Electric Power Production Modeling for Optimal Driving

Electric power production operators have adopted a new strategy to digitize its industrial processes. This is achieved by integrating connected sensors into equipment to collect data and enable real-time process monitoring, which ensures effective remote control and driving. In this context, our project is to optimize the operating parameters of the steam management and distribution process of Medium Pressure and Low Pressure at the level of a real thermoelectric plant, in order to maximize the production of electrical energy. After collection, we clean, filter and consolidate the data in such a way as to have a database containing all the necessary variables of the process. We have built a predictive model that enables the production of electrical power using a machine learning approach. This model, will be exploited for the development of a decision-making application.