Login | Register

Data Mining Frameworks for Energy Consumption Reduction of Existing Buildings


Data Mining Frameworks for Energy Consumption Reduction of Existing Buildings

Ashouri Sanjani, Milad (2019) Data Mining Frameworks for Energy Consumption Reduction of Existing Buildings. PhD thesis, Concordia University.

[thumbnail of Ashouri_Sanjani_PhD_S2020.pdf]
Text (application/pdf)
Ashouri_Sanjani_PhD_S2020.pdf - Accepted Version


Many technical solutions have been developed to reduce buildings’ energy consumption, but limited efforts have been made to adequately address the role or action of building occupants in this process. On the other side, Building Management System (BMS) monitors the performance of buildings by recording the data to improve the building operation, control systems and maintenance. Usually, BMS produces a large volume of data throughout the year including information with regard to patterns of energy use, occupant behavior, etc. The availability of this huge data has created an opportunity to extract information to improve the building energy performance through leveraging powerful data analytic tools.
The objectives defined in this thesis lie in developing methodologies to find energy saving opportunities by analyzing data coming from occupants’ energy consumption. Three tasks are defined in this thesis. The first task is to provide a recommender system to alert the occupants to take certain measures in order to reduce their energy consumption through end-use loads. Therefore, the quantification of potential savings is provided upon following recommendations. The proposed methodology is also capable to detect the energy saving measures performed by occupants. The second task focuses on a systematic comparison procedure between the buildings to make the occupants aware of their rank among other buildings and hence give them clues on how to improve their performance. The third task focuses on developing a framework to create a reference building acting as a reference for a given building. Therefore, the given building can be compared against its reference building. Potential savings are given to the given building along with directions how to achieve them. The results show successfulness of developed methodologies in finding energy saving opportunities through modifying occupant behavior.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Ashouri Sanjani, Milad
Institution:Concordia University
Degree Name:Ph. D.
Program:Building Engineering
Date:September 2019
Thesis Supervisor(s):Haghighat, Fariborz and Fung, Benjamin
Keywords:Data Mining Energy smart building
ID Code:986091
Deposited By: Milad Ashouri Sanjani
Deposited On:29 Jun 2021 23:25
Last Modified:29 Jun 2021 23:25


