A NUMERICAL WEATHER PREDICTION (NWP) MODELLING-BASED APPROACH FOR WIND ENERGY FORECAST OVER AN INDIAN REGION

Abstract

Climate change is a real problem that the society is facing today. It is evident that anthropogenic activities like burning of fossil fuels for electricity production has been adding more and more greenhouse gases (like CO2, CO) in the environment. These gases act as a blanket around the Earth, trapping Sun’s heat and raising temperature which is ultimately contributing to climate change. Several researchers have indicated the rising demand for early mitigation measures to be taken to tackle climate change. One such measure can be switching from coal burning to renewable energy sources (biomass, geothermal, hydro, solar and wind) for our daily energy requirements. Wind energy is a leading renewable energy source used worldwide due to the abundance in availability. In the Indian context, some states can produce high amounts of wind energy and help meet the energy requirements of a larger population. One such state is the state of Gujarat, which is also the study area chosen for this work. For maximum power generation a suitable site is required. Site selection is done by analyzing the meteorological conditions such as wind speed and temperature. Wind speed is then used to calculate the wind power density, which further helps decide the location of the wind power plant. To obtain the wind speed data, we need scientific instruments installed at various locations and meteorological stations to be set up in the concerned areas. This requires large amounts of capital expenditure, and it is also only possible to set up a weather station at few locations. For this purpose, Numerical Weather Prediction Models (NWP) can be used to predict the weather conditions over a large area, reducing the need to install weather stations. In this study, the WRF model was used to analyze the wind speed and temperature at various locations, 17 in total, in the state of Gujarat, India. The model setup has been done over the Gujarat region on two domains at a very high horizontal resolution of 5 km2 and 1 km2. The model was run for the first 14 days of five months (February, March, April 2023 and February, March 2024), reaching a total of 70 days. Finally, the spatial and time-series plots for wind speed, wind power density and temperature were generated to understand the conditions in the study area. Comparison of WRF data was done with ERA5 global reanalysis data and observational data from IMD for validation purposes. Statistical methods like RMSE, MAB and CC were also used to assess the closeness of modelled data to the reanalysis and observed data.