1 Abstract— 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. Keywords— ARIMA, Power System, Renewable Energy, Wind Power forecasting I. INTRODUCTION Depleting natural resources and increasing global pollution have wheeled the development of the Renewable Energy industry. Human evolution has been fuelled by fossil fuels which are exhaustible and finite in number. The use of these non-renewable fuels has now become a major threat to the existence of mankind. The introduction of renewable energy sources instead of coal and oil-fuelled power plants has provided an alternative to conventional energy sources. Energy is vital for the economic and infrastructural development of the country. With the increasing demand for electricity in a developing country like India, energy sources like Solar Power, Wind Energy, and Geothermal Energy sources are the highlights for the days to come. The Indian economy and growth have been so far anchored by fossil fuels. Recently, the wind power sector has witnessed a tremendous growth rate providing a key opportunity for the expansion and its integration into the power grid. With reference to the report by the Ministry of New and Renewable Energy, Govt. of India, India produces 42.633 GW of wind power making it the four largest installed wind power capacity in the world [1]. Accounting for about 10% of India’s total installed energy generation capacity, Wind energy is India's oldest developed renewable energy technology [2]. More than 800 wind monitoring stations have been installed nationwide by the Indian government through the National Institute of Wind Energy (NIWE), which has also produced wind potential maps at elevations of 50, 80, 100, and 120 metres above ground level. According to the most current study, the country has a total wind power potential of 302 GW at 100 metres and 695.50 GW at 120 metres above ground level [1]. Fig. 1. Generation Category-wise Installed Capacity (as of 18/04/2023) [3] Studies rank the state of Tamil Nadu with the highest installed capacity, which is well ahead of Gujarat which ranks second in terms of installed capacity. But in terms of potential energy generation, Gujarat ranks first with 142.56 GW (at 120 m AGL) [4]. Agricultural and industrial activities demand an extensive energy supply which the country can be with fulfil through a properly balanced energy supply through renewable energy and conventional energy sources. Thus, an precise prediction is needed to strategize, plan, and to schedule the power generation for smooth integration with the current power grid. A forecast of anticipated load requirements is essential for power system expansion, for determining generation capacity, transmission, etc. Smart management and the inclusion of renewable energy are important in today's scenario to meet the increasing demands. Wind power forecasting has been a focal point for researchers, hailing to its significance in energy balance in the grid and for proper scheduling of power generation. Wind power forecasting methods broadly are of two types which include the physical method of analysing the physical quantity to obtain wind speed and then calculating the power thereafter, and the statistical method for establishing wind power prediction models by collecting historical data and training the model to yield future results [5]. Load forecasting is categorised into the following forms based on the forecasting horizon: Short-term (STLF), medium- A Statistical Analysis Model of Wind Power Generation Forecasting for the Western Region of India Sulagna M., Piyush H., Vineet S., and Pankaj R.