At present there exists a great deal of GIS providing data on renewable energy on local and international level. An overview of GIS commonly used in Russia and USA is given below. All the data used in the overview is available as project descriptions given on the web portal being discussed. All the systems have been analyzed from the point of view of the data used on the portal, visualization tools and maps (wind, solar etc.).
Data
The data is the main valuable source of such systems. If certain data is confidential there exist several open source datasets providing correct data on renewable energy, and in some cases, with relatively high precision. Table 2.2 provides a brief overview of some datasets with information on solar energy.
Besides using these datasets it is also possible to use NASA SSE, open source datasets on solar energy, providing information about the whole Earth surface on the (1х1)º grid. According to the researches held on the territory of Russia during several years, the NASA SSE data provides a sufficient accuracy level [10].
Visualization tools
The main issue in creating geographical information systems is finding the way of representing features being visualized (geographic parameters in this particular case).
There exist several ways of describing geographical features.
Firstly it is necessary to recognize the data types. Normally two data types are defined:
Spatial data (data describing the location)
Attribute data (data specifying the characteristics of the spatial data i.e. what, when or how much) [11]
Secondly, it is needed to find the way of representation of the data in the GIS. Digitally it is possible to represent the data by grouping it into layers or by selecting appropriate data features.
Data grouping by layers is focused on finding similarities or relevant features in the target data (these features may be the source type as hydrography, elevation, water lines, sewer lines, grocery sales). In this case it is possible to use one of the following data models:
vector (data model using coverage in ARC/INFO, shapefile in ArcView)
raster data model (GRID or Image in ARC/INFO & ArcView) [1]
Selecting data properties should be done for for each layer separately. Features are chosen with respect to:
projection
scale
accuracy
resolution
Finally it is necessary to find the means of data incorporation into a computer application system.
It is needed to discuss each data type separately.
Spatial data types are represented as continuous, areas, networks and points.
Continuous data types are divided into:
elevation
rainfall
ocean salinity
Areas are generally defined as:
unbounded: landuse, market areas, soils, rock type
bounded: city/county/state boundaries, ownership parcels, zoning
moving: air masses, animal herds, schools of fish
Networks may be classified as:
roads
transmission lines
streams
Points are generally described as:
Fixed ones: wells, street lamps, addresses
Moving ones: cars, fish, deer
Attribute data types are generally defined as special data tables that contain locational information in the form of addresses, a set of longitude/latitude coordinates (or x/y) etc. Systems like ArcView consider these data tables as event tables. However, the spatial data in the real system is described as a shape file. Thus, all the event tables are to be converted to the strict format. In order to convert data to a shape file format, it is possible to use geocoding, and later display the data as a map.
Maps
In common case all the maps provided by renewable energy monitoring systems are classified with respect to the energy source they are representing. They are also classified concerning the means of their creation (i.e. raster and vector data model).
Raster data is simple and faster to realize but in certain cases the map resolution may not be sufficient enough for proper analysis.
Vector data model requires more complicated and sophisticated tools but it is, in turn, a correct way of weather data representation. Thus, vector data model provides high quality resolution which may be extremely important in certain cases.
In the next article we are going to give a more detailed analysis of the existing monitoring platforms.
References
[1] Fernandez Pierna, J.A. Combination of Support Vector Machines (SVM) and Near Infrared (NIR) imaging spectroscopy for the detection of meat and bone meat (MBM) in compound feeds // Journal of Chemometrics. -2004. -#18. – 349 p.
[2] "Russia’s Renewable Energy Sources" GIS // http://gisre.ru/menu-about: 3.04.2015
[3] Yohanis,Y; Popel,O; Frid,S; Norton.B Geographic Variation of Solar Water Heater Performance in Europe // Proceedings of the I MECH E Part A. // Journal of Power and Energy. – 2006. – Vol.220, No. 4. – P. 395-407.
[4] The NASA Surface Meteorology and Solar Energy Data Set // http://eosweb. larc.nasa.gov/sse: 18.07.2015
[5] "NREL Photovoltaic Research Home". Photovoltaic Research // http://www.nrel.gov/pv/: 25.07.2015
[6] NATIONAL RENEWABLE ENERGY LABORATORY NREL – 25 Years of Research Excellence 1977–2002 // http://www.nrel.gov/docs/gen/fy02/30845.pdf : 21.07.
[7] The NASA Surface Meteorology and Solar Energy Data Set // http://eosweb. larc.nasa.gov/sse: 18.07.2015
[8] EUROSOLAR-Memorandum zur Einrichtungeiner Internationalen Agentur für Erneuerbare Energien (IRENA) // http://www.eurosolar.de: 3.05.2001
[9] Abu Dhabi News. Speedier adoption of renewable energy mooted to offset climate change // http://www.abudhabinews.net/index.php/sid/225556185: 9.09.2014
[10] О.С.Попель, С.Е.Фрид, Ю.Г.Коломиец, и др.Атлас ресурсов солнечной энергии на территории России. М.: ОИВТ РАН, 2010.85 с.
[11] Ishmanov A.O., Alikhojayev I.B. Spatial data consolidation for decision support in the field of green energy // The International conference information technologies and management. – Riga: Information Systems Management Institute, 2015. – P. 90-91.
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