Your SlideShare is downloading. ×
Marvuglia
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Marvuglia

613
views

Published on

Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

Published in: Technology, Business

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
613
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
6
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Session: Geographical Analysis, Urban Modeling, Spatial statistics A web-based autonomous weather monitoring system of the town of Palermo and its utilization for temperature nowcasting By: Giorgio Beccali, Maurizio Cellura, Simona Culotta, Valerio Lo Brano, Antonino Marvuglia UNIVERSITY OF PALERMO
  • 2. Introduction
    • Nowadays, the influence of microclimatic conditions on the energy behavior of buildings draws the attention of many researchers .
    • The enhanced urbanization, occurring both in developed and developing countries, led to the appearance of the Urban Heat Island (UHI) effect, notably known for the air temperature increase in the wide urban areas, compared to the conditions measured in the extra-urban areas .
  • 3.
    • An accurate analysis of the spatial and temporal evolution of the UHI needs a detailed collection of local weather data (recorded with a suitable sampling time) which can be accomplished only through an efficient weather monitoring system.
    • The Department of Energy and Environmental Researches (DREAM) of the University of Palermo has started to build up a network of weather stations displaced in different parts of the town.
    • The weather stations are equipped with:
      • Air Temperature sensor;
      • Barometer;
      • Hygrometer;
      • Anemometer and weather vane;
      • Rain gauge;
      • Radiometer
  • 4.
    • Weather stations are Vantage Pro2 Plus (Davis Instruments).
    Solar radiation sensors Pluviometer SIM + solar battery Consolle Anemometer Anemometer base
  • 5. Albunea Pizia Merlino Amaltea Meteo Palermo1 Morgana Cassandra
  • 6. Area: 6.25 Km 2 Area: 13.5 Km 2 Area: 45.6 Km 2 Installed in March 2008 July, 10 th 2007 MeteoPalermo1 March, 4 th 2008 Albunea May, 21 st 2007 Pizia February, 20 th 2007 Amaltea November, 30 th 2006 Cassandra November, 13 rd 2006 Morgana September, 26 th 2006 Merlino Installation date Weather station
  • 7. Data acquisition and web publishing system The Linux server of DREAM connects to the shared folder of this PC where the file is stored and copies it into a local folder. The procedure is automated by a bash script and is repeated for each weather station. Re-formatting of the ASCII file through a Perl script A web server (Apache) is connected with the database server (MySQL) and an http service is available over TCP/IP network . Graphs and data digest : publicly available. Statistic elaborations and data download : protected by login and password. Every 30 minutes each weather station automatically generates an ASCII file containing the last 336 collected data and immediately transfers it (via GSM) to a MS Windows PC located at the DREAM building, in which a proprietary software is installed.
  • 8. Data publishing system
  • 9. Data publishing system
  • 10. Data publishing system
  • 11. Data publishing system
  • 12. Data publishing system
  • 13. Data publishing system
    • Web surfers can have two different representations of the data: a numerical representation with the current values and a graphical representation of historical time series.
    • The generated graphs are dynamic and are created by the system every time someone visits the web page. Several statistical elaborations are also provided in graphical form.
  • 14. Statistical elaborations Elaborations edited by Dr. Valerio Lo Brano and Dr. Simona Culotta
  • 15. Temperature and Humidity
    • For temperature and humidity analysis interactive graphs have been created, that allow the user to select the desired reference period, to zoom in on a particular area of the chart or to change the scale of representation.
  • 16. Rainfall
    • It is possible to display the bar plot of the monthly total rainfall depth in mm.
  • 17. Wind data
    • It is possible to display several polar diagrams showing the prevailing wind direction during the selected period. It is also possible to plot a polar diagram with the wind events subdivided in velocity classes (< 1 m/s; from 1 m/s to 2.5 m/s; > 2.5 m/s).
  • 18. Wind data
    • The wind speed frequency histogram can also be displayed.
    • It is useful for the anemometric characterization of the sites.
    Wind speed (m/s) Frequency (%)
  • 19. Case study: ANN-based temperature nowcasting
    • T he very short-term temperature forecasting (nowcasting) problem was treated as the identification of a dynamical system and tackled by the utilization of a Nonlinear Black-Box model based on a ANN.
    • The underlying hypothesis is that the system is signified by transfer function characterizations. As the behaviour of the system changes, the ANN model developed keeps track of the changes in the features and parameters of the system.
