By a News Reporter-Staff News Editor at Computer Weekly News -- Current study results on Information Technology - Data Mining have been published. According to news originating from Beijing, People’s Republic of China, by VerticalNews correspondents, research stated, “Residential buildings in northern China have annually consumed over 4.4% of China’s total energy by space heating, and the proportion is still growing. A key task when making an energy conservation guide is to identify the parameters that have significant impacts on the building heating energy consumption.”

Funders for this research include Engineering and Physical Sciences Research Council, National Natural Science Foundation of China.

Our news journalists obtained a quote from the research from Tsinghua University, “The traditional methods for this task, such as optimization-simulation, regression, and artificial neural network (ANN) methods, either require considerable amounts of computing capacity and time, or present the results in complex equations or networks that are difficult to understand. This study proposed a data mining approach to analyze the correlation between a building’s heating energy consumption and its physical & heating system parameters, based on a field survey of 5615 households from 116 buildings in Tianjin, a city in China’s cold winter climate region. The box-plot method was used to detect outliers in the original database, and determine the attributes. The information gain ratio calculation algorithm was applied to rank the correlation between the heating energy consumption per unit area (HECPA) and 16 (19) attributes on both household and building scales. Finally, the C4.5 decision tree classifier was used to model the correlations and output the classification rules. The results indicated that the window heat-transmission coefficient and type of heating-terminal were the two attributes that most significantly affected the heating energy consumption on both scales. According to the classification rules, a higher window heat-transmission coefficient for a household usually resulted in a higher HECPA level, while buildings that used floor heating as their heating-terminals had a high probability of consuming more heating energy than those that used radiators. Nevertheless, a higher window-to-wall ratio may not necessarily result in more heating energy consumption.”

According to the news editors, the research concluded: “The main contribution of this study was the development of a promising approach that could assist in quickly understanding the hidden correlation between heating energy consumption and related factors through a massive amount of collected data.”

For more information on this research see: Analysis of correlation between actual heating energy consumption and building physics, heating system, and room position using data mining approach. Energy and Buildings , 2018;166():73-82. Energy and Buildings can be contacted at: Elsevier Science Sa, PO Box 564, 1001 Lausanne, Switzerland. (Elsevier - www.elsevier.com; Energy and Buildings - http://www.journals.elsevier.com/energy-and-buildings/)

The news correspondents report that additional information may be obtained from B.R. Lin, Tsinghua Univ, Sch Architecture, Beijing 100084, People’s Republic of China. Additional authors for this research include H. Zhou, J.Q. Qi, L.H. Zheng and Z.C. Zhang.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.enbuild.2018.01.042. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2018, NewsRx LLC

CITATION: (2018-05-09), Studies from Tsinghua University Yield New Information about Data Mining (Analysis of correlation between actual heating energy consumption and building physics, heating system, and room position using data mining approach), Computer Weekly News, 820, ISSN: 1944-1606, BUTTER® ID: 015632429

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