Average Monthly Wind Power Forecasting Using Fuzzy Approach

The growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic increase cost, necessity and the reduction fossil reserve, for better power quality reliability, is obliging sector to use renewable based sources. In India, gradually becoming an important significant resource. Keeping opinion aforementioned prediction essential study harnessing prospective. This paper proposes effective technique on intelligent approach predicting different areas. using model concerning predicted gap its similar one two year old data. There are many conventional models existed literature like support vector machines (SVM), back propagation (BP) etc. this fuzzy logic predictive control have been developed offered microgrid application by air density speed input parameters system. outcomes compared with computed data existing it can be observed that errors found within permissible limits. obtained from very close calculated values if technique. Hence, proposed employed of selected stations. results Kolkata city outcomes. Error RMSE Support machine, Back propagation, Model forecast error correction +SVM +BP, Neural Network method, system 30.48%, 32.83%, 26.81%, 28.58%, 1.1431%, 1.38% 1.12% respectively. Therefore, techniques provide best even these suitable Additionally, achieved used Microgrid/SmartGrid applications.