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Extracting knowledge from building-related data — A data mining framework

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  • Advances in Modeling and Simulation Tools
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Abstract

Energy management systems provide an opportunity to collect vast amounts of building-related data. The data contain abundant knowledge about the interactions between a building’s energy consumption and the influencing factors. It is highly desirable that the hidden knowledge can be extracted from the data in order to help improve building energy performance. However, the data are rarely translated into useful knowledge due to their complexity and a lack of effective data analysis techniques. This paper first conducts a comprehensive review of the commonly used data analysis methods applied to building-related data. Both the strengths and weaknesses of each method are discussed. Then, the critical analysis of the previous solutions to three fundamental problems of building energy performance improvement that remain significant barriers is performed. Considering the limitations of those commonly used data analysis methods, data mining techniques are proposed as a primary tool to analyze building-related data. Moreover, a data analysis process and a data mining framework are proposed that enable building-related data to be analyzed more efficiently. The process refers to a series of sequential steps in analyzing data. The framework includes different data mining techniques and algorithms, from which a set of efficient data analysis methodologies can be developed. The applications of the process and framework to two sets of collected data demonstrate their applicability and abilities to extract useful knowledge. Particularly, four data analysis methodologies were developed to solve the three problems. For demonstration purposes, these methodologies were applied to the collected data. These methodologies are introduced in the published papers and are summarized in this paper. More extensive investigations will be performed in order to further evaluate the effectiveness of the framework.

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References

  • Abu Hamdeh NH, Al-Muhtaseb MTA (2010). Optimization of solar adsorption refrigeration system using experimental and statistical techniques. Energy Conversion and Management, 51: 1610–1615.

    Article  Google Scholar 

  • Al-Mumin A, Khattab O, Sridhar G (2003). Occupants’ behavior and activity patterns influencing the energy consumption in the Kuwaiti residences. Energy and Buildings, 35: 549–559.

    Article  Google Scholar 

  • Balaras CA, Dascalaki E, Gaglia A, Droutsa K (2003). Energy conservation potential, HVAC installations and operational issues in Hellenic airports. Energy and Buildings, 35: 1105–1120.

    Article  Google Scholar 

  • Balta MT, Dincer I, Hepbasli A (2010). Performance and sustainability assessment of energy options for building HVAC applications. Energy and Buildings, 42: 1320–1328.

    Article  Google Scholar 

  • Bi Y, Chen L, Sun F (2008). Heating load, heating-load density and COP optimizations of an endoreversible air heat-pump. Applied Energy, 85: 607–617.

    Article  Google Scholar 

  • Cabena P, Hadjinian P, Stadler R, Verhees J, Zanasi A (1998). Discovering Data Mining: From Concept to Implementation. Upper Saddle River, USA: Prentice Hall.

    Google Scholar 

  • Cao LB, Yu PS, Zhang CQ, Zhang HF (2009). Data Mining for Business Applications. New York: Springer.

    Book  Google Scholar 

  • Chekir N, Bellagi A (2011). Performance improvement of a butane/octane absorption chiller. Energy, 36: 6278–6284.

    Article  Google Scholar 

  • Chen S, Yoshino H, Levine MD, Li Z (2009a). Contrastive analyses on annual energy consumption characteristics and the influence mechanism between new and old residential buildings in Shanghai, China, by the statistical methods. Energy and Buildings, 41: 1347–1359.

    Article  Google Scholar 

  • Chen S, Yoshino H, Li N (2009b). Statistical analyses on summer energy consumption characteristics of residential buildings in some cities of China. Energy and Buildings, 42: 136–146.

    Article  Google Scholar 

  • Chung W, Hui YV (2009). A study of energy efficiency of private office buildings in Hong Kong. Energy and Buildings, 41: 696–701.

    Article  Google Scholar 

  • Cios KJ (2007). Data Mining: A Knowledge Discovery Approach. New York: Springer.

    MATH  Google Scholar 

  • de la Flor FJS, Lissén JMS, Domínguez Sá (2006). A new methodology towards determining building performance under modified outdoor conditions. Building and Environment, 41: 1231–1238.

    Article  Google Scholar 

  • Delgado M, Sánchez D, MartIn-Bautista MJ, Vila M (2001). Mining association rules with improved semantics in medical databases. Artificial Intelligence in Medicine, 21: 241–245.

    Article  Google Scholar 

  • Deng SM, Burnett J (2000). A study of energy performance of hotel buildings in Hong Kong. Energy and Buildings, 31: 7–12.

    Article  Google Scholar 

  • Dong B, Cao C, Lee SE (2005a). Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings, 37: 545–553.

    Article  Google Scholar 

  • Dong B, Lee SE, Sapar MH (2005b). A holistic utility bill analysis method for baselining whole commercial building energy consumption in Singapore. Energy and Buildings, 37: 167–174.

    Article  Google Scholar 

  • Ekici BB, Aksoy UT (2009). Prediction of building energy consumption by using artificial neural networks. Advances in Engineering Software, 40: 356–362.

