University Projects Published in IEEE publications!

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  Solar power forecasting using ML Algorithms

  Team lead by Naveen with 3 members

  The industrial sector relies heavily on solar power forecasting because it is dependable and readily available. High solar energy penetration is essential for the financial sustainability of power systems. The fact that solar energy is actually renewable is the most significant benefit of all the features of solar panels. Forecast models incorporate information from numerical weather prediction (NWP) factors such as cloud cover, sunshine, radiation, precipitation, etc. However, there is NTP Uncertainty in the assumption of the atmosphere’s starting state. Advanced predictions are made in the meantime by machine learning algorithms. We need to estimate and compare the accuracy rates between Ann algorithm and Rnn algorithm for the outgoing solar power in this study using the dataset from the previous month.In the upcoming month, the crucial machine learning algorithm will be created.

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  Fuzzy clustering using Hybrid CSO-PSO

  Team Lead by Naveen with 2 members

  Recently COVID-19 virus had made a potential threat to humanity. The effect of mobility habits might be considerably high in the spread of disease. This work analyzes the mobility patterns in selected districts of Tamilnadu and applies Fuzzy C-Means clustering based hybrid variant of cuckoo search combined with best features from particle swarm optimisation. Google Community Mobility reports are taken for the experimental purpose and 26 districts in Tamilnadu are considered. The decision regarding the restricted movement of people can be made from the results. The correlation between human mobility during and after lockdown to the disease spread is analysed. The results are evaluated using internal indices like Silhouette and DB index.