A smart energy-efficient fire prediction system for a charcoal manufacturing plant

In recent years, there has been a tendency to make objects, buildings, and cities connected and smart. The themes of smart buildings, cities, and grids are new and behind these concepts, there is a digital revolution that is transforming all the sectors of society, including agriculture, finance, logistics, etc. It is the case of factories that use all the technologies that can allow them to operate efficiently while producing good performance. However, regardless of the technologies used, work in the factories presents risks, in particular fire risks for many reasons. For example, a charcoal manufacturing plant presents fire risks. In this regard, many technologies, with the help of the appropriate equipment are nowadays used to predict fires. For these equipments to have a long life, it is necessary to properly manage the energy with which they operate. For this reason, to ensure better energy management, a smart energy-efficient fire prediction system for a charcoal manufacturing plant is proposed. Problems related to excessive energy consumption by active equipment in a network need to be resolved. A dataset has been used to train and test data to predict the risk of fire with the help of four different machine learning algorithms. We also