top of page
  • Writer's pictureAlibek Jakupov

Renewable Energy sources monitoring: Part 1

Updated: Nov 19, 2021


At present there exists a great deal of GIS providing data on renewable energy on local and international level. In case of lack of field measurements the idea of using open source data along with field actinometrical measurements on the certain (or existing) sites seems to be efficient from the point of view of accuracy and costs. An overview of GIS commonly used in Russia and USA is also provided in the work. All the data used in the project is available as project descriptions provided on the web portals. 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.). Nowadays, the amount of data being produced and stored increases rapidly. Due to the rapid development and low cost of data transmission and storing tools the amount of stored data is steadily reaching enormous values.

Naturally, in order to process all this data and process information ‘usefully’ it is necessary to develop more complicated methods and, in some cases, new fields of study. Moreover, from the point of view of processing speed more powerful calculation capacity is required. Modern development of cloud computing allows to access, in the short period of time, almost unlimited resources for data processing and representation, and, in case of shortage, to broaden and deepen the needed data. Most of the spatial datasets suitable for energy source monitoring are presented in textual format. Due to the large amount of the information they are impossible to be processed directly. Thus, the main goal is facilitate the data perception as much as possible. Spatial data visualization is one of the most recently revealed issues in big data analysis whose parameters are defined by geographical coordinates.

Generally, datasets contain geographical coordinates in of the existing map projections and describe certain energy source or event at the particular point on the map. Creating architecture of a system providing receiving and processing large amount of data in real time scale requires a lot of investigations. One of the main reasons of this is an absence of standard methods or systems providing a deep and profound vision of a large collection of heterogeneous data on data sources, analysis and representation in sight of view multilayer geographic information system. The data visualization may be implemented by means of existing multifunctional GIS overviewed and briefly described in the research.

Besides using existing GIS for map creation it is also efficient to use program libraries, including open source ones. Ready solution sets are commonly available as web applications. These applications use server side for storing and processing user data. This data may later be used to create different type visualizations with the help of Javascript libraries and preprocessing on the server side. At present there is no unified classification of tasks concerning geographically/spatially distributed data visualization. But still it is possible to distinguish the most typical task classes as, for instance, classification, clusterization or heat maps. These classes may not be considered and treated as solid terms and have a wide interpretation. In practice, while solving concrete problems the most adequate and useful in application methods and tools of visualization are commonly chosen.

Necessity of renewable energy monitoring

Energy sources monitoring comprises data gathering and data collection from the different sources. Weather stations, weather measuring units, distant zoning dataset, satellites earth surface images may be considered as reliable data sources. Moreover the crowdsourcing data gathering is becoming more popular. Examples of such projects are SETI@home, Galaxy Zoo, Citizen Weather Observer Program (CWOP).

The latter project is aimed at collecting weather data with the help of community for weather forecasting and security services, providing user feedback in order to increase data quality. The data obtained is used by universities, research centers, weather forecasting services. In such kind of services data gathering and collecting is carried out by means of weather stations set by users, by measuring cell phone batteries etc. For instance the OpenWeatherMap project uses the data obtained from the amateur weather stations to verify the weather datasets as in forecasting models, the main factor is the measurement sites number, which is more important than the measurement accuracy. In the context of the modern situation in Kazakhstan, the problem of data collection and gathering remains actual because of lack of spatial data specific to the region. For instance, to evaluate the potential of solar and wind energy, it is necessary to get information on weather on the whole territory with high resolution, including the time parameter.

Wind energy and direction, insulation and precipitation level influence directly the work of power stations using solar or wind energy. In order to calculate these parameters with respect to the chosen territory the weather stations are sited. According to the NASA Global Summary of Day (GSOD) in 2015 the total coverage of weather stations entering the World Meteorological Organization (WMO) network on the Kazakhstan territory was 1 to 7590 km2 (according to NASA GSOD datasets) that is extremely low in comparison with Europe and USA. Such a small number of weather stations does not allow evaluating the energy potential of weather conditions sufficiently accurately.

Besides these stations there are local weather stations networks. It is possible to apply the distant probing data but this method does not give sufficient accuracy rate near the surface which is necessary as the majority of the renewable energy power stations are to be installed on the surface. A large number of weather stations makes it possible to increase the quality of forecasting models as the energy potential evaluation, in this case, is much higher. At present these models are commonly based on distant probing data and interpolation of this data.

In the next article we are going to discuss data formats and data sources applied in such monitoring systems.

33 views0 comments


bottom of page