A Study on the Building Demend Forecasting for a Satellite Town.

1. A STUDY ON THE BUILDING DEMAND
FORECASTING FOR A SATELLITE
TOWN
NAME OF THE SCHOLAR NAME OF THE GUIDE
T.R.RAGHAVAN Mr.T.PRADEEP
12CMR009 ASST.PROFESSOR
DEPARTMENT OF CIVIL ENGINEERING

2. INTRODUCTION
• The effects of population growth are varied
and vast. While population growth, of any
species, may be beneficial to a certain extent,
there may come a time when the number in the
population exceeds the natural resources
available to sustain it. This is referred to as
overpopulation. The consequences of such an
event are severe and major.

3. SATELLITE TOWN
• A satellite town or satellite city is a concept in
urban planning that refers essentially to smaller
metropolitan areas which are located somewhat
near to, but are mostly independent of larger
metropolitan areas.
• Satellite cities are small or medium-sized cities
near a large metropolis, that:
• predate the metropolis' suburban expansion
• are at least partially independent from that
metropolis economically and socially

4. Continues…
• are physically separated from the metropolis by
rural territory or by a major geographic barrier
such as a large river;
• satellite cities should have their own independent
urbanized area, or equivalent
• have their own bedroom communities
• have a traditional downtown surrounded by
traditional "inner city" neighborhoods
• may or may not be counted as part of the large
metropolis' Combined Statistical Area

5. OBJECTIVES
• A study on the building demand forecasting
for a satellite town

6. LITERATURE COLLECTIONS
• Jan Franklin Adamwoski, Peak Daily water demand fprecast
modelling using Artificial Neural Networks, Journal of Water
Resources Planning and Management © ASCE / March/April
2008
• Peak daily water demand forecasts are required for the cost-
effective and sustainable management and expansion of urban
water supply infrastructure.
• This paper compares multiple linear regression, time series
analysis, and artificial neural networks ANNs as techniques
for peak daily summer water demand forecast modeling.
• Analysis was performed on 10 years of peak daily water
demand data

7. • S. Saravanan, S. Kannan and C. Thangaraj Department
of Electrical and Electronics Engineering, Kalasalingam
University, India , India’s Electricity Demand Forecast
Using Regression Analysis and Artificial Neural Networks
based on Principal Components, ICTACT Journal on Soft
Computing, july 2012, Volume: 02, ISSUE: 04
• Power System planning starts with Electric load (demand)
forecasting.
• Accurate electricity load forecasting is one of the most
important challenges in managing supply and demand of the
electricity, since the electricity demand is volatile in nature;
• The aim of this study deals with electricity consumption in
India, to forecast future projection of demand for a period of
19 years from 2012 to 2030.

8. Guoqiang Zhang, B. Eddy Patuwo, Michael Y. Hu*,
Forecasting with artificial neural networks: The state of the art,
International Journal of Forecasting 14 (1998) 35 –62
•Interest in using artificial neural networks (ANNs) for forecasting
has led to a tremendous surge in research activities in the past
decade.
•While ANNs provide a great deal of promise, they also embody
much uncertainty. Researchers to date are still not certain about
the effect of key factors on forecasting performance of ANNs.
•This paper presents a state-of-the-art survey of ANN applications
in forecasting. Our purpose is to provide
(1) a synthesis of published research in this area,
(2) insights on ANN modeling issues, and
(3) the future research directions.

9. • Bassam M. AbuAl-Foul, Economics Department,
American University of Sharjah, Forecasting Energy
Demand in Jordan Using Artificial Neural Networks, Topics
in Middle Eastern and African Economies Vol. 14,
September 2012
• The purpose of this study is to forecast energy use in one of
the MENA countries, Jordan using annual data over the
period 1976-2008.
• The methodology used in this study follows the artificial
neural networks analyses.
• We use four independent variables, namely, gross domestic
product, population, exports, and imports to forecast energy
use.

10. Literature collectionLiterature collection
Review of literatureReview of literature
Data CollectionData Collection
Selection of ParametersSelection of Parameters
Population ForecastingPopulation Forecasting
Demand ForecastingDemand Forecasting
METHODOLOGY

11. Analysing the suitable place for Satellite
Town
Analysing the suitable place for Satellite
Town
Justifying the analysisJustifying the analysis
Providing SuggestionsProviding Suggestions

12. SELECTION OF SOFTWARE
• Artificial Neural Network
• Statistical Product and Service Solutions
(SPSS)

13. ARTIFICIAL NEURAL NETWORK
• Neural network software is used to simulate,
research, develop and apply
artificial neural networks,
biological neural networks and in some cases a
wider array of adaptive systems.
• Commonly used artificial neural network
simulators include the
Stuttgart Neural Network Simulator (SNNS),
Emergent, JavaNNS, Neural Lab and
NetMaker

14. Continues...
• A neural network (NN), in the case of artificial
neurons called artificial neural network (ANN), is an
interconnected group of natural or artificial neurons
that uses a mathematical or computational model for
information processing based on a connectionistic
approach to computation.

