1. Synopsis

Energy Consumptions around the world are expected to increase for supporting modern lifestyles, economic growth, and modern technologies. In 21st century, the demands and productions are provided with conventional electricity supplies. For a sustainable future and economic development, growing energy-mix to renewables is no longer an option but becomes a mandatory choice. Therefore, researchers and scientists strive to bring clean and affordable technologies for best practices among societies such that we share our responsibilities for a better future tomorrow acting from today. 

2. Scientific Aspects

Even though wind is one of free resources in the world, harvesting wind energy is a challenge because of its chaotic nature. While conventional sources are considered with environmental impacts, cost and benefits are equally emphasized to produce more energy options like renewables for a balanced energy mix. Considering installation costs with an affordable price, the evolution for turbine innovations are rapidly racing across the globe both for short-term and long-tern energy harvests. Eventually, We can now plan and model out our energy consumptions to meet our demands and needs. Depending upon resources and accessible information, we can provide and predict real-time energy harvests.

The entry provides a case study on Bangladesh, particularly, eight divisions from Hussain and Park (2021). After scrutinizing wind (m/s) speeds at various heights (m) and mean boundary layer height (BLH) from the European Center for Medium Range Weather Forecast reanalysis 5th generation (ERA5), climatological analysis includes trends, time series, anomalies, and linear correlations for 40 years from 1979 to 2018 with a baseline year (1979 to 2000). By Considering power-curve relationships, a simple power predictive model (SPPM) is developed using global wind atlas (GWA) datasets (sample: 1100) to estimate wind power at expected (hub) height. During model development, SPPM (model) are compared with GWA and found a linear correlation of 0.918 and 0.892 for Exponential (EXP) and Polynomial (PN) with mean absolute percentage errors (MAPE) of 22.92 and 21.8%, respectively. After validation with ERA5 and referred datasets, SPPM is utilized to provide an approximation for an annual energy production (year: 2018) representing two distinguished location: southern and northern regions in Bangladesh respectively 1748 and 1070 megawatt. With an approximation to 100 m (pressure level ~975 hPa at ERA5), SPPM prediction accuracy is about  84.29 and 94.14% (MAPE: 15.71 and 5.85%) for EXP and PN models, except Sylhet. SPPM model predictions and robustness further can be enhanced with on-site observations by incorporating wind speeds, direction, and turbine types for operational power plants in educational institutions, large-factories, Agro-lands, or housing society on demands.