The rapid rate of transformation in the power sector of India has placed a significant emphasis on robust grids and distributed generation units. The observable shift in the energy sector, especially in wind and solar energy, also requires smooth integration of Distributed Generation units with the existing power grid. Precise wind power generation forecast, therefore, becomes an important and complex task for the strategic planning and management of the systems. We, thus, aim towards a system that can actually provide precise wind power forecasts by applying machine learning techniques. This work proposes a comparative and comprehensive analysis of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) model. The experimentations and modelling are performed considering meteorological and historical power generation data. The study is concentrated in Kutch, Gujarat and is validated on the data collected from the Central Electricity Authority (CEA), India for power generation data and weather data collected from regional weather centres. The findings show that ARIMA outperforms the other models for non-linear data in multivariate analysis, with a MAPE score of 5.87 on the prediction dataset.

Wind energy has witnessed an upswing, with the improvement in current technology and cost-effective electricity production but due to uncertain wind behaviour and weather trends, it is essential to forecast wind energy. The paper presents the Autoregressive Integrated Moving Average model, a statistical analysis model, to forecast future power generation values, for the Kutch region of Gujarat, India. The historical wind power generation data and weather parameters have been taken into consideration for the predictive analysis of future trends in power generation. Wind power generation is dependent on various weather factors like wind speed, wind direction, temperature, humidity, air density, etc. The historical data obtained from Central Electricity Authority (CEA), India, and weather data collected from regional weather stations have been made into use for the forecast. A summary of the results is shown using the performance metrics for model evaluation, indicating that the model can forecast wind power generation with higher accuracy.