    Article  MATH  Google Scholar 

  • Emery AF, Kippenhan CJ (2006). A long-term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards. Energy, 31: 677–693.

    Article  Google Scholar 

  • Escrivá-Escrivá G, álvarez-Bel C, Roldán-Blay C, Alcázar-Ortega M (2011). New artificial neural network prediction method for electrical consumption forecasting based on building end-uses. Energy and Buildings, 43: 3112–3119.

    Article  Google Scholar 

  • Eskin N, Türkmen H (2008). Analysis of annual heating and cooling energy requirements for office buildings in different climates in Turkey. Energy and Buildings, 40: 763–773.

    Article  Google Scholar 

  • Freire RZ, Oliveira GHC, Mendes N (2008). Development of regression equations for predicting energy and hygrothermal performance of buildings. Energy and Buildings, 40: 810–820.

    Article  Google Scholar 

  • Gaitani N, Lehmann C, Santamouris M, Mihalakakou G, Patargias P (2010). Using principal component and cluster analysis in the heating evaluation of the school building sector. Applied Energy, 87: 2079–2086.

    Article  Google Scholar 

  • Georgilakis PS, Gioulekas AT, Souflaris AT (2007). A decision tree method for the selection of winding material in power transformers. Journal of Materials Processing Technology, 181: 281–285.

    Article  Google Scholar 

  • Ghiaus C (2006). Experimental estimation of building energy performance by robust regression. Energy and Buildings, 38: 582–587.

    Article  Google Scholar 

  • Givoni B, Krüger EL (2003). An attempt to base prediction of indoor temperatures of occupied houses on their thermo-physical properties. In: Proceedings of the 18th International Passive and Low Energy Architecture Conference (PLEA’03), Santiago, Chile.

    Google Scholar 

  • Han J, Kamber M (2006). Data Mining Concepts and Techniques, 2nd edn. San Francisco: Elsevier.

    MATH  Google Scholar 

  • Hand D, Mannila H, Smyth P (2001). Principles of Data Mining. Cambridge, USA: MIT Press.

    Google Scholar 

  • Hou ZJ, Lian ZW, Yao Y, Yuan XJ (2006). Cooling-load prediction by the combination of rough set theory and an artificial neural- network based on data-fusion technique. Applied Energy, 83: 1033–1046.

    Article  Google Scholar 

  • Hsu CH (2009). Data mining to improve industrial standards and enhance production and marketing: An empirical study in apparel industry. Expert Systems with Applications, 36: 4185–4191.

    Article  Google Scholar 

  • Jiao J, Zhang Y (2005). Product portfolio identification based on association rule mining. Computer-Aided Design, 37: 149–172.

    Article  Google Scholar 

  • Jiménez MJ, Heras MR (2005). Application of multi-output ARX models for estimation of the U and g values of building components in outdoor testing. Solar Energy, 79: 302–310.

    Article  Google Scholar 

  • Kim YS, Kim KS (2007). Simplified energy prediction method accounting for part-load performance of chiller. Building and Environment, 42: 507–515.

    Article  Google Scholar 

  • Krüger EL, Givoni B (2004). Predicting thermal performance in occupied dwellings. Energy and Buildings, 36: 301–307.

    Article  Google Scholar 

  • Kyrö R, Heinonen J, Säynäjoki A, Junnila S (2011). Occupants have little influence on the overall energyconsumption in district heated apartment buildings. Energy and Buildings, 43: 3484–3490.

    Article  Google Scholar 

  • Lam JC, Hui SCM, Chan ALS (1997). Regression analysis of high-rise fully air-conditioned office buildings. Energy and Buildings, 26: 189–197.

    Article  Google Scholar 

  • Lam JC, Wan KKW, Cheung KL (2009). An analysis of climatic influences on chiller plant electricity consumption. Applied Energy, 86: 933–940.

    Article  Google Scholar 

  • Lior R, Oded M (2008). Data Mining with Decision Trees: Theory and Applications. Singapore: World Scientific.

    MATH  Google Scholar 

  • Schweiker M, Shukuya M (2010). Comparative effects of building envelope improvements and occupant behavioural changes on the exergy consumption for heating and cooling. Energy Policy: 2976–2986

    Google Scholar 

  • Masoso OT, Grobler LJ (2010). The dark side of occupants’ behaviour on building energy use. Energy and Buildings, 42: 173–177.

    Article  Google Scholar 

  • Murakami S, Akabayashi S, Inoue T, Yoshino H, Hasegawa K, Yuasa K, et al. (2006). Energy consumption for residential buildings in Japan. Architectural Institute of Japan, Maruzen Corp., Available online at http://tkkankyo.eng.niigata-u.ac.jp/HP/HP/database/index.htm.

    Google Scholar 

  • Olofsson T, Andersson S (2001). Long-term energy demand predictions based on short-term measured data. Energy and Buildings, 33: 85–91.

    Article  Google Scholar 

  • Ourghi R, Al-Anzi A, Krarti M (2007). A simplified analysis method to predict the impact of shape on annual energy use for office buildings. Energy Conversion and Management, 48: 300–305.

