Wind Report 2

The Potential of the South Campus at Maynooth University as a Renewable Source of Energy

A Spatial Analysis Using Remote Sensing and GIS Technology

Padraig QuinnStudent No: 11125900

2016Maynooth University

Padraig Quinn11125900

Executive Summary

Since the Industrial Revolution people have been over reliant on fossil fuels, which have had a detrimental effect on our environment and also in aspects of our society. Due to increased recent awareness and advancements in technologies, the demand for and implementation of renewable energy production is reaching new heights. The south campus at Maynooth University was analysed in this study as a potential location for micro wind generation by means of micro wind turbines. The study followed strict guidelines and regulations throughout. Remote sensing and GIS technologies were implemented in order to produce a plausible high quality model outlining the potential of the south campus as a source of renewable energy. The model consisted of spatial interpolation processes combined with spatial and wind speed constraints. The end result proved positive, suggesting that the location may have the potential, however, the duration of the study was short term and further extensive research is needed to produce a more definitive model. The purpose of the study was to outline the potential that the location may have as a possible future renewable energy source, and not to produce a definitive location for renewable energy production.

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EXECUTIVE SUMMARY..................................................................................................................... 1

1. INTRODUCTION........................................................................................................................... 3

1.1 Introduction........................................................................................................................................31.2 Context................................................................................................................................................31.3 Aims and Objectives...........................................................................................................................4

2. METHODOLOGY........................................................................................................................... 5

2.1 Collecting Ground Control Points Using Trimble GPS.........................................................................52.2ErdasImage Rectification.....................................................................................................................5

2.2.1 Map to Image Rectification.........................................................................................................52.2.2 Image to Image Rectification.......................................................................................................7

2.3 Exporting Erdas Images into ArcMap.................................................................................................92.4 Collecting Wind Data..........................................................................................................................92.5 Data Capture.....................................................................................................................................102.6 Creating a Raster Surface from Point Data.......................................................................................11

2.6.1 Kriging Interpolation.................................................................................................................112.6.2 IDW Interpolation......................................................................................................................12

2.7 Interpreting the Raster Surface........................................................................................................122.7.1 Euclidean Distance....................................................................................................................132.7.2 Raster Calculator.......................................................................................................................13

3. TRIMBLE GEOMATICS OFFICE.....................................................................................................15

3.1 Creation of New Project...................................................................................................................153.2 Project Properties.............................................................................................................................153.3 Importing the Data...........................................................................................................................153.4 Feature and Attribute Editor............................................................................................................153.5 Exporting Survey as a Shapefile........................................................................................................163.6 Importing into ArcGIS.......................................................................................................................17

4. RESULTS.................................................................................................................................... 19

4.1 Surface Interpolation........................................................................................................................194.2 Accuracy Assessment........................................................................................................................20

4.2.1 Ground Measurements Compared to Image Measurements....................................................204.2.2 Comparison between Rectified Images Using Inquire Tool.......................................................224.2.3 Assessing Accuracy Using Trimble Survey..................................................................................22

5. DISCUSSION AND CONCLUSION..................................................................................................23

5.1 Discussion.........................................................................................................................................235.2 Conclusion.........................................................................................................................................24

BIBLIOGRAPHY.............................................................................................................................. 25

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1. Introduction

1.1 IntroductionDue to the over use and dependency on fossil fuels as a source of energy, many reserves globally have been severely depleted or completely exhausted.This dependency and exploitation not only diminished many resources, but also has had a detrimental effect on our environment and have even caused near irreversible social effects (Pieprzyk, et al., 2009). This rapid rate of consumption has created the need for other environmentally friendly sustainable methods. The Irish Government has recently announced that a radical transformation of Ireland’s energy programme is required and aims to reduce greenhouse gas emissions by between 80 and 95 percent by 2050, compared to figures relating to the early 1990s by generating our electricity from renewable sources (Department of Communications, Energy and NaturalResources, 2015).Ireland is an island surrounded by the Atlantic Ocean, which generates strong westerly winds throughout the year.Thus, wind energy can be considered an excellent source of environmentally friendly sustainable energy that is abundantly available. Wind farm energy production is increasing each year and by June 2010 there were a total of 110 wind farms producing electricity (Sustainable Energy Authority ofIreland, 2016). This study outlined the potential of the South Campus at Maynooth University toaccommodate a wind turbine wind farm and generate its own sustainable electricity, whilst reducing its carbon footprint for the future.The report begins with an introduction and context section outlining the topic, followed by a detailed methodology section divided into subheadings and sections that explain each fundamental step, supported with screen shots for visual representation. Furthermore, this segment will be followed by a results section detailing the outcome of each stage. The final segment will comprise of a discussion and conclusion section that further explains and summarises the findings in the context of this report, followed by a bibliography section. Also, it includes a section containing the use of Trimble Geomatics Office (TGO) and how it was implemented to create a feature library. Finally there is a separate section containing all the necessary appendices that relate to the report.