[1] “International Energy Agency, Key World Energy Statistics,” 2006.
[2] “A review on buildings energy consumption information,” Energy Build., vol. 40, no. 3, pp. 394–398, Jan. 2008.
[3] N. R. Canada, “Energy Efficiency Trends in Canada 1990 to 2013,” pp. 11–18, 2013.
[4] D. Bourgeois, “Detailed occupancy prediction, occupancy-sensing control and advanced behavioral modeling within whole-building energy simulation,” l’Universite Laval, Quebec, 2005.
[5] V. Dhar, “Data Science and Prediction,” Commun. ACM, vol. 56, no. 12, pp. 64–73, Dec. 2013.
[6] C. Fan, F. Xiao, and C. Yan, “A framework for knowledge discovery in massive building automation data and its application in building diagnostics,” Autom. Constr., vol. 50, no. C, pp. 81–90, Feb. 2015.
[7] P. Waide, J. Ure, N. Karagianni, G. Smith, and B. Bordass, “The scope for energy and CO2 savings in the EU through the use of building automation technology,” Final Rep. Eur. Copp. Inst., 2013.
[8] A. Capozzoli, D. Grassi, M. S. Piscitelli, and G. Serale, “Discovering knowledge from a residential building stock through data mining analysis for engineering sustainability,” Energy Procedia, vol. 83, pp. 370–379, 2015.
[9] I. Khan, A. Capozzoli, S. P. Corgnati, and T. Cerquitelli, “Fault Detection Analysis of Building Energy Consumption Using Data Mining Techniques,” Energy Procedia, vol. 42, pp. 557–566, Jan. 2013.
[10] A. Capozzoli, F. Lauro, and I. Khan, “Fault detection analysis using data mining techniques for a cluster of smart office buildings,” Expert Syst. Appl., vol. 42, no. 9, pp. 4324–4338, Jun. 2015.
[11] Z. Yu, B. C. M. Fung, F. Haghighat, H. Yoshino, and E. Morofsky, “A systematic procedure to study the influence of occupant behavior on building energy consumption,” Energy Build., vol. 43, no. 6, pp. 1409–1417, 2011.
[12] and J. P. J. Han, M. Kamber, Data mining, concepts and techniques 3rd ed, 3rd ed. Elsevier, 2012.
[13] D. J. Hand, P. Smyth, and H. Mannila, Principles of Data Mining. Cambridge, MA, USA: MIT Press, 2001.
[14] Z. Yu, F. Haghighat, B. C. M. Fung, and H. Yoshino, “A decision tree method for building energy demand modeling,” Energy Build., vol. 42, no. 10, pp. 1637–1646, 2010.
[15] Z. Du and X. Jin, “Detection and diagnosis for sensor fault in HVAC systems,” Energy Convers. Manag., vol. 48, no. 3, pp. 693–702, Mar. 2007.
[16] Y. Hu, H. Chen, G. Li, H. Li, R. Xu, and J. Li, “A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method,” Energy Build., vol. 112, pp. 270–278, Jan. 2016.
[17] S. Wang and F. Xiao, “AHU sensor fault diagnosis using principal component analysis method,” Energy Build., vol. 36, no. 2, pp. 147–160, Feb. 2004.
[18] X. Cipriano, A. Vellido, J. Cipriano, J. Martí-Herrero, and S. Danov, “Influencing factors in energy use of housing blocks: a new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects,” Energy Effic., vol. 10, no. 2, pp. 359–382, Apr. 2017.
[19] E. Wang, “Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach,” Appl. Energy, vol. 146, pp. 92–103, 2015.
[20] A. Rakotomamonjy, “Variable Selection Using SVM-based Criteria,” J. Mach. Learn. Res., vol. 3, pp. 1357–1370, 2003.
[21] J.-S. Chou, Y.-C. Hsu, and L.-T. Lin, “Smart meter monitoring and data mining techniques for predicting refrigeration system performance,” Expert Syst. Appl., vol. 41, no. 5, pp. 2144–2156, 2014.
[22] S. Wang and F. Xiao, “Detection and diagnosis of AHU sensor faults using principal component analysis method,” Energy Convers. Manag., vol. 45, no. 17, pp. 2667–2686, Oct. 2004.
[23] M. Peña, F. Biscarri, J. I. Guerrero, I. Monedero, and C. León, “Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach,” Expert Syst. Appl., vol. 56, pp. 242–255, Sep. 2016.
[24] Z. Du, B. Fan, X. Jin, and J. Chi, “Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis,” Build. Environ., vol. 73, pp. 1–11, Mar. 2014.
[25] J. M. Abreu, F. C. Pereira, and P. Ferrão, “Using pattern recognition to identify habitual behavior in residential electricity consumption,” Energy Build., vol. 49, pp. 479–487, 2012.
[26] F. Xiao and C. Fan, “Data mining in building automation system for improving building operational performance,” Energy Build., vol. 75, pp. 109–118, 2014.
[27] S. D’Oca and T. Hong, “A data-mining approach to discover patterns of window opening and closing behavior in offices,” Build. Environ., vol. 82, pp. 726–739, 2014.
[28] C. M. R. do Carmo and T. H. Christensen, “Cluster analysis of residential heat load profiles and the role of technical and household characteristics,” Energy Build., vol. 125, pp. 171–180, 2016.
[29] X. Liang, T. Hong, and G. Q. Shen, “Occupancy data analytics and prediction: a case study,” Build. Environ., vol. 102, pp. 179–192, 2016.
[30] M. Saarikoski, “A data mining approach to indoor environment quality assessment: A study on five detached houses in Finland,” 2016.
[31] Z. Yu, B. C. M. Fung, and F. Haghighat, “Extracting knowledge from building-related data—A data mining framework,” in Building Simulation, 2013, vol. 6, no. 2, pp. 207–222.
[32] S. D’Oca, S. Corgnati, and T. Hong, “Data Mining of Occupant Behavior in Office Buildings,” Energy Procedia, vol. 78, pp. 585–590, 2015.
[33] M. Sameti and F. Haghighat, “Optimization of 4th generation distributed district heating system: Design and planning of combined heat and power,” Renew. Energy, vol. 130, pp. 371–387, Jan. 2019.
