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Workshop of the European Group for Intelligent Computing in Engineering

EG-ICE 2018: Advanced Computing Strategies for Engineering pp 568–593Cite as

Data-Driven, Multi-metric, and Time-Varying (DMT) Building Energy Benchmarking Using Smart Meter Data

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

Abstract

New and emerging data streams, from public databases to smart meter infrastructure, contain valuable information that presents an opportunity to develop more robust data-driven models for benchmarking energy use in buildings. In this paper, we propose a new Data-driven, Multi-metric, and Time-varying (DMT) energy benchmarking framework that utilizes these new data streams to benchmark building energy use across multiple metrics at the daily time scale. High fidelity data from smart meters enables the DMT benchmarking framework to produce daily benchmarking scores and use daily weather data to understand seasonally adjusted performance. Intra-day building efficiency is also investigated by benchmarking buildings across several metrics (e.g., total energy usage, operational energy usage, non-operational energy usage) thereby enabling deeper insights into building operations than traditional yearly benchmarking models. By using quantile regression modeling, the DMT framework can differentiate and understand the main drivers of energy consumption between low and high performing buildings and between building operational states. To illustrate the insights that can be gleaned from the proposed DMT framework, we apply the framework to understand building performance for over 500 schools throughout the state of California. The DMT framework provided insights into how various drivers impacted energy usage for both high and low performing buildings, and results indicated that schools had consistent drivers of energy usage. Overall the DMT framework was designed to be highly interpretable such that it could help bridge the gap between data science and engineering methods thus enabling better decision-making in respect to energy efficiency.

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Author information

Authors and Affiliations

  1. Urban Informatics Lab, Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, 94305, USA

    Jonathan Roth & Rishee K. Jain

Authors
  1. Jonathan Roth
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  2. Rishee K. Jain
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Corresponding author

Correspondence to Rishee K. Jain .

Editor information

Editors and Affiliations

  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

Appendix A

Appendix A

Variable name

Characteristic and Explanation

District type

District Ownership Type Description

Educational type

Educational Option Type Description

Charter School

A “Y” or “N” value indicating whether a school is a charter school in the current academic year.

High grade

Highest grade offered

Enrollment

A total count of K-12 students enrolled (primary or short-term) on Census Day (the first Wednesday in October). These data were submitted to CALPADS as part of the annual Fall 1 submission

Total free meal count

Of the Enrollment (K-12), a total unduplicated count of students who meet household income or categorical eligibility criteria for free meals based on one or more of the following reasons: (1) applying for the National School Lunch Program (NSLP); (2) submitting alternative household income forms; (3) student homeless or migrant statuses in CALPADS; (4) being “directly certified” through CALPADS as participating in California’s food stamp or CalWORKs programs during July - November; or (5) being identified through the weekly CALPADS Foster Matching process or matched by the LEA through the CALPADS online match process as being in Foster Placement or Foster Family Maintenance on Census day. The Free Meal Count (K-12) is not displayed on any CALPADS report; however, this count represents the official Free Meal Count (K-12) for the academic year.

Percent eligible free

The percent of students eligible for free meals. [Free Meal Count (K-12) divided by Enrollment (K-12)]

FRPM count

Of the Enrollment (K-12), a total unduplicated count of students who meet household income or categorical eligibility criteria for free or reduced meals (FRPM) based on one or more of the following reasons: (1) applying for the National School Lunch Program (NSLP); (2) submitting alternative household income forms; (3) student homeless or migrant statuses in CALPADS; (4) being “directly certified” through CALPADS as participating in California’s food stamp or CalWORKs programs during July - November; or (5) being identified through the weekly CALPADS Foster Matching process or matched by the LEA through the CALPADS online match process as being in Foster Placement or Foster Family Maintenance on Census day

Percent eligble FRPM

The percent of students eligible for free or reduced price meals (FRPM). [FRPM Count (K-12) divided by Enrollment (K-12)]

EDP 365

The total cost for the current expense of education

Current expense ADA

Total ADA (average daily attendance) is defined as the total days of student attendance divided by the total days of instruction. This is the total cost of the ADA

Current expense per ADA

The total cost per ADA or the EDP_365 divided by the Current Expense ADA

School type

The type of school as either “High School”, “Unified”, or “Elementary”

Area (sf)

Total area of the school building(s) in square feet

Median Household Income

The median household income for the zip code taken from the US Census Bureau

Temperature max

The maximum temperature recorded during the day in Fahrenheit

Temperature min

The minimum temperature recorded during the day in Fahrenheit

Temperature mean

The average daily temperature for the day in Fahrenheit

Dewpoint

The average daily dewpoint temperature for the day in Fahrenheit

Temperature wetbulb

The average daily wetbulb temperature for the day

Heating degree day (HDD)

Number of degrees that the day’s average temperature was below 65° Fahrenheit

Cooling degree day (CDD)

The number of degrees that the day’s average temperature was above 65° Fahrenheit

Total precipitation

The total amount of precipitation (water equivalent) in inches

Standard pressure

The total standard pressure for the day in Hg

Result speed

The resulting wind speed for the day

Average wind speed

The daily average wind speed in miles per hour

Max5speed

The max speed of wind with a duration of 5 min

Max2speed

The max speed of wind with a duration of 2 min

Temperature mean squared

The average daily temperature squared

Heating degree day squared (HDD_2)

The heating degree day (HDD) squared

Cooling degree day squared (CDD_2)

The cooling degree day (CDD) squared

Temperature mean natural log

The natural log transformation of the average daily temperature

Weekend

Dummy variable where “1” is a weekend and “0” is a weekday

Enrollment per area

The total enrollment per unit area (Students per square foot)

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Roth, J., Jain, R.K. (2018). Data-Driven, Multi-metric, and Time-Varying (DMT) Building Energy Benchmarking Using Smart Meter Data. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_30

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