    • Thus, at any instant of time, it correctly simulates the given time-varying system, despite any significant change in its properties.
    • The analyses were accomplished by using Matlab 7.0:
      • NNSYSID Toolbox by M. Nørgaard ( Model selection );
      • System Identification Toolbox ( Parameters estimation and Model validation )
    Perugia, June 30 - July 3, 2008
  • 20. Linear model structures
    • According to Ljung (1999), a single-output system with input u and output y is called linear if it is possible to describe it by a model that takes the form:
    • where:
    • - G and H are transfer functions in the time-delay operator
    • e ( t ) is a white noise signal that is independent of past inputs and that can be characterized by some probability density function;
    • u ( t ) is an exogenous signal.
    • In the case of the A uto R egressive M oving A verage with e X ogenous inputs ( ARMAX ) model the transfer functions are defined as:
  • 21. The ARMAX model
    • and the optimal predictor is:
    prediction error or residual : Regression vector : Parameter vector :
  • 22. Nonlinear model structures
    • When widening the focus to also include identification of nonlinear dynamic systems, the problem of selecting model structures becomes more difficult.
    • By exploiting the typical capability of MultiLayer Perceptrons (MLP) to learn nonlinear relationships from a set of data, nonlinear extensions of the most common linear structures for time series prediction have been created.
    • By making this choice, the model structure selection is basically reduced to dealing with the following two issues:
      • Selecting the inputs to the network;
      • Selecting an internal network architecture
    An often used approach is to reuse the input structures from the linear models substituting the internal architecture with a MLP network.
  • 23. Nonlinear model structures
    • Nonlinear counterparts to the linear time series forecasting model structures are thus obtained by:
    where: is the vector containing the adjustable parameters in the neural network known as weights ; g is the function realized by the neural network and it is assumed to have a feed-forward structure. is again the regression vector ;
    • Depending on the choice of the regression vector, different nonlinear model structures emerge. If the regression vector is selected as for ARX models, the model structure is called NNARX. Likewise, NNFIR, NNARMAX, NNOE and NNSSIF structures there exist .
  • 24. The NNARMAX model
    • In particular, in the NNARMAX model the regressors are selected as in an ARMAX model. The past prediction errors depend on the model output and consequently they establish a feedback.
    u3: Wind speed u2: Dew point u1: Humidity u5: Solar radiation u4: Atm. pressure Temperature
  • 25. The networks used in the case-study
    • The parameters used for the predictor ( n, m, k, d ) were the same estimated by using Matlab System Identification Toolbox for the corresponding ARMAX model (with the same inputs and outputs).
    Except MeteoPalermo1 station
  • 26. Results
    • The models were validated by observing the differences between the measured and the predicted temperatures for the week ranging from August 25 th to 31 th 2007, which had not been used for the training phase.
    max min 0.39 1.27 Pizia 0.45 1.44 Morgana 0.68 2.11 MeteoPalermo1 0.35 1.13 Merlino 0.48 1.55 Cassandra 0.45 1.47 Amaltea MAE (°C) MAPE (%) Weather station
  • 27. Results Week August 25 th to 31 th 2007 Morgana Cassandra Amaltea Pizia MeteoPalermo1 Merlino Output One-step ahead prediction
  • 28. Results Merlino station
  • 29. Map of the average forecasted temperature for the week 25/08/07 – 31/08/07
  • 30. Map of the average measured temperature for the week 25/08/07 – 31/08/07
  • 31. Conclusions and future work
    • The developed web-based autonomous weather monitoring system was designed to study the spatial and temporal variation of the temperatures within the urban area of Palermo and investigate the influence of various factors on the UHI.
    • It represents a very powerful tool for urban monitoring.
    • In a future research activity a forecasting model with a wider time span will be implemented, in order to obtain the future evolution of the temperature with a relevant advance and use this information to study the time evolution of urban comfort conditions.
    • By using some of the existing outdoor comfort indices it will be possible to create dynamic maps of the actual and forecasted thermal hygrometric comfort conditions at urban scale and publish them on the website of DREAM.
  • 32. Dr. Antonino Marvuglia e-mail: marvuglia@dream.unipa.it tel: +39 091 236 139 web site: www.dream.unipa.it Thank you for your attention
  • 33.  
  • 34.  
  • 35.  
  • 36.  
  • 37.  
  • 38.  
  • 39. Forecasting performaces of the ARMAX (4,4,1) model for Merlino weather station Week 25-31 August 2007. Output One-step ahead prediction

×