Affiliations: [a] Deparment of Electrical Engineering, Manav Bharti University, Solan, HP, India
| [b] International Institute of Health Management Research, New Delhi, India
| [c] Electrical Engineering Department, IIT Delhi, New Delhi, India
Correspondence:
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Corresponding authors. Abdul Azeem and Nuzhat Fatema, International Institute of Health Management Research, New Delhi, India and Deparment of Electrical Engineering, Manav Bharti University, Solan, HP, India. E-mails: nuzhat.fatemaa@gmail.com and nuzhat.fatemaa@gmail.com.
Abstract: Development of power through wind with the enhancement of renewable energy resources, frolics/romps a principal role in a developing country like India due to its censorious locations. Wind speed prediction in long term scenario has become a key research area in distinct applications (i.e., management of energy, optimal designing of wind farm, restructuring of electricity marketing, load-shedding and load forecasting). However, forecasting of accurate wind speed data for installation of wind turbine is very difficult due to its deterministic and probabilistic characteristics. The presented technique in this study may bridge the research gap related with the long term wind speed forecasting as resolve the previously indicated problems. Thence, two basically distinct techniques, k-nearest neighbors (kNN) algorithm and artificial neural network (ANN), have been implemented to forecasting of monthly wind speed of Indian cities. The uniqueness of the presented paper is to predict the wind speed in common form of incoming month by implementing the kNN algorithm. A dataset of current wind speed recorded specimen from 168 cities of India is utilized to train and test the proposed approach. Obtained results through the proposed approach have been validated by using ANN technique, which shows very small MSE.
