

sub-meter

cold water

CTW

not metered

whole-house

WHG

low pressure

stovetop

STG

not metered

Physical/hardware meter

Software calculated meter

Figure 6. Metering Bus Diagrams. Bus diagrams of how each of the (a) electricity, (b) natural gas, and(c) water meters are connected in relation to each other. For natural gas and water, we show what other appliances/loads exist that consume each resource. This is not done for electricity because there are too many unmetered loads.

whole-house

WHW

400 kPa

dishwasher

DWW

soft-meter

clothes washer

CWW

not metered

unmetered

UNW

not metered

furnace fireplace

FRG

sub-meter

hot water

HTG

not metered

FPG

not metered

Not metered, no data exists

Measurement uncertainty between main & sub-meters

The DENT power meter used is considered revenue class (Class 0.5) which has a very high accuracy, typically better than 1% (o0.5% typical). This meter accuracy classes are governed by two standards organizations: ANSI C12.20 for North America and IEC 62053 elsewhere (see Table 2).

For this class of meter the absolute error is limited to the 0.5% of the full scale reading. Usually, however, the error is somewhat proportional to the reading, with higher readings subject to larger absolute error than lower-valued readings. To make a simple model, we could consider each meter to add a Gaussian error to the true value, with variance proportional to the true value. Each individual meter adds such Gaussian noise. So the variance of the sum of such readings is the sum of individual variances(i.e., proportional to the sum of true values). According to the same model, the main meter makes a Gaussian error with variance proportional to the whole-house power usage, which is the sum of true power values in each individual meter. Hence, the error in the main meter has the same variance as the sum of the readings of individual meters. This is due to the fact that all meters are Class 0.5, so we expect they would have the same constant of proportionality for the variance. If the main meter had a higher class (better rating than 0.5%), then it would produce less uncertainty than the sum of individual meters.

Dataset cleaning

For the electricity data, an additional step was performed. We checked that the whole-house reading was never less than the summation of all sub-meters. If it was then the whole-house reading was changed to be equal to the summation. This can happen because not all meters can be read simultaneously. Each DENT PowerScout 18 meter has 6 three-phase sub-meters (labelled A through F) which can be congured to be 18 single-phase sub-meters. The storage registers within each of the 6 three-phase sub-meters is updated once per second with new measurements. Previously, we discussed the issue of timestamp synchronization and that timestamps between sub-meters could be off by 10 s due to the fact that the meters have a limiting xed baud rate of 9600 bsp. This slight variation in reading time is the cause of having whole-house readings less than the summation of all sub-meters. Suppose, for example, the electricity mains are metered by sub-meter A and the heat pump is metered by sub-meter F. The data acquisition unit would download the measurement data from sub-meter A, then B, and so on, nally to Ftaking a total of 10 s to do. If the heat pump was to turn ON within that 10 s window, then the readings from sub-meter A would not reect the more recent event that would be reected in sub-meter Fthe heat pump turning ON.

The second factor that can contribute to this summation has to do with rounding. Although the meter is quite precise, the measurement values stored in the memory registers are rounded to the nearest whole number for some measurements and tenths of a whole number for other measurements. When we sum up these rounded numbers, they can exceed the whole-house reading. No changes to the whole-house reading were performed if the opposite was true. This is because there were many unmetered loads in the house that could be running at any given time.

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We found an additional problem that affected the metering of the clothes dryer (CDE) PowerScout 18 Unit 1 Meter E. L3 (line 3, for 3-phase loads) was not used but the meter was recording a phantom load for 04.A and between 2730VA. We veried with a multimeter that this should not be the case. There is an additional step to remove the phantom load measurements from the CDE datale. For details, refer to the make_AMPds2_power.py script24.

For the water data, 14 discrepancies were found between the counter and avg_rate. In all cases, the counter should be a cumulative sum of the avg_rate. Of the few times when this was not the case, usually (9 out of 14 times) it was because a pulse failed to be recorded in the avg_rate column. In a few cases (4 out of 14 times), the avg_rate was not a multiple of the pulse size. For both of these types of error, the avg_rate was simply overwritten with the true value derived from the change in the counter. The remaining occurrence (1 out of 14 times) was an accuracy error of 0.001 in the counter. This and all following counter values were adjusted to x this. For details, refer to the make_AMPds2_pulse.py script24.

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Acknowledgements

We would like to thank British Columbia Institute of Technology (BCIT), Electrical and Computer Engineering Technology students and faculty Bob Gill for their collaborations in the past.

Author Contributions

S.M. lead dataset development, information acquisition, data analytics (organized, processed, veried, and collated), and wrote the manuscript. B.E. provided algorithmic annotations of water data, and wrote associated manuscript section. I.V.B. and F.P. provided mentoring, edited and revised the manuscript.

Additional Information

Tables 4 and 5 are only available in the online version of this paper.

Competing nancial interests: The authors declare no competing nancial interests.

How to cite this article: Makonin, S. et al. Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Sci. Data 3:160037 doi: 10.1038/sdata.2016.37 (2016).

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SCIENTIFIC DATA | 3:160037 | DOI: 10.1038/sdata.2016.37 12