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ABSTRACT

Designing an optimal network measurement sensors

for monitoring geographical wind resource:

Statistical techniques and GISLaura Frías Paredes (lfrias@cener.com) / Edurne Pascual Chahuan / Martín Gastón Romeo

National Renewable Energy Centre (CENER)

Fermín Mallor (Public University of Navarre, Spain)

PO.010

EVALUATION OF THE STATISTIC INDEX OF FEASABILITY

A DEEPER DESCRIPTIVE ANALYSIS BY MEAN OF BICLUSTERING

FUTURE DEVELOPMENTS : BICRITERIA INDEX AND PARETO CURVE

EWEA Resource Assessment 2015 – Helsinki– 2-3 June 2015

This work presents a new methodology developed to design a sensor network extent to ensure

a certain level of uncertainty in the development of spatial analysis of long-term estimations of

wind energy resource. The resource maps are a useful tool in the screening process areas of

interest for the use, development and installation of renewable energy. Different techniques and

sources of information are used to estimate the long-term resource availability in a geographic

area, weather models, satellite images and ground measurements are the most common.

Obviously, the more accurate information is provided by real measurements using appropriate

sensors, but this information presents the disadvantage of referring to a particular point and the

usual scarcity of this type of data in many parts of the world. On the other hand, it should be

noted that in the development of resource maps the actual available information is introduced to

adjust estimates and validate the results. In this work it is considered an initial estimation

resource in a geographic area, with the goal of identifying the ideal location for a sensor network

that allows us to reduce the uncertainty of the estimate. For this purpose, the spatial and

temporal variability is analyzed by using spatial statistic and to identify distinct areas and

representative points. This analysis is merged with the rest of geographic information available

by mean of a GIS to obtain the most interesting locations to the measurement network

installation.

The methodology developed in this work can play an important role when the potential of the

wind energy is analyzed in large geographic areas.

REFERENCEs

For future developments we could perform more complex and exhaustive area

optimization developments taking into account not only linear approach distances but path

costs calculation for displacement for measuring towers and site installation. Other factors

can be added such as view shed analysis, airport entrance areas, etc.

Along this work, we will use a database of wind resource over Iberian peninsula obtained with

the numerical weather prediction model SKIRON with a resolution of 0.05º. Period: 2003-2013

(hourly wind data).

The first task is to analyze the data from the geostatistical point of view.

Biclustering consists of simultaneous partitioning of the set of samples and the set of their

attributes (features) into subsets (classes). Samples and features classified together are

supposed to have a high relevance to each other.

A typical situation to calculate bicluster are a high dimensional dataset with many variables, so

that normal cluster algorithms lead to diffuse results due to many uncorrelated variables. Also

biclustering is useful if there is a assumed connection of objects and some of the variables in

the dataset, e.g. some objects have 'similar' patterns for a given set of variables.

EVALUATION OF THE ECONOMIC FEASIBILITY INDEX FOR EACH POINT

GIS is a set of tools to evaluate geographical data, characterized by being geo-referenced, with

the ultimate purpose of making territorial studies, obtaining analysis using spatial relationships

between elements themselves in the territory. Today its use is more and more important

because of all the features that are made in this tool: operations between layers of information

from different sources, storage, data unification and visualization of different variables of meso-

microscale models that characterize the resource (vm, power density) of a given territory .The

objective is to obtain optimized areas for wind farm construction and installation of measuring

towers. To do this we consider various aspects with different legal, economic and environmental

criteria, but always the determining factor will be the existing resource.

These factors have been taken into account in Spain:

1. Wind resources obtained by SKIRON.

2. Minimum legal distance and maximum economical

distance to national and autonomic roads.

3. Maximum economical distance to high-voltage

power lines.

4. Maximum economical distance to substations.

5. Absolute restriction of protected areas.

6. Minimum legal distance to population centers.

7. Maximum economical slope obtained from the

digital elevation model.

So, each grid point has associated a score that represents its economic feasibility. This

score has been obtained by mean of a ponderated sum of the different layers and taking

into account that a score equals to 0 means that the site is not suitable by some reason.

In this case the objects to classify are the

grid points meanwhile the variables are the

monthly mean of wind speed. We can seen

3 different areas, two of them present high

relationship in months from November to

March (2 and 3). However the bicluster 1

presents high relation when summer

months are taken into account.

The design of an optimal network is related with minimizing the variance of our estimator.

Now, the data is prepared to analyze the variance using a variogram. The variogram is the

fundamental tool to describe the spatial covariance.

The figure shows the adjust of model to the empirical variogram. For our case, r2 = 0.3481,

σ2 = 0.2370 and the range is hr = 1.57.

These parameters give the amount of variance that it is explained when a sensor is located at

a certain point. Near the point, the variance decrease and the influence is lost at distances

greater than hr. The variogram could be considered as a reference of the amount of variance

that should be explained where a sensor is located at a certain point.

In the following strategies are considered to select the network grid.

Basically, all of them selects a point, then downscale the variance surface according

with the spatial dependence given by the variogram and so on.

In this analysis the whole points of the area are scored according to the order that

they are selected by the methodology.

At this point each pixel of the area is

identified by two different scores. The goal is

to minimize both index to identify the most

interesting sites. The pareto curve appears

as an interesting tool to achieve this goal

The key idea of this method is to use the variance

surface of the remote monitoring data to find those

sites with higher variance which will be the

candidates to be included in the network. Once a

location is included, the variance is downscaling in

the vicinity according with the estimated spatial

dependence detected in the remote monitoring data.