1.2 ContextAs previously stated, wind energy is an abundant natural resource in Ireland. Windfarms are already present in Ireland however, micro wind turbine farms are underutilised and under researched (Li, et al., 2011). Due to the enormous size of some wind turbine structures, as high as 150 metre tip height (Irish Wind EnergyAssociation, 2012), micro wind turbine structures were best suited for the South Campus of Maynooth. Microgeneration is the production of energy on a small scale, best suited for domestic, farm and business usage and it operates at low voltage between six and eleven kilowatts (kW). For small scale (domestic), the total height of the structure must not be in excess of 13 metres, and for small scale (commercial) the total height must not be greater than 20 metres. Additionally, they must not be within the total height structure of one another, or any boundary or structure.(Irish Wind EnergyAssociation, 2014).

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Due to spatial constraints and the day to day running of the college, these domestic scale structures were considered to be viable and appropriate. Also, it is recommended that the average wind speed needs to be a minimum of 3m/s in order for the turbine to generate electricity and greater than or equal to 5m/s in order for the turbine to be economically viable. Furthermore, there is a safety cut-off speed where the turbine shuts down to prevent it from being damaged, or from damaging the surrounding area (Li, et al., 2011).

1.3 Aims and ObjectivesThe aim of this report was to examine the potential of the South Campus of Maynooth as a possible location for sustainable energy production, by means of micro wind turbine generation. The report intended to record and document primary wind data obtained twice weekly throughout the course of a month. The data was recorded through the use of a hand held anemometer (Figure 1) and documented. The project proposed to outline the potential of the campus as a whole, together with the best possible locations. The study intended to identify the surface areas within the south campus of Maynooth that had an average windspeed of greater than or equal to 5m/s. This investigationinvolved a statistical analysis of the recorded wind dataset produced, but also spatial analysis such as interpolation,through the usage of GIS software. A geo-rectified image of the campus was also incorporated using ERDAS Imagine software, and exported to the same GIS software for visualisation and interrogative purposes. The results of this study were intended to be for plausible viability and not definitive recommendation. The details of each of these fundamental steps was documented and explained in full in the upcoming section of this report.

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2. Methodology

2.1 Collecting Ground Control Points Using Trimble GPSEighteen Ground Control Points (GCPs) were collected and the coordinates were recorded using a Trimble GPS system. There needed to be a good spread of the GCPs around the college and not have them bunched or clustered in particular areas. Also a good sky view was needed.For best readings to accurately identify locations, a minimum of five satellites were required. If points were selected too close to buildings or tall trees, they would not be picked up by the satellites or could not be identified on the images. One of the most important requirements of a good GCP, is that it must be identifiable both on the image and on the ground (Gibson & Power,2000). The Trimble GPS system comprised of a staff and a hand held receiver for extremely precise measurements. All of the selected GCPs are visually represented as JPEGS in the Appendices section of this report.

2.2ErdasImage RectificationFirstly, in order to evaluate the Maynooth Campus as a whole and analyse all of the most appropriate locations through the use of GIS software, a geographically rectified image was needed. Two satellite images of the campus were obtained from a zip file taken at different times, and utilised for the rectification process. These images were referred to as Campus 1 (October 1999) and Campus 2 (Summer 2007). To successfully rectify the images, clearly identifiable ground control points (GCPs) were chosen from the Trimble GPS that could be identified on the image. Trimble GPS recorded the exact coordinates that were used to rectify the image. The first image chosen to rectify was the Campus2 image (Figure 2). The raw satellite image was distorted due to the earth’s rotation, curvature of the earth and the satellites orbital path. The image was not exactly the same shape and seemed skewed when compared to a map of the same area. The image is usually offset by 9 degrees due to the near polar orbit of the satellite (Gibson & Power, 2000).

2.2.1 Map to Image RectificationThis image was chosen as it was the more recent of the two and it also had a higher resolution and made identifying the GCPs easier, which meant it was more accurate. The GCP option was activated from within the multispectral tab underneath the transform and auto correct option, which activated the Set Geometric Model window,displayed below.

Next, followed the Tool Reference Setup (Figure 4) and Projection Chooser (Figure 5), configured exactly as displayed below.

Figure 1: Projection Chooser

The projection was saved as Msc 2016 (check this) and the polynomial order of 1 was selected. Usually after every 5 GCPs the polynomial order increases by 1, meaning

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this should have been selected as 3. However, the output proved highly accurate as 1. It was unlikely to be high, as the image was taken over a small geographical area with less variance between the points. Sometimes images are taken over a broader geographical area, so in these cases increasing the polynomial order would be very important.Put in results and maybe image as proof.Also maybe rerun GCPs with high polynomial order.Before entering the GCPs, the online Osi map viewer was activated so as to identify the coordinates of the GCPs and make sure that they were identifiable on the map, and also to locate them for use in the image. The GCPs were entered using the Multi Point Geometric Correction Window (within the multi spectral tab, followed by selecting GCPs option). After each point was entered, the coordinates were also entered in the X Ref and Y Ref fields in the table beneath the image. Importantly, after the third point was entered, the predict next option had to be deactivated, as predicting all of the GCPs from just three coordinates was not thought to be accurate. The RMS error also needed to be monitored. Normally a reading of less than two is sufficient, however, for the purpose of this study the RMS errors were all less than one for high precision rectification. See Figure 6 to view the selected GCPs for rectification of the image and Table1 for a selection of the coordinates and RMS errors for the GCPs. The original GCP 8 was not used, as it was not present on the image. It was situated wayward to the left of GCP7 and was not visible, as it was outside the geographical area represented on the image, therefore it could not be utilised for selection.

