Lasse Blanke, Dipl. geographer (CEO) is employed at the anemos GmbH since 2009 as head of site assessment. Since 2020 he is managing director and co-responsible for research and development projects. Furthermore, he is in charge of due diligence processes with investors and project developers. For many years, Mr. Blanke has been regularly participating in the meetings of the German Wind Energy Association. Additionally, he is chairperson of different working groups within this association and participates in the working group "Wind potential" within the FGW (Association for Wind Energy and other Renewable Energies) for the development of the Technical Guideline TR6.

André Glücksmann, M.Ed. (CEO) is employed at the anemos GmbH since 2010 as the person responsible for developing software, automation solutions and maintaining the anemos server cluster. Furthermore he is in charge of supporting the mesoscale weather simulations and the corresponding IT infrastructure. He is involved in the development of the anemos wind atlas family and the market value atlases.
Starting 2017 he has taken over the management of the IT department. In 2020 Mr. Glücksmann received power of attorney.
Since 2021 he is managing director and responsible for IT management, determination of site quality after commissioning (TR 10), mesoscale modeling, windatlas, energy yield index and market value atlases as well as the anemos webtool awis.

Martin Schneider, M.Sc. is employed at the anemos GmbH since 2016 as the person responsible for mesoscale modeling and data validation. In addition to the development, optimization and verification of the anemos windatlases, he is responsible for the support and further development of the windatlas products, as well as for the area of ​​revenue and market values.
He is involved in ongoing research projects at anemos GmbH. Since 2018 he is in charge of quality management and is responsible for maintaining the accreditation.
Since 2021 he is authorized officer (Prokurist) and responsible for research and development, mesoscale modeling and the resulting products i.e. the anemos energy yield index.

The turbulence intensity (TI) and its forecast play a decisive role in the operation management strategies, since this has one of the highest influences on the wind turbine service life. The TI is parameterized and calibrated from the existing output values of the weather models. For calibration and quality estimation of the TI forecast model, anemos will develop the TI from mesoscale models with different horizontal resolution and investigate their quality using wind measurements.

In addition, TI is an essential parameter in the assessment of onshore site suitability and especially in the application of high-quality wake models in the context of energy yield calculations for offshore wind farms. Park shading is the largest loss factor for offshore wind farms and is subject to the highest uncertainty. Therefore, the time series of TI should improve the quality of park shading calculations. The objective is to establish mesoscale TI for wind applications. Therefore, the accurate determination of the TI is of great importance to gain the necessary acceptance in the wind industry.

In order to achieve the expansion targets for wind energy set by new government, it is necessary to develop a large number of wind farms in a short time. The basis for wind farm planning at a new site is the estimation of the expected energy yields and the selection of suitable wind turbines.

At present, the yield estimation is subject to high uncertainties. In addition, it is time-consuming and cost-intensive, especially due to the currently required one-year wind measurements. The aim of the project is therefore to enable qualitatively better yield estimates in a shorter time and at significantly lower costs through improvements along the entire process chain. To achieve this goal, procedures are being developed that provide a better data basis (e.g. reanalyses, roughness data) for the wind sector. In addition, innovative procedures from the field of data science such as machine learning or model ensembles are used at various points to enable an accurate estimation of energy yields in a shorter time. The merging of the various methods and data into an overall process enables a high degree of automation of yield assessments in addition to the increase in quality. Ultimately, the project thus creates the basis for a reduction in project risks for planners and project developers. In addition, the developed methods can also be used for more precise regional potential assessments and thus contribute to better planning of wind energy expansion.

anemos is playing a key role in the automation of the overall process in this joint project. Scientifically, anemos focuses on the development of methods for determining the site-specific power curve and time series-dependent loss models for improved yield estimates. Furthermore, anemos analyses and corrects the data basis (wind atlas) with regard to the long-term reference of short-term measurements for relevant seasonal deviations.

Many questions in the area of regional climate and air pollution control or small wind turbines need detailed knowledge about the long-term and representative near-surface wind conditions. These are calculated site-specific with dispersion models or microscale flow models. The main idea of the research project is the processing and the development of closed-meshed high qualitative meteorological data from the anemos wind atlas data for Germany. These data should be able to use for approval processes for dispersion calculations and nationwide air pollution control measures.

The Uncertainty is one of the important issues when modelling the wind conditions and energy yields of wind turbines. It is a combination of modelling errors (e.g. due to the finite model resolution), model assumptions (e.g. turbulence, stability) and the uncertainty of the input data (terrain data, land use, wind distribution, etc.). Consequently, any type of model is characterized by a certain scope of quality and validity.

In terms of linear models (e.g. WAsP), results of systematic investigations regarding the uncertainty are already available, whereby the site assessment by CFD-Modelling was not investigated in detail so far. The joint research project Sunday aims to fill this gap in the uncertainty of CFD-Modelling. In particular, quantitative knowledge about the dimension of each source of error and about the individual causes will be gained.

The main goal of the project is the generation of a proven wind atlas based on ERA5 reanalysis data that is adjusted and corrected with measured data.

As part of the tendering process for onshore wind energy in 2019 and 2020, maximum values ​​for offers will be introduced differentiated by region. The German Environment Agency requires a data basis in order to verify the defined high-value areas appropriately. This is realized by examining the wind conditions.

Until recent, the market value atlas depended on the anemos wind atlas, which was based on MERRA-1 reanalysis data (MODERN-ERA RETROSPECTIVE ANALYSIS FOR RESEARCH AND APPLICATIONS) by NASA, a global data set of atmospheric parameters. At the end of 2015, the calculation of the MERRA-1 data was terminated and replaced with MERRA-2 data. As a result, the anemos wind atlas and the market value atlas had to be completely recalculated in order to ensure consistency over the entire time period.

A verification of the market value atlas and an adaptation of the wind atlas to observational data is essential in order to prove their accuracy and to increase the acceptance of these products as well as to extend the number of users. The accuracy of the wind and market value atlas is determined by developing and adopting new modelling methods and comparing the atlas data with wind measurements and energy yield data from wind turbines.

The aim of the project is to provide a new frame for the determination of the site energy yield after 5 years for onshore wind turbines which are compensated according to the EEG 2017. A guideline for determining the site quality before and after commissioning is developed.

The main purpose of the sub-project was to develop a verified wind atlas for Germany, which is intended to be applied for determining the wind energy yield of one year throughout Germany.

SOPCAWIND is a web service to assist in the whole wind farm design process. It contains the SOPCAWIND data pool and an external data access interface (API), which combines various datasets to provide a view on development opportunities within large areas. The SOPCAWIND web tool is suitable for initial screening of large areas for development opportunities. It also contains a layout optimization module, optimizing by cost and yield.