    Article  Google Scholar 

  • Ouyang J, Hokao K (2009). Energy-saving potential by improving occupants’ behavior in urban residential sector in Hangzhou City, China. Energy and Buildings, 41: 711–720.

    Article  Google Scholar 

  • Pan H, Li J, Zhang W (2007). Incorporating domain knowledge into medical image clustering. Applied Mathematics and Computation, 185: 844–856.

    Article  MATH  Google Scholar 

  • Pérez-Lombard L, Ortiz J, Coronel JF, Maestre IR (2011). A review of HVAC systems requirements in building energy regulations. Energy and Buildings, 43: 255–268.

    Article  Google Scholar 

  • Priyadarsini R, Xuchao W, Eang LS (2009). A study on energy performance of hotel buildings in Singapore. Energy and Buildings, 41: 1319–1324.

    Article  Google Scholar 

  • Li Q, Meng Q, Cai J, Yoshino H, Mochida A (2009). Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86: 2249–2256.

    Article  Google Scholar 

  • Quinlan JR (1986). Induction of decision trees. Machine Learning, 1: 81–106.

    Google Scholar 

  • Quinlan JR (1986). Induction of decision trees. Machine Learning, 1: 81–106.

    Google Scholar 

  • RapidMiner (2012): http://rapid-i.com/content/view/181/190/

  • Santamouris M, Mihalakakou G, Patargias P, Gaitani N, Sfakianaki K, Papaglastra M, Pavlou C, Doukas P, Primikiri E, Geros V, Assimakopoulos MN, Mitoula R, Zerefos S (2007). Using intelligent clustering techniques to classify the energy performance of school buildings. Energy and Buildings, 39: 45–51.

    Article  Google Scholar 

  • Santin G, Itard L, Visscher H (2009). The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock. Energy and Buildings, 41: 1223–1232.

    Article  Google Scholar 

  • Tonooka Y, Liu J, Kondou Y, Ning Y, Fukasawa O (2006). A survey on energy consumption in rural households in the fringes of Xian city. Energy and Buildings, 38: 1335–1342.

    Article  Google Scholar 

  • Tso GKF, Yau KKW (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32: 1761–1768.

    Article  Google Scholar 

  • Waltrich PJ, Barbosa Jr JR, Hermes CJL (2011). COP-based optimization of accelerated flow evaporators for household refrigeration applications. Applied Thermal Engineering, 31: 129–135.

    Article  Google Scholar 

  • Wang EY, Fung AS, Qi CY, Leong WH (2012). Performance prediction of a hybrid solar ground-source heat pump system. Energy and Buildings, 47: 600–611.

    Article  Google Scholar 

  • Wood CJ, Liu H, Riffat SB (2010). An investigation of the heat pump performance and ground temperature of a piled foundation heat exchanger system for a residential building. Energy, 35: 4932–4940.

    Article  Google Scholar 

  • Wu S, Clements-Croome D (2007). Understanding the indoor environment through mining sensory data—A case study. Energy and Buildings, 39: 1183–1191.

    Article  Google Scholar 

  • Yao Y, Lian ZW, Hou ZJ, Liu W (2006). An innovative air-conditioning load forecasting model based on RBF neural network and combined residual error correction. International Journal of Refrigeration, 29: 528–538.

    Article  Google Scholar 

  • Yu Z, Haghighat F, Fung BCM, Yoshino H (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42: 1637–1646.

    Article  Google Scholar 

  • Yu Z, Fung BCM, Haghighat F, Yoshino H, Morofsky E (2011a). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 43: 1409–1417.

    Article  Google Scholar 

  • Yu Z, Haghighat F, Fung BCM, Yoshino H, Morofsky E (2011b). A methodology for identifying and improving occupant behavior in residential buildings. Energy, 36: 6596–6608.

    Article  Google Scholar 

  • Yu Z, Haghighat F, Fung BCM, Zhou L, Morofsky E (2012). A novel methodology for knowledge discovery through mining associations between building operational data. Energy and Buildings, 47: 430–440.

    Article  Google Scholar 

  • Yun GY, Kim H, Kim JT (2012). Effects of occupancy and lighting use patterns on lighting energy consumption. Energy and Buildings, 46: 152–158.

    Article  Google Scholar 

  • Zhang Q (2004). Residential energy consumption in China and its comparison with Japan, Canada, and USA. Energy and Buildings, 36: 1217–1225.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec, H3G 1M8, Canada

    Zhun Yu & Fariborz Haghighat

  2. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Quebec, H3G 1M8, Canada

    Benjamin C. M. Fung

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  1. Zhun Yu
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  2. Benjamin C. M. Fung
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Correspondence to Fariborz Haghighat.

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Yu, Z., Fung, B.C.M. & Haghighat, F. Extracting knowledge from building-related data — A data mining framework. Build. Simul. 6, 207–222 (2013). https://doi.org/10.1007/s12273-013-0117-8

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  • DOI: https://doi.org/10.1007/s12273-013-0117-8

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