Data Mining Approach for Wind Energy Prediction and Modelling Optimized Turbine Placement

Abstract

Now a day rising price of tradition fuels, environmental concerns, renewable energies has grown dramatically with regard to other conventional energies. On the other hand, wind power has experienced a bigger growth, among the renewable energies. In this research, presented evolutionary algorithm to look for the optimum primary design of the wind park, taking into account all the part involved wind speed; wind direction; number of wind turbines; wind turbine hub height and rotor diameter. Wind farms are gaining fascination as a cost newlineeffective energy solution compared to other non- newlineconventional sources of acquiring energy. A considerable amount of research work has been carried out to find the newlinelocation where wind velocity is optimum, to design layout plan to place wind turbines in a wind farm to be established at identified site. Emphasis is on the newlinecomputation analytics of finding location receiving optimum wind energy. newlineIn this research, optimization complications have been explored that control wind turbine performance parameters. The parameters were manipulated by R newlinestatistics and Java using R statistics built-in algorithms. The Optimized Turbine Placement Algorithm (OTPA) searches for the optimum wind speed, compliant newlinethe lowest wake losses and ensuring the maximum annual energy production of placed wind farm. newlineThis research elaborates two main goals. First to find optimised site wherewind speed is suitable for wind turbine placement. This was achieved by executing recreations with the software, based on predefined flow conditions. The results of the reproductions were associated with power measurement data, taken from the Genetic Algorithm (GA) and Teaching- Learning Based Optimization (TLBO).ix newline