Project Abstract

The pressing need to increase the effectiveness of renewable energy sources, such as wind and solar power, is what drives this research. These sources are essential for lowering global dependency on fossil fuels and preventing climate change. The project intends to address both the environmental and economic difficulties in energy production by determining the best places for these facilities, which will result in a considerable reduction in carbon emissions and implementation costs. This research is unusual because it takes a complete, data-driven approach, combining machine learning techniques like K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), with statistical analysis. This technique aims to assess global temperature trends and climatic conditions, offering a strong foundation for the selection of sites for renewable energy.Large-scale climate data sets are gathered and analyzed as part of the techniques used in this work in order to train the SVM, ANN, and KNN models. This method ensures long-term applicability by allowing accurate forecasts of ideal sites based on environmental factors and adjusting to dynamic changes in climatic patterns over time. The primary goal is to create a forecasting tool that can successfully direct the advancement of renewable energy initiatives around the globe.Although the study is still in its early stages, preliminary results indicate that combining many machine learning algorithms can improve the forecast accuracy for appropriate areas.It is expected that the results of this research will provide a scalable model that will cut down on the time and expenses associated with site investigation and planning. A thorough map of possible locations that has been sorted according to appropriateness using the variables that have been examined is one of the expected outputs.When this project is finished successfully, it will offer important insights into the strategic planning of renewable energy resources. It will further our understanding of how data-driven tools can be used to promote a more sustainable energy future by advising stakeholders and policymakers on the best places to invest resources in order to take advantage of the potential of wind and solar energy. This might fundamentally change the way that renewable energy is developed and provide everyone with the information they need to make wise investment decisions in sustainable energy.

Keywords: Data Science, Machine Learning,

 Conference Details

Session: Presentation Stream 3 at Presentation Slot 4

Location: GH029 at Tuesday 7th 13:30 – 17:00

Markers: Deb Roy, Solmaz safari

Course: MSc Advanced Computer Science, Masters PG

Future Plans: I’m looking for work