15. STATISTICAL PRODUCT AND
SERVICE SOLUTIONS (SPSS)
• SPSS consists of an integrated series of computer
programs which enable the user to read data from
questionnaire surveys and other sources (e.g. Demand
and administrative records),to manipulate them in
various ways and to produce a wide range of
statistical analyses and reports, together
with documentation.

16. Continues...
• SPSS Statistics is a software package used for
statistical analysis. It is now officially named "IBM
SPSS Statistics". Companion products in the same
family are used for survey authoring and deployment
(IBM SPSS Data Collection), data mining (IBM
SPSS Modeler), text analytics, and collaboration and
deployment (batch and automated scoring services).

17. Continues...
• With SPSS predictive analytics software can
predict with confidence what will happen next so that
can make smarter decisions, solve problems and
improve outcomes.

18. FOR SOFTWARE
ANALYSIS

19. FACTORS INFLUENCING THE
BUILDING DEMAND FORECASTING
• AREA
– Free space available inside the city (private &
public)
– Free space available outside the city (private &
public)
– Area of the proposed satellite towns separately
(Thindal & Solar)

20. Continues...
• POPULATION
– Population of the whole city
– Population of the proposed satellite towns
separately (Thindal & Solar)
– Population of the western side of the district (from
Thindal to Sengapalli)
– Population of the north-eastern side of the district
(from Solar to Sengodampalayam)

21. Continues...
• BUILDINGS
– Number of Residential buildings inside the city
– Number of Governmental buildings
– Capacity of the Governmental building as per the
codal provision & raw data
– Utilisation of the government building (in %)

22. FOR QUESTIONNAIRE
SURVEY

23. FACTORS INFLUENCING THE
SELECTION OF A SATELLITE
TOWN
• THINDAL & SOLAR
– Accessibility to the city from Thindal & Solar
– Development of the town
– Job opportunities around the town
– Number of peoples approaching the city from
Thindal (western side of the district)

24. Continues...
– Number of peoples approaching the city from
Solar (North-eastern side of the district)
– Availability of water facility (ground water &
other sources)
– Sources and availability of electricity
– Availability of enough Government land to setup a
Satellite Town

25. REFERENCES
• Refense, A.N.; Zapranis, A. and Francis, G. (1994):
“Stock Performance Modelling using Neural
Networks: a comparitive study with regression
models.” Neural Networks, 7, No.2, PP. 375-388
• Zheng, D.X.M., NG, S.T. and Kumaraswamy, M.M.
(2004) Applying a GA-based multiobjective approach
for time-cost optimization. Journal of Construction
Engineering and Management, ASCE, 130(2), 168-
176.

26. Continues…
• Zhang, G. and Hu, M. Y.(1998): “Neural Network
forecasting of the British/US dollar exchange rate.”
Omega, Vol.26, No. 44, PP. 495-506.
• Tse, R.Y.C., Ho, C.W. and Ganesan, S.(1999)
Matching housing supply and demand: an emprical
study of Hong Kong’s market, Construction
Management and Economics, 17(5), 625-634.

27. PROJECT SCHEDULE
July August September October
Weeks 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Literature collection & Project
selection
Literature study
Selection of software
Factors influencing the building
demand forecasting (for software
analysis)
Factors influencing the selection
of a Satellite Town (for
questionnaire survey)
Data collection

28. POPULATION OF THE
ERODE DISTRICT
• 1991 - 18,02,900 peoples
• 2001 - 20,16,582 peoples (11.85% incd.)
• 2011 - 22,51,744 peoples (11.66% incd.)
No.of.Males - 10,24,732
No.of.Females - 991850

29. POPULATION OF THE
ERODE CITY
• 2011 - 5,21,776 peoples
• Male - 2,61,470 peoples (82.2%)
• Female - 2,60,306 peoples (72.42%)
• Ratio - 996 Females : 1000 Males

30. AREA OF THE ERODE
DISTRICT
• Area - 2198 sq.miles (5692 sq.kms)
(whole district)
• Area of the Erode City :
– Rural Area - 287 sq.miles (743 sq.kms)
– Urban Area - 3.22 sq.miles (8.34 sq.kms)

31. Continues…
• Area of the North-eastern side of the
district
– From Solar to Sengodampalayam
– 400 sq.miles (1037 sq.kms)
• Area of the Western side of the District
– From Thindal to Sengapalli
– 1798 sq.miles (4655 sq.kms)

32. LAND USAGE
• 83.25% of the ERODE municipal area has
been developed along the road side in all
major roads, in municipal area along Kongan
road near the southern boundary of the local
planning area, mainly the commercial area in
Erode town is concentrated near the junction
of Brough road and archery road and Bazaar
area.