Mortar: an open testbed for portable building analytics

Access to large amounts of real-world data has long been a barrier to the development and evaluation of analytics applications for the built environment. Open data sets exist, but they are limited in their span (how much data is available) and context (what kind of data is available and how it is described). Evaluation of such analytics is also limited by how the analytics themselves are implemented, often using hard-coded names of building components, points and locations, or unique input data formats. To advance the methodology for how such analytics are implemented and evaluated, we present Mortar: an open testbed for portable building analytics, currently spanning 90 buildings and containing over 9.1 billion data points. All buildings in the testbed are described using Brick, a recently developed metadata schema, providing rich functional descriptions of building assets and subsystems. We also propose a simple architecture for writing portable analytics applications that are robust to the diversity of buildings and can configure themselves based on context. We demonstrate the utility of Mortar by implementing 11 applications from the literature.

[1]  David E. Culler,et al.  SEDA: an architecture for well-conditioned, scalable internet services , 2001, SOSP.

[2]  Pieter de Wilde,et al.  Energy Waste in Buildings Due to Occupant Behaviour , 2017 .

[3]  V. Stanković,et al.  An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study , 2017, Scientific Data.

[4]  Mani Srivastava,et al.  Brick: Towards a Unified Metadata Schema For Buildings , 2016, BuildSys@SenSys.

[5]  Steven T. Bushby,et al.  A rule-based fault detection method for air handling units , 2006 .

[6]  Eamonn J. Keogh,et al.  On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.

[7]  Steve Doty.  Simultaneous Heating and Cooling—The HVAC Blight , 2009 .

[8]  Zheng Yang,et al.  How Does Building Occupancy Influence Energy Efficiency of HVAC Systems , 2016 .

[9]  Therese Peffer,et al.  Mortar , 2019 .

[10]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[11]  David E. Culler,et al.  Automated Metadata Construction to Support Portable Building Applications , 2015, BuildSys@SenSys.

[12]  Steven R. Schiller,et al.  Evaluation of U.S. Building Energy Benchmarking and Transparency Programs: Attributes, Impacts, and Best Practices , 2017 .

[13]  Julian J. McAuley,et al.  Scrabble: converting unstructured metadata into brick for many buildings , 2017, BuildSys@SenSys.

[14]  Thomas Weng,et al.  BuildingDepot 2.0: An Integrated Management System for Building Analysis and Control , 2013, BuildSys@SenSys.

[15]  Bin Cheng,et al.  Building a Big Data Platform for Smart Cities: Experience and Lessons from Santander , 2015, 2015 IEEE International Congress on Big Data.

[16]  Jeannie R. Albrecht,et al.  Smart * : An Open Data Set and Tools for Enabling Research in Sustainable Homes , 2012 .

[17]  et al.,et al.  Jupyter Notebooks - a publishing format for reproducible computational workflows , 2016, ELPUB.

[18]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[19]  Thierry Talbert,et al.  Black-box modeling of buildings thermal behavior using system identification , 2014 .

[20]  Haimonti Dutta,et al.  NILMTK: an open source toolkit for non-intrusive load monitoring , 2014, e-Energy.

[21]  David E. Culler,et al.  BTrDB: Optimizing Storage System Design for Timeseries Processing , 2016, FAST.

[22]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[23]  Leon R. Glicksman,et al.  Case study results: fault detection in air-handling units in buildings , 2018, Advances in Building Energy Research.

[24]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[25]  Howard S. Brightman,et al.  Developing Baseline Information on Buildings and Indoor Air Quality (BASE '94): Part I -Study Design, Building Selection, and Building Descriptions , 1995 .

[26]  M. A. Rafe Biswas,et al.  Regression analysis for prediction of residential energy consumption , 2015 .

[27]  Clayton Miller,et al.  The Building Data Genome Project: An open, public data set from non-residential building electrical meters , 2017 .

[28]  Wes McKinney,et al.  pandas: a Foundational Python Library for Data Analysis and Statistics , 2011 .

[29]  David E. Culler,et al.  BOSS: Building Operating System Services , 2013, NSDI.

[30]  David E. Culler,et al.  Design and analysis of a query processor for brick , 2017, BuildSys@SenSys.

[31]  Farrokh Janabi-Sharifi,et al.  Review of modeling methods for HVAC systems , 2014 .

[32]  Paul Raftery,et al.  Evaluation of a cost-responsive supply air temperature reset strategy in an office building , 2018 .

[33]  Thomas Weng,et al.  BuildingDepot: an extensible and distributed architecture for building data storage, access and sharing , 2012, BuildSys '12.

[34]  Andrea Monacchi,et al.  GREEND: An energy consumption dataset of households in Italy and Austria , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).