[34] T. Hong, D. Yan, S. D’Oca, and C. Chen, “Ten questions concerning occupant behavior in buildings: The big picture,” Build. Environ., vol. 114, pp. 518–530, Mar. 2017.
[35] S. D’Oca and T. Hong, “Occupancy schedules learning process through a data mining framework,” Energy Build., vol. 88, pp. 395–408, 2015.
[36] K. Sun, D. Yan, T. Hong, and S. Guo, “Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration,” Build. Environ., vol. 79, pp. 1–12, 2014.
[37] Ashrea, “‘Energy Standard for Buildings except Low-RiseResidential Buildings, 90.1,’” 2004.
[38] A. Capozzoli, M. S. Piscitelli, A. Gorrino, I. Ballarini, and V. Corrado, “Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings,” Sustain. Cities Soc., vol. 35, pp. 191–208, Nov. 2017.
[39] Y. Wang and L. Shao, “Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning,” Build. Environ., vol. 114, pp. 106–117, Mar. 2017.
[40] I. Kastner and E. Matthies, “Implementing web-based interventions to promote energy efficient behavior at organizations – a multi-level challenge,” J. Clean. Prod., vol. 62, pp. 89–97, Jan. 2014.
[41] A. Meinke, M. Hawighorst, A. Wagner, J. Trojan, and M. Schweiker, “Comfort-related feedforward information: occupants’ choice of cooling strategy and perceived comfort,” Build. Res. Inf., vol. 45, no. 1–2, pp. 222–238, Feb. 2017.
[42] C. Fischer, “Feedback on household electricity consumption: a tool for saving energy?,” Energy Effic., vol. 1, no. 1, pp. 79–104, Feb. 2008.
[43] Z. Yu, J. Li, H. Q. Li, J. Han, and G. Q. Zhang, “A Novel Methodology for Identifying Associations and Correlations Between Household Appliance Behaviour in Residential Buildings,” Energy Procedia, vol. 78, pp. 591–596, Nov. 2015.
[44] M. Mohanraj, S. Jayaraj, and C. Muraleedharan, “Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review,” Renew. Sustain. Energy Rev., vol. 16, no. 2, pp. 1340–1358, 2012.
[45] M. Y. Haller et al., “Dynamic whole system testing of combined renewable heating systems–The current state of the art,” Energy Build., vol. 66, pp. 667–677, 2013.
[46] J. M. Belman-Flores and S. Ledesma, “Statistical analysis of the energy performance of a refrigeration system working with R1234yf using artificial neural networks,” Appl. Therm. Eng., vol. 82, pp. 8–17, May 2015.
[47] D. J. Swider, “A comparison of empirically based steady-state models for vapor-compression liquid chillers,” Appl. Therm. Eng., vol. 23, no. 5, pp. 539–556, Apr. 2003.
[48] E. Mocanu, P. H. Nguyen, M. Gibescu, and W. L. Kling, “Deep learning for estimating building energy consumption,” Sustain. Energy, Grids Networks, vol. 6, pp. 91–99, Jun. 2016.
[49] T. Hong and H.-W. Lin, “Occupant behavior: impact on energy use of private offices,” in ASim 2012 - 1st Asia conference of International Building Performance Simulation Association, 2013.
[50] S. Murakami et al., “Energy consumption for residential buildings in Japan,” Archit. Inst. Japan, Maruz. Corp, 2006.
[51] M. Ashouri, F. Haghighat, B. C. M. Fung, A. Lazrak, and H. Yoshino, “Development of Building Energy Saving Advisory: A Data Mining Approach,” Energy Build., May 2018.
[52] C. Nguyen, “Computer-aided Nonlinear Analysis of Microwave and Millimeter Wave Amplifiers and Mixers,” University of Central Florida, Orlando, FL, USA, 1991.
[53] L. Rokach and O. Maimon, “The Data Mining and Knowledge Discovery Handbook: A Complete Guide for Researchers and Practitioners.” New York: Springer, 2005.
[54] Scikit-Learn, “Scikit-Learn Documentation.” [Online]. Available: http://scikit-learn.org/stable/modules/clustering.html.
[55] “Python (3.5).” [Online]. Available: https://www.python.org/.
[56] S. M. C. Magalhães, V. M. S. Leal, and I. M. Horta, “Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior,” Energy Build., vol. 151, pp. 332–343, Sep. 2017.
[57] Y. Zhang, X. Bai, F. P. Mills, and J. C. V. Pezzey, “Rethinking the role of occupant behavior in building energy performance: A review,” Energy Build., vol. 172, pp. 279–294, Aug. 2018.
[58] S. Bhattacharjee and G. Reichard, “Socio-Economic Factors Affecting Individual Household Energy Consumption: A Systematic Review,” ASME 2011 5th Int. Conf. Energy Sustain., no. 54686, pp. 891–901, 2011.
[59] S. Walfish, “A Review of Statistical Outlier MethodsTitle,” Pharm. Technol., vol. 11, no. 30, pp. 82–88, 2006.
[60] C. Fu, J. Zheng, J. Zhao, and W. Xu, “Application of grey relational analysis for corrosion failure of oil tubes,” Corros. Sci., vol. 43, no. 5, pp. 881–889, 2001.
[61] C. Fan, F. Xiao, Z. Li, and J. Wang, “Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review,” Energy Build., vol. 159, pp. 296–308, Jan. 2018.
[62] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
[63] Z. (Jerry) Yu, F. Haghighat, B. C. M. Fung, E. Morofsky, and H. Yoshino, “A methodology for identifying and improving occupant behavior in residential buildings,” Energy, vol. 36, no. 11, pp. 6596–6608, Nov. 2011.
[64] M. Ashouri, F. Haghighat, B. C. M. Fung, and H. Yoshino, “Development of a ranking procedure for energy performance evaluation of buildings based on occupant behavior,” Energy Build., vol. 183, pp. 659–671, Jan. 2019.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top