Progress Report 1

 We are excited to announce the beginning of our MM466: Machine Learning project: the development of an AI-driven prediction tool designed to forecast Savonius turbine performance. Leveraging the power of artificial intelligence and regression techniques, this project promises to redefine the way we optimize the efficiency of these vertical-axis wind turbines.

Physical testing is tedious and time consuming, additionally the experimental setup for physical testing can be costly, as such the development of AI prediction tool decreases time and makes testing more efficient 

Our strategy entails employing cutting-edge regression techniques to construct a predictive model. This model will incorporate data on the wind's entry angle and utilize it to forecast Savonius turbine performance accurately. 

Our aim is to offer valuable insights that can assist engineers and renewable energy enthusiasts in optimizing these turbines' efficiency.

Data Collection Milestone Achieved

We are delighted to report that the initial phase of our project, data collection, has been completed. This dataset encompasses more than 900 data sets for four different entry angle ( 0 10 20 and 30), each observation had three trials to make the data more extensive 

Next Step: Data Exploration

With our extensive dataset in hand, we are now embarking on the data exploration phase. This critical step involves delving deeply into the dataset to uncover hidden patterns, correlations, and insights that will guide the development of our predictive model. Our dedicated team of data scientists and engineers is eager to dive into this exploration process.

Stay tuned, we invite you to stay updated on our progress in this exciting project. As we advance through the data exploration phase and transition to model development, we will eagerly share our discoveries and insights with the community. We firmly believe that open collaboration and knowledge sharing are key to advancing renewable energy solutions.