Figure 2: GCPs for South Campus

GCPs 4 and 6 were measurements between two trees and their exact location was identified by typing the coordinates into the search bar and by using the digital globe base map as a reference map, due to its recent high resolution imagery. The trees were visible on the digital globe map, but not on the other aerial photographic maps.The digital globe was available within the GeohiveOsi map viewer. The result outlined the

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precise location and could easily be identified on the image. GCP 6 served as an example for explanatory purposes.See figure 7 for visualisation. Once the RMS errors were low, the user was confident that the correct location was identified.

The newly created model was saved as a gms. file called Campus2_Model and the GCPs were saved as Campus2_GCPs as a gcc. file.It was then activated by following the steps illustrated below.

These steps again activated the Multi Point Geometric Correction Window and allowed the image to be resampled. The resample technique used was nearest neighbour and the pixel (output cell) size was 20cm and was named rectcamp2.img, as represented below. This saved the geo-rectified image as a new independent image evident in Figure 12.

Figure 3: RectCamp2 Image

2.2.2 Image to Image RectificationTo geo-rectify the second image, similar procedures were implemented, except this time the unrectified image was corrected using the newly geo-rectified image as a reference layer. As with the previous image, the GCP option was selected from within the multispectral tab, followed by polynomial from the resulting window. The image layer option was chosen from the GCP Tool Reference Window, and the newly created rectcamp2 was selected fromthe files in the resulting window. This activated the window with the correct, previously saved projection system. Both processes are evident in Figure 13.

Similar to the previous section, the Multipoint Geometric Correction window was instigated, however this time it contained both images, see figure 14. Nineteen good GCPs that were visible on both images were selected for high accuracy rectification.

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Again, the RMS errors were monitored so that they remained less than one. A selection of GCPs with RMS errors is evident in table 4.

Figure 4: Image to Image Rectification Using GCPs

Once more, the image was resampled using the same constraints as with the previous image and is outlined below.However, this image was named rectcamp1, displayed in Figure 16.

Figure 5: RectCamp1 Image

The inquire and measure tools were implemented to test the accuracy of the images. When the viewers were linked, and the inquire crosshair tool was pointed over the corner of the tennis court in rectcamp1, it was almost exactly in the same position on rectcamp2. This suggested that the rectification of the two images was accurate and therefore a success.See below for visual representation. See the results section for more details.

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Figure 6: Accuracy of Rectified Images Using Inquire Tool

2.3 Exporting Erdas Images into ArcMapBefore the rectified images were added to ArcMap, they needed to be modified using ArcCatalogue. The spatial reference needed to be identified by selecting IRENET95 Irish Transverse Mercator, as this was the coordinate system used for the image rectification in Erdas. This was achieved by right clicking on the images, then selecting the edit button from within the Spatial Reference Heading, choosing Projected Coordinate System, followed by National Grids, Europe and finally IRENET95 Irish Transverse Mercator. The XY coordinate system is illustrated below.

Once completed, the two images were brought into ArcMap by selecting the ArcCatalogue icon within ArcMap and dragging the images across to the table of contents. The images now had a recognised coordinate system and could be used for further analysis using various techniques within Arc Toolbox, for the purposes of this study. Arc Catalogue was considered to be superior for file management, as it permitted the user to modify and prepare the images before adding them to the viewer. Additionally, the user could clearly arrange and manage all files and images in an organised drop down menu, which could be easily accessed when required.

2.4 Collecting Wind DataThe wind data collected and used for this study was raw primary data obtained through the use of a hand held anemometer, as previously illustrated in Figure 1. The data was collected throughout the course of a month.The month consisted of the first day in March until the first day in April. The data was recorded on a Tuesday and Friday of each week. This was deemed a better method, as if the days were too close together, the accuracy of the measurements may have suffered, where days close together can often have similar weather.Therefore, days further apart gave a broader range of the variations within the weather for the area, as much as possible whilst working within the constraints and parameters of this study. The data was also collected at the same time each day. The chosen time was the afternoon at 12pm. Winds are usually strongest in the afternoon due to several components working together such as; surface heating, convection and atmospheric stability (Aherns &Samson, 2010). Thirty six points were selected from around the rectified south campus image, with the coordinates of these locations noted.They served as the research sites for the study. Each point was then located in the field using a hand held

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Garmin GPS device, principally to ensure that the user could return to the exact point every time. The researcher stood at thepoints for one minute and the highest reading detected throughout the duration was recorded as the optimal wind speed. All of the points contained ten days of individual primary data. The wind speed was recorded in metres per second, as recommended (Irish Wind Energy Association, 2014), (Sustainable Energy Authority of Ireland, 2016) and (Li, et al., 2011). The data was recorded and transferred to ArcMap where it could be further analysed and utilised to generate spatial models.

