Marvuglia

2008 International Conference on Computational Science and Its 2008 International Conference on Computational Science and Its Applications (ICCSA 2008)Applications (ICCSA 2008)

Perugia, June 30 - July Perugia, June 30 - July 3, 20083, 2008

Dr. A. MarvugliaDr. A. Marvuglia

Session: Geographical Analysis, Urban Modeling, Spatial statistics

A web-based autonomous weather monitoring system of the town of

Palermo and its utilization for temperature nowcastingBy:

Giorgio Beccali,Maurizio Cellura,Simona Culotta,Valerio Lo Brano,Antonino Marvuglia

UNIVERSITY OF PALERMO




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.




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:1. Air Temperature sensor;2. Barometer;3. Hygrometer;4. Anemometer and weather vane;5. Rain gauge; 6. Radiometer




Weather stations are Vantage Pro2 Plus (Davis Instruments). Parameter Resolution Precision ±

External Temp.

0,1 °C 0,5 °C Temperature

Dew point 1 °C 1,5 °C

Rainfall depth 0,2 mm 4 % Rainfall

Rainfall intensity

0,1 mm/h 5 %

Humidity Internal humidity

1 % 5 %

Wind direction

1° 7°

Cardinal points

22,5° 7°

Wind speed 0,1 m/s 5 % Wind

Gust direction 22,5° 7°

Pressure Barometric pressure

0,1 hPa 1 hPa

Global & UV solar radiation

Global solar radiation

1 W/m² 5 %

Solar radiation sensors

Pluviometer

SIM + solar battery

Consolle

Anemometer

Anemometer base




Albunea

Pizia

Merlino

Amaltea

MeteoPalermo

1Morgan

a

Cassandra




Area: 6.25 Km2

Area: 13.5 Km2

Area: 45.6 Km2

Installed in March 2008

Weather station

Installation date

Merlino September, 26th 2006

Morgana November, 13rd 2006

Cassandra November, 30th 2006

Amaltea February, 20th 2007

Pizia May, 21st 2007

MeteoPalermo1

July, 10th 2007

Albunea March, 4th 2008




GSM connection via SMS

SMB connection over a LAN

Linux Server hosting a LAMP platform: Linux, Apache, MySQL, PHP

http://www.dream.unipa.it/meteo

Public HTTP connection or with login and password

Internet






Internet

Windows PC

Weather station







Internet






Internet

Windows PC

Weather station








Internet






Internet

Windows PC

Weather station







Internet






Internet

Windows PC

Weather station



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.




Data publishing system




















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.




Statistical elaborations

Elaborations edited by Dr. Valerio Lo Brano and Dr. Simona Culotta




Temperature and HumidityFor 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.




Rainfall It is possible to display the bar plot of the monthly total rainfall depth in mm.




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).




Wind dataThe wind speed frequency histogram can also be displayed.

Wind speed (m/s)

Fre

qu

en

cy

(%)

It is useful for the anemometric characterization of the sites.




Case study: ANN-based temperature nowcasting

The 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)





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:

1 1y t G q u t H q e t 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 AutoRegressive Moving Average with eXogenous inputs (ARMAX) model the transfer functions are defined as:

1

1

1, d

B qG q q

A q

1

1

1,

C qH q

A q

1 1 ;q x t x t




The ARMAX model

and the optimal predictor is:

1 1

1 1

1 1 1

ˆ 1

1 1 ,

d

d

B q A qy t q u t y t

C q C q

q B q u t A q y t C q t

ˆ,t y t y t prediction error or residual :

1 , , 1, , ,T

t y t y t n u t d u t d m t t k

Regression vector :

Parameter vector : 1 0 1, , , , ,

T

n m ka a b b c c

,T t




Nonlinear model structuresWhen 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.




Nonlinear model structures

Nonlinear counterparts to the linear time series forecasting model structures are thus obtained by:

, ,y t g t e t 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; ,t

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 .




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.

Neural Network

y(t-1)

y(t-n)

u(t-d)

u(t-d-m)

(t-1)

(t-k)

-1q

q -1

(t)

y(t)

y(t)u3: Wind speed

u2: Dew point

u1: Humidity

u5: Solar radiation

u4: Atm. pressure

Temperature




The networks used in the case-studyThe 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).

10

Dew point

Wind speed

Atm. pressure

Solar radiation

Temperature

Humidity

1

2

x

y

y

x

y=tanh(x)

Except MeteoPalermo1

station




ResultsThe models were validated by observing the differences between the measured and the predicted temperatures for the week ranging from August 25th to 31th 2007, which had not been used for the training phase. Weather station MAPE (%) MAE (°C)

Amaltea 1.47 0.45

Cassandra 1.55 0.48

Merlino 1.13 0.35

MeteoPalermo1 2.11 0.68

Morgana 1.44 0.45

Pizia 1.27 0.39

maxmin

1

ˆ100MAPE

Ni i

i i

y y

N y

1

1ˆMAE

N

i ii

y yN




Results

Morgana

Cassandra

Amaltea

Pizia

MeteoPalermo1

Merlino

Week August 25th to 31th 2007

OutputOne-step ahead prediction




Results

Merlino station




Map of the average forecasted temperature for the week 25/08/07 – Map of the average forecasted temperature for the week 25/08/07 – 31/08/0731/08/07




Map of the average measured temperature for the week 25/08/07 – Map of the average measured temperature for the week 25/08/07 – 31/08/0731/08/07




Conclusions and future workThe 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.




Dr. Antonino Marvuglia

e-mail: [email protected]

tel: +39 091 236 139

web site: www.dream.unipa.it






















Forecasting performaces of the ARMAX (4,4,1) model for Merlino

weather station Week 25-31 August 2007.

OutputOne-step ahead prediction

Technology

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