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This paper aims to provide a methodology for identifying energy consumption patterns in a large dataset of flats using data mining. The authors analyzed energy usage data from over 92,000 flats to classify them and establish rules for decision making. The classifications can help designers and planners identify major causes of high energy consumption and evaluate potential impacts of retrofitting actions. The paper recommends further investigating the dataset to improve classification accuracy and studying how building owner decisions influence applying proposed retrofits.

Engineering

Paper Review
Tittle
Data mining for energy analysis of a large data set of flats
Presented By:
Name: Naresh Landman,
Student ID : 45288 ,
Name : Sushanth reddy chilukuri,
Student ID :44947,
Name : Arbaaz khan
Student ID :44584

Purpose of this paper
 Providing methodology based on data mining so as to help in the setting of
rules which will be used in decision-making.
 These decisions are based on identifying energy consumption patterns of large
data set of flats.
 The decisions will also be used in evaluation of potential achievable effects of
by retrofitting actions.

Methods and methodology
 92906 flats were involved in the classification process conducted in this
paper.
 The information provided was representative, this was due to the large
dimension of the data set adopted.
 With the classification criteria used having a basis of statistical variables, this
method can be adapted easily on any dataset.

Benefits from the methodology.
The methods used in this paper will greatly benefit designers and authority
planners who are in need of the following:
 Identifying the major causes of high energy consumption and suggest rules for
incentivizing energy retrofit actions(Fracastoro and Serraino, 2011)
 Evaluating benchmark values for purposes of driving policies for building
design approaches which are sustainable. (Capozzoli et al., 2015;

Further areas of study recommended by
this paper.
 Additional data set investigation so as to lower the limit of the error rate of
the classification tree.
 The influence the decisions of the owners of buildings have on the application
of proposed retrofit actions.

References
1) Capozzoli A, Grassi D, Piscitelli MS and Serale G (2015b) Discovering knowledge
from a residential building stock through data mining analysis for engineering
sustainability. Energy Procedia 83: 370–379, http://dx.doi.org/10.1016/j.
egypro.2015.12.212.
2) Galiotto N, Heiselberg P and Knudstrup MA (2015) The Integrated Renovation
Process: application to family homes. Proceedings of the Institution of Civil
Engineers –Engineering Sustainability 168(6): 245–257, http://dx.doi.org/
10.1680/ensu.14.00020.

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1. Paper Review Tittle Data mining for energy analysis of a large data set of flats Presented By: Name: Naresh Landman, Student ID : 45288 , Name : Sushanth reddy chilukuri, Student ID :44947, Name : Arbaaz khan Student ID :44584

2. Purpose of this paper  Providing methodology based on data mining so as to help in the setting of rules which will be used in decision-making.  These decisions are based on identifying energy consumption patterns of large data set of flats.  The decisions will also be used in evaluation of potential achievable effects of by retrofitting actions.

3. Methods and methodology  92906 flats were involved in the classification process conducted in this paper.  The information provided was representative, this was due to the large dimension of the data set adopted.  With the classification criteria used having a basis of statistical variables, this method can be adapted easily on any dataset.

4. Benefits from the methodology. The methods used in this paper will greatly benefit designers and authority planners who are in need of the following:  Identifying the major causes of high energy consumption and suggest rules for incentivizing energy retrofit actions(Fracastoro and Serraino, 2011)  Evaluating benchmark values for purposes of driving policies for building design approaches which are sustainable. (Capozzoli et al., 2015;

5. Further areas of study recommended by this paper.  Additional data set investigation so as to lower the limit of the error rate of the classification tree.  The influence the decisions of the owners of buildings have on the application of proposed retrofit actions.

6. References 1) Capozzoli A, Grassi D, Piscitelli MS and Serale G (2015b) Discovering knowledge from a residential building stock through data mining analysis for engineering sustainability. Energy Procedia 83: 370–379, http://dx.doi.org/10.1016/j. egypro.2015.12.212. 2) Galiotto N, Heiselberg P and Knudstrup MA (2015) The Integrated Renovation Process: application to family homes. Proceedings of the Institution of Civil Engineers –Engineering Sustainability 168(6): 245–257, http://dx.doi.org/ 10.1680/ensu.14.00020.

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