2.5 Data Capture2.5.1 Importing Recorded Wind Data into ArcGisThe wind data for the thirty six reference points was recorded into a Microsoft Excel spreadsheet. The day was recorded, the name of the measurement point, the wind speed in metres per second (m/s) and the X Coordinate and Y Coordinate for the points in separate columns. Table 5 represents the structure of the recorded data.Additionally, a final column was created in the spreadsheet that contained the average wind speed for each measurement point. This was achieved by employing a simple average function within Excel. The average function contained the wind speed for each point for each day, divided by the number of days that the point was measured. This was an essential step when editing the data, as any further querying of the data within ArcGis related to the average speed of the data collected at the measurement points.

The spreadsheet was added to ArcMap through ArcCatalogue in the same way as previously explained with the rectified images. However, before adding it to the table of contents, the user had to select the correct spreadsheet from within the spreadsheet folder. The spreadsheet that contained the wind data was $3, so this was the one added to the table of contents. Once added to the table of contents (TOC), XY coordinates needed to be added. This was option was selected from within the File menu, followed by Add Data then Add XY data. This tool allowed the user set up a wizard to find any XY co-ordinate data in a dataset and automatically convert it to a point layer inArcGis. The important aspect to note was that the separate X and Y coordinate columns that were added to the spreadsheet, which contained GPS X and Y coordinates, could then be identified by the software and displayed in the X and Y field of the shapefile attributes. Also the spatial reference was edited so as it was the same reference system as the documented GPS coordinates. The configuration of the XY data for the creation of a point shapefile is evident in Figure 19.

After successful completion, a new layer appeared in the TOC called Sheet3$Events. In order to fully convert the layer into an independent shapefile, the layer had to be selected followed by data, export data and finally it was saved as a shapefile called Wind_Data. The shapefile contained all of the recorded measurements and data in its attribute table. Figure 20 represents the newly created point file superimposed on the rectcamp2 image.

Figure 7: Newly Created Point Shapefile

2.5.2 Digitising

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For the purpose of this study, certain features on the rectified image needed to be digitised as a means of data input for querying at a later stage. The features that were digitised which formed part of a future query for the modelling process were the buildings, trees and a campus boundary polyline layer. These needed to be digitised and given attributes, because the modelling process and the studied literature required that; no micro wind generation structure could be within 14 metres (maximum total structure height plus one metre) of any structure or boundary (Irish Wind EnergyAssociation, 2014) and (Li, et al., 2011). To implement, the folder where the images were saved was right clicked, followed by new, then shapefile. Once more, buildings served as the example for explanatory purposes. The configuration was exactly as depicted below in Figure 21. Within the shapefile properties window, under the fields tab, building was selected as the fields name and text as the data type. Then, the editor toolbar was activated and within the create features window the newly created buildings shapefile was selected and polygon from the construction tools options.

This permitted the user to draw an outline of the buildings with a series of clicks along the edge of the structure as outlined below.

The more clicks the better the accuracy. Finally the attributes of the newly digitised features were populated. This was achieved by selecting attributes from the editor toolbar and keying in the ID and building name. Each ID was given a value of one greater than the original FID value. See Figure 23 for visual interpretation. The same steps were followed for the remaining features, except polyline was selected for the boundary shapefile instead of polygon.

2.6 Creating a Raster Surface from Point DataUp until this stage, the rectcamp2 image had a point file, but did not have a continuous raster surface. In order to create a continuous (raster) surface, a spatial analysis procedure needed to be applied, known as interpolation. Interpolation is thepractice of estimating the values at unsampled or unmeasured sites within an area covered by existing measurements (Heywood, et al., 2006). The interpolation application allowed the user to further interrogate the point data and represent it as a continuous range across the image. Two interpolation methods were implemented and analysed before the most accurate means was chosen. The two processes were Kriging and IDW (Inverse Distance Weighted).

2.6.1 Kriging InterpolationThe surface interpolation process used for this exercise was a sophisticated process known as kriging. Kriging forms weights from surrounding measures and uses them to predict values at locations where the values are unknown. The values closest to the measured values are more important in this method. It assumes that the distance between sample points reflects a spatial correlation that can explain variations. The predictions fit a function (semi-variogram) within a certain study area. It is a method used in health sciences, geo-chemistry and air pollution modelling. Kriging uses probability to both predict and assess the error of the prediction (Childs, 2004). The kriging method was available within the spatial analyst toolbox. Certain modifications needed to be applied before running the analysis tool. The input feature was the Wind Data point file and the Z value field was the average speed column. The output cell

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size was set to 0.2 inorder to keep it the same as the output cell size for the rectcamp2 image. Also, within the environments tab, the processing extent was set to same as Campus_Bound shapefile. This polyline shapefile was digitised so it would act as a containment barrier. The kriging setup window is evident below.

The results of the kriging interpolation procedure are evident in Figure 25. The dark blue contained highest average wind speeds and the lighter coloured areas contained the lowest average wind speeds.

Also, the number of classes was set to 6, as this divided the classes best suited for the requirements of this report. Areas with values greater than or equal to 5 were of importance to the user. When the class numbers were set to six, the values of 5 upwards are clearly distinguishable and separated from any irrelevant values. Figure 24 illustrates the separate class divisions.

2.6.2 IDW InterpolationThe IDW interpolation method is also a linear weighted combination based on a set of sample points. However, it is not as sophisticated as the kriging method, as it does not take into account the error of its predictions. The weight assigned is a function of the distance of an input point from the output cell location (Childs, 2004). It assumes that the variable being mapped decreases in influence from its sample location. The IDW method was also available within the spatial analyst toolbox and it also needed to be modified before it could be enforced. Similar to the kriging method, the same constraints and parameters were implemented. However, the IDW setup had an input barrier polyline features option, where the Campus Boundary (Camp_Bound) was selected. This contained the surface entirely within the polyline layer unlike the kriging method. This layer acted as a barrier, as only areas within this proximity were of interest to the user. Additionally, the result output of this layer proved fundamental for masking future layers within the study area, with details documented in the upcoming sections. See below for details of the IDW configuration.

Figure 28 illustrates the results of the IDW raster surface. Once more, the lighter areas have the lowest values and the darkest areas have the highest values.

2.7 Interpreting the Raster SurfaceThe newly interpolated raster surface contained continuous surface data based on the average speed calculated at each measurement location. The surface created was predicted based on these measurements and could be displayed categorically and could also be further analysed. This study was interested in areas where the average wind speed was greater than or equal to five m/s. However, there were also other contributing factors. Outlined in the context section of this report, it stated that the turbines must not be within 14 metres of a structure or boundary. That meant the total structure height of 13 metres (assuming that the maximum height permitted for turbines was selected for construction) plus one metre. This meant that the regulated distance needed to be implemented for buildings, boundaries and where there were considerable tree densities. This was achieved through the use of Euclidean distance and map algebra functions.

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2.7.1 Euclidean DistanceThe Euclidean distance is a spatial analyst tool within the distance tool set. This function ran a straight line distance from the buildings, boundaries trees. This was the fundamental procedure needed in order to enable the software to perform any further distance queries. The buildings Euclidean distance process served as an example for instructive purposes. It was achieved by selecting Euclidean distance from within the distance tool set. From the resulting window, buildings were selected as the input feature and the output cell size was 0.2, which was the same as the rectified image. See Figure 29 for representation.

The output produced a surface with measured straight line distances separated at equal intervals, with the yellow colours being the closest and the pink and purple colours being furthest away. The output is visible in Figure 30 below.

2.7.2 Raster CalculatorThe raster calculator is a valuable tool within the map algebra toolbox. This function enabled the user to perform a mathematical function that could highlight any areas that were situated within 14 metres of a boundary or structure, but also any locations where the wind speed was greater than or equal to 5 m/s. After successful completion of the Euclidean distance surface, the new layer was then queried through usage of the raster calculator. The raster calculator produces surfaces in binary format. It assigns a value of 1 if the query meets the requirements (true) and a value of 0 if it does not (false). For the objective of this study, areas that were greater than or equal to 14 metres were of interest to the researcher. Again, the buildings calculation served as an example and is evident below.

It produced a map in binary format, with areas that were further than or equal to 14 metres assigned 1 (light orange) and areas less than these requirements assigned 0 (charcoal). See below for visualisation.

Finally the results were altered using an extract function within arc toolbox. The new layer was the input raster feature and the input raster used for the mask was the IDW layer as this was contained within the campus boundary, which was only of interest to the user. This function wasessentially implemented to enhance the visual representation of the final image. Itmerely extracted the data that was of interest to the study and clipped away the irrelevant data. See Figure 33for representation of the input configurationand Figure 34 visual exemplification of the masked building output image.

The same steps were followed for the trees and boundaries layers. Finally, to combine all three layers and also areas where the average wind speed was greater than 5 m/s (Krig_Great5) they needed to be multiplied together in order to form single layer. The raster calculator function implemented was exactly as depicted below in Figure 27.

The combined raster calculation called combined 14 that contained the above mentioned constraints, produced an image, where suitable areas were at a minimum of

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14 metres from any building, concentration of trees or boundary, but also the area had an average wind surface of greater than or equal to 5 m/s. The output image is identifiable in the results section.

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3. Trimble Geomatics Office

The purpose of Trimble Geomatics Office (TGO) is to create a feature library for surveys using GPS coordinates collected using Trimble VRS differential GPS system. The surveys can be incorporated and displayed in ArcGis for analysis, representation purposes and image accuracy assessment.

3.1 Creation of New ProjectA new project was selected from within the projects tab and the project template selected was metric. The project was named Geosurv14 and saved as a new project within TGO. The sample data collected was recorded in a Microsoft Excel spreadsheet. The data was divided and organised into five columns; Name, Easting, Northing, Elevation and Feature Code respectively as highlighted in Figure 35.

3.2 Project PropertiesTo set the project properties certain procedures were selected and altered. Firstly, within project properties the coordinate order needed to be changed from north first to east first as evident in figure 36.

Secondly, the default coordinate system was changed to Republic of Ireland ITM and the Geoid model was also set to the Republic of Ireland. See Figure 37 and 38 below.

A final altercation was carried out within the project properties setup within the recompute tab. The use best observation option was selected as illustrated below.

3.3 Importing the DataAfter successful completion of the project properties setup, data could then be imported into TGO. However, certain adjustments were undertaken so that TGO understood what each column of data represents and what order they were in shown in Figure 40.

Also it was organised that different features could appear at different layers within the software. A new column was added to the data by inserting [Layer] after [Feature Code] as evident in Figure 41. The newly coded column permitted TGO to realise the final column contained layer data.

After completing the procedure, the data appeared in point format and could be viewed in survey view or plan view. It appeared with a black background and the process implemented for reversing this did not appear to work. Labels were then added to the point data from within the View menu by selecting point labels followed by feature code. These techniques and resulting labelled customimport are revealed in Figure 42 and Figure 43.

3.4 Feature and Attribute Editor

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Attributes containing colour and style were added to the feature codes. This allowed the different features to be easily distinguished from one another. Several different geophysical survey techniques were undertaken for the survey and different colours were applied to each technique. For example, the GD line was assigned a green colour. To accomplish this task, the feature and attribute editor was activated by clicking the tree icon highlighted below.

The new name was typed into the resulting new feature code window similar to the one represented below in Figure 45. This created a new line style tab in the main features and attribute editor window.

The GD line served as an example for the purposes of this report. The same procedures were followed for the remaining features except the point feature, where a new point style was edited rather than a new line style. The new line style tab was selected and this opened a new line style properties window. The name, colour and style of the line were selected and are clearly displayed in Figure 46.

Next, the feature code properties window was reactivated where its newly created name (GD). The line tab was then selected, followed by GD from the line style menu and finally the layer option was changed to GD as depicted in Figure 47.

The point style method is illustrated in Figure 48.

The feature codes were saved as a .fcl file. Figure 49 portrays the final feature code library.

The newly created codes could then be assigned from within TGO using the process feature codes option from within the CAD selection as demonstrated below.

The lines and points were then displayed in the colours and styles chosen for them whilst in survey view. See Figure 51 for visualisation.

New lines were also added within TGO by using the insert line function from the CAD menu. The insert linework window opened as a result and enabled a line to be drawn between the required points (g1 and g6). The correct line style and layer were also selected. See Figure 52. Figure 53represents the result of the procedure.

3.5 Exporting Survey as a ShapefileAfter successful completion of the survey, it was possible to export the survey file into ArcGis. The export option was implemented, followed by the GIS tab from the resulting window and the first option was selected from the menu as depicted below.

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Finally the survey was exported as a shapefile leaving all the default settings, but also the grid coordinates setting was ticked, as evident below. The files were saved in the export folder within the main project folder.

3.6 Importing into ArcGISThe coordinate system was set for the data frame which was also the same for the survey. Firstly a geo rectified image of the South Campus of Maynooth was added (also with the same coordinate system). There were a number of different ways to complete the next step. For the purpose of this assignment, the coordinate system for the newly created shapefiles within the export folder were assigned by using ArcCatalogue, then selecting the ITM for the Republic of Ireland for each one before adding them to the table of contents. This could also have been completed by using the add data button, followed by right clicking on the layer, then data, export data and finally, use the same coordinates as the data frame option. Figure 56 illustrates the survey import.

Also the points and lines created in the survey were labelled from within the ArcGIS software package. Finally a grid was added to the layout view for printing. Within the data frame properties window grids was selected, followed by measured grid in the grids and graticules wizard window, as evident in Figure 57 and 58 respectively..

The result is displayed in Figure 59 below.

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Figure 8: Grids in Layout View

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4. Results

4.1 Surface InterpolationThe raster wind surface produced from the GPS coordinated point shapefile, produced varied results around the south campus of Maynooth. The interpolation used for this study was the Kriging method. After studying the outputs of both the IDW and Kriging methods and researching various literature, this method appeared to be more accurate and sophisticated (Ziaty & Safari, 2007) and (Childs, 2004). The areas closest to the buildings, in particular to the north-east of the image, appeared to have lower wind speeds on average. The more exposed areas situated in the mid to north west regions of the image appeared to have the strongest average wind speeds. The areas that were greater than or equal to 5 m/s were of interest to the user for the purpose of this study, as values less than 5 m/s were not deemed to be economically viable (Li, et al., 2011). The image in Figure 60 highlights the kriging interpolated surface and Figure 61 displays areas with values greater than 5 m/s in orange and areas that do not meet this requirement in black.Therefore, the orange areas had the potential to generate electricity by means of micro wind turbines. The results deemed that the areas in black were obsolete and therefore, were not applicable in the model.

Also, the remaining three criteria discussed earlier in the methodology section impacted on the result of the model. The criteria required that no area with a dense population of trees, or boundary or any building could be within 14 metres of a potential surface that may be used for micro wind turbine structures. Below are the results of the three criteria, 14 metres from trees, boundaries and buildings respectively. For all images, the orange expanses were the suitable areas and the black zones were unsuitable.

After these four separate criteria were combined using the raster calculator as explained in the methodology section, they excluded any locations that did not fulfil the requirements of the study. They produced a single image containing all suitable areas in green and unsuitable areas in grey. The results are clearly visible in Figure 63 below. A detailed PDF of the final results is attached as an appendix at the end of this report. All final output images were assigned grids in the same way as explained in section 3.6. The grids were formed by a series of Northings and Eastings intersecting one another, similar to the grids on a map. They permitted features to be identified easily by using grid references. See appendices 16, 17, 18 and 19 for visual representation.

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Figure 9: Final Output

The results showed that through the use of the available data collected, the south campus of Maynooth University has the potential to produce energy by means of micro wind turbine generation. The study area had the required space to accommodate the structures, while maintaining the recommended distance from structures and boundaries. Furthermore, it also possessed sufficient locations where the mandatory 5 m/s average wind speed was available in order to make the micro wind turbines economically viable.

4.2 Accuracy Assessment4.2.1 Ground Measurements Compared to Image MeasurementsThe results of the image rectification process appeared to be relatively accurate. Several measurements and enquires were implemented between the rectified image and by means of ground measurements taken by the researcher. The locations that were measured on the ground were then compared to the features on the image using the measurement tool within Erdas. The areas measured were from pillar to pillar at the orchard, the distance between the old goal posts at the back of the campus near the farm, and from kerb to kerb in the staff car park near the pay and display area. The measurement between the pillars at the orchard is represented in Figure 66. The measure tool was activated and a polyline was drawn between the two points (outlined in red circle). Erdas then produced a table with the measurements according to the image. For this measurement, the real life measurement was 3.05 metres, whereas on the rectified image, the measurement was 3.04 metres. This comparison suggested that the real life measurement and the image measurement differences were minimal. The real life measurement for the goal posts was 5.7 metres and the real life measurements between the two kerbs in the car park were 21.7 metres. The comparative measurements taken from the rectified image are documented in table 4.

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Figure 10: Measurement Tool

Figure 11: Measurement between Kerbs at Carpark

Figure 12: Measurement between Goal Posts

The results of the ground measurements proved to be highly accurate when compared to the rectified image. This was instrumental for the user in order to produce a realistic plausible spatial model when combing the image with the collected wind data.

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4.2.2 Comparison between Rectified Images Using Inquire ToolWhen the two images were compared using the inquire tool, the differences between the two appeared to be very slight. In figure 69, the goal posts are slightly misaligned from one another in relation to the cross hair. Also the cross hair is on the centre of the sun dial in the first image in Figure 70, but slightly off-set to the bottom left in the second image. The features are highlighted with a red circle in both images.

Figure 13: Inquiry Tool for Goal Posts

Figure 14: Inquire Tool for Sun Dial

The measurement of the building in Figure 71 also displayed some variance. When the polyline was drawn across the length of the building in rectcamp2 (image on right), the same appeared line appeared to fall short along the length of the building in rectcamp1. The line is highlighted in red in Figure 71.

4.2.3 Assessing Accuracy Using Trimble SurveyAnother way of assessing the accuracy of the image was to implement the Trimble Geomatics survey. The survey was constructed using measurements taken from the same projected coordinate system as the rectified image. The survey was completed on a green area outside the front door of Rhetoric House. When the survey data was entered into geomatics office it was then exported into ArcGis as previously explained. When added to the table of contents within ArcGis, the survey image was superimposed on top of the rectified image. When the image was zoomed in to the new survey layer, it fit exactly on top of the area that was surveyed. This proved that the image rectification was of high quality, as otherwise the survey would not have matched and aligned geographically with the feature on the rectified image. The result is evident in Figure 72

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5. Discussion and Conclusion

5.1 DiscussionThe results generated in this research study provided valuable insight into the available land that could be utilised as a means of micro wind turbine energy generation, for the south campus at Maynooth University. The complex analysis provided a high quality assessment of the available land contained within the south campus. The analysis included, strict professional guidelines and requirements concerning spatial constraints and physical wind speednecessities needed to achieve economic viability(Irish Wind Energy Association, 2014) and (Li, et al., 2011). The best possible locations were clearly identifiable after intensive andthorough querying of the available data. After reviewing literature that identified the negative effects of our reliance on fossil fuels, both for the environment and society in general (Pieprzyk,et al., 2009), research methods such as thesemay prove a valuable asset when assessing possible future renewable energy sources.Ireland has evolved in its knowledge and understanding of micro wind turbine generation, but work still needs to be done to become more efficient (Li, et al., 2011). Plans have been put in place to greatly reduce our fossil fuel consumption and increase our renewable energy production by 2030 (Department of Communications,Energy and Natural Resources, 2015). Therefore, unobtrusive forward planning is essential for providing information that relates to recorded data, but also visual representation concerning areas and locations that have the potential to host renewable energy sites, such as the south campus at Maynooth University.An important factor to note is that, micro wind turbine generation does not require vast quantities of land like the quantity needed for the larger full scale industrial turbines. They reach heights of up to 150 metres and need huge clearances between structures and also a higher average wind speed of at least 9 m/s (Irish Wind EnergyAssociation, 2012). These smaller structures can be constructed in peoples homes, businesses or farms, making their application less troublesome, with huge potential for their productivity.However, the regulation time scale to monitor the area in which a location for a micro wind turbine is proposed, is a minimum of 365 days (Irish Wind Energy Association,2014). Although this study was over a short time period of one month, it offered valuable insight relating to the possible potential of the campus to provide such energy generation. The study was by no means a definitive recommendation, but a plausible model that could be implemented if provided with stationary anemometers at certain locations around the campus for the required temporal period. The combination of Remote Sensing and GIS technology and processes, have proved to be effective research tools and may provide a bright future for maximising our renewable energy production in the future.Additionally, the image rectification proved to be highly accurate and could therefore be considered successful. This was fundamental for assessing the potential of the south campus for micro wind generation, as the assessment criteria were dependent on accurate spatial measurements relating to features on the campus. Consequently, this extended the confidence of the researcher as regards to the precision and correctness of the model.

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5.2 ConclusionThis report provided significant understanding into the complexity of producing a plausible model for micro wind turbine generation. The study had to follow meticulous regulations that related to physical wind energy requirements, but also spatial constraints relating to the proximity of the turbines to structures and boundaries. The researcher also faced challenges of how to best interpret, interrogate and display the data as a final image. Several issues needed to be addressed before the modelling could even begin. Issues such as; image rectification, which involved selecting, collecting and implementing suitable GCPs. Moreover, data collection, data input, adding coordinates to the collected data and converting the data to a point shapefile with recognised XY coordinates. Also, several features needed to be digitised and assigned attributes, so that they could be incorporated into the modelling. After successful completion, the data could be modelled using spatial interpolation combined with specific constraints using map algebra functions. The combination of all these stages produced a highly detailed and specified output that followed precise guidelines, in order to generate a sophisticated plausible model of extremely high quality. For future improvements, stationary anemometers need to be situated in various locations around the campus. This study provided a positive result relating to the potential of the south campus at Maynooth University. However, the time scale at which the study was undertaken was too short. The study did not allow for changes in wind conditions throughout the day. Wind speed fluctuates throughout the day and can be very changeable. The measurements were recorded in the afternoon over the period of one hour, therefore the results cannot be considered as being an exact figure for the entire day. In addition, it did not account for changes in wind conditions throughout the week as recordings were only taken twice weekly. The results should only be considered for the proposal of implementing stationary anemometers and not micro wind turbine structures. Conclusively, this research highlighted the importance and flexibility of remote sensing and GIS techniques for producing genuine models from real life data unobtrusively and at a relatively low cost, with excellent efficiency. Additionally, these techniques can be incorporated into a diversity of scenarios and have proven to be a valuable research and development tool.

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Bibliography

Aherns, D. C. & Samson, P. J., 2010. Extreme Weather and Climate. Belmont: Brooks/Cole Cengage Learning.

Childs, C., 2004. Interpolating Surfaces in ArcGIS Spatial Analyst. [Online] Available at: www.esri.com/news/arcuser/0704/files/interpolating.pdf[Accessed 12 April 2016].

Department of Communications, Energy and Natural Resources, 2015. Ireland's Transition to a Low Carbon Energy Future - 2015-2030, Dublin: Department of Communications, Energy and natural Resources.

Gibson, P. & Power, C., 2000. Introductory Remote Sensing: Digital Image Processing and Applications. London: Routledge.

Heywood, I., Cornelius, S. & Carver, S., 2006. An Introduction to Geographical Information Systems. 3rd ed. Essex: Pearson.

Irish Wind Energy Association, 2012. Best Practice Guidelines, Cork: Fehily, Timoney and Company.

Irish Wind Energy Association, 2014. Wind Micro Generation Step by Step Guide, Naas: IWEA.

Li, Z., Boyle, F. & Reynolds, A., 2011. Domestic Application of Micro Wind Turbines in Ireland: Investigation of Their Economic Viability, Dublin: Dublin Institute of Technology.

Pieprzyk, B., Kortluke, N. & Hilje, P., 2009. The Impact of Fossil Fuels - Greenhouse Gas Emmissios, Environmental Consequences and Socio-Economic Effects , Germany: Energy Research Architecture.

Sustainable Energy Authority of Ireland, 2016. Wind Energy. [Online] Available at: http://www.seai.ie/Renewables/Wind_Energy/[Accessed 15 March 2016].

Ziaty, Y. & Safari, H., 2007. Comparison Methods Interpolation IDW and Kriging in Make Up of Land Price, Hong Kong: Strategic Integration of Surveying Services.

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Wind Report 2

Documents

Transcript of Wind Report 2