Data mining of space heating system performance in affordable housing

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems in terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. The results from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.

[1]  Franco Chingcuanco,et al.  A microsimulation model of urban energy use: Modelling residential space heating demand in ILUTE , 2012, Comput. Environ. Urban Syst..

[2]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[3]  K. alik,et al.  Validity index for clusters of different sizes and densities , 2011 .

[4]  Chuang Wang,et al.  Air-conditioning usage conditional probability model for residential buildings , 2014 .

[5]  Fu Xiao,et al.  Data mining in building automation system for improving building operational performance , 2014 .

[6]  L. Schipper,et al.  Linking Life-Styles and Energy Use: A Matter of Time? , 1989 .

[7]  Derek Clements-Croome,et al.  Understanding the indoor environment through mining sensory data—A case study , 2007 .

[8]  Jie Zhao,et al.  Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining , 2014 .

[9]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[10]  Benjamin C. M. Fung,et al.  A systematic procedure to study the influence of occupant behavior on building energy consumption , 2011 .

[11]  Hamidreza Zareipour,et al.  Data association mining for identifying lighting energy waste patterns in educational institutes , 2013 .

[12]  Janne Paavilainen,et al.  A unified model for the simulation of oil, gas and biomass space heating boilers for energy estimating purposes. Part I: Model development , 2011 .

[13]  Andrew Kusiak,et al.  Modeling and short-term prediction of HVAC system with a clustering algorithm , 2014 .

[14]  Taehoon Hong,et al.  A decision support model for improving a multi-family housing complex based on CO2 emission from gas energy consumption , 2012 .

[15]  Svetha Venkatesh,et al.  Recognition of emergent human behaviour in a smart home: A data mining approach , 2007, Pervasive Mob. Comput..

[16]  Andrew Kusiak,et al.  Minimizing energy consumption of an air handling unit with a computational intelligence approach , 2013 .

[17]  Danny S. Parker,et al.  Accuracy of the Home Energy Saver Energy Calculation Methodology , 2012 .

[18]  Therese Peffer,et al.  How people use thermostats in homes: A review , 2011, Building and Environment.

[19]  Bryan Urban,et al.  A CASE FOR THERMOSTAT USER MODELS , 2013 .

[20]  Bing Dong,et al.  Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings , 2009 .

[21]  Tianzhen Hong,et al.  Occupancy schedules learning process through a data mining framework , 2015 .

[22]  Da Yan,et al.  Quantitative description and simulation of human behavior in residential buildings , 2012 .

[23]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[24]  Christian Ghiaus,et al.  Overview of IEA/ECBCS/Annex 53 "total energy use in buildings-analysis and evaluation methods" , 2011 .

[25]  Kenneth D. Bailey,et al.  Typologies And Taxonomies , 1994 .

[26]  Tianzhen Hong,et al.  A data-mining approach to discover patterns of window opening and closing behavior in offices , 2014 .

[27]  Kenneth D. Bailey,et al.  Numerical Taxonomy and Cluster Analysis , 1994 .

[28]  Danny S. Parker,et al.  Monitored Energy Use Patterns in Low-Income Housing in a Hot and Humid Climate , 1996 .

[29]  Bo Fan,et al.  Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis , 2014 .

[30]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[31]  Benjamin C. M. Fung,et al.  A novel methodology for knowledge discovery through mining associations between building operational data , 2012 .

[32]  Yi Jiang,et al.  A novel approach for building occupancy simulation , 2011 .

[33]  Karin Engvall,et al.  Interaction between building design, management, household and individual factors in relation to energy use for space heating in apartment buildings , 2014 .

[34]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..