Combining Machine Learning and Multi Criteria Decision Analysis Modeling Regulatory, Economic and Social Influences on Wind Turbine Allocation

Knowledge about the future allocation of wind turbines is relevant for assessments of energy markets or necessary grid expansions. In Germany, political decisions drive the allocation together with investment decisions, social rejections, land use planning, regional development and ecological aspects. Taking all influences into account, a standardized multi-criteria optimization problem combining economic suitability, residential burden and site suitability calculates the regional distribution of wind turbines as input for further assessments. By considering the political framework as boundary conditions for the optimization and detailed geographic area suitability factors using a machine learning approach as input parameters, it is possible to assess effects of regulatory restrictions on regional developments. We use a backtesting for validation and weighting of the objectives. Sensitivities of changing regulatory frameworks modeled as different boundary conditions show effects of changing political decisions.