Power Forecasting Using Deep Learning Algorithm

#Deep #Learning #Algorithm

On 23^rd January, 2021, IEEE BUBT Student Branch had arranged a seminar on Power Forecasting Using Deep Learning Algorithm. The purpose of the seminar was to learn deep learning algorithm for power forecasting. In this seminar, our mentor was Dr. Abdul Motin Howlader. He had nicely presented the deep learning algorithm. At first, he was talking about the basic knowledge of power forecasting. He was talking about background of the renewable energy, importance of solar forecasting. Then he deep down into the network algorithms and talked about the machine learning process. And took a broad class about the algorithm.

Power forecasting is a technique to predict future energy needs to achieve demand and supply equilibrium. Power forecasting of renewable energy power plants is a very active research field, as reliable information about the future power generation allows for a safe operation of the power grid and helps to minimize the operational costs of these energy sources. Deep Learning algorithms have shown to be very powerful in forecasting tasks, such as economic time series or speech recognition. Up to now, Deep Learning algorithms have only been applied sparsely for forecasting renewable energy power plants. By using different Deep Learning and Artificial Neural Network algorithms, such as Deep Belief Networks, Autoencoder, and LSTM, we introduce these powerful algorithms in the field of renewable energy power forecasting. After a short discuss the seminar was over. E-certificate was given to the active participants.

The You Tube link of this seminar: https://youtu.be/22Y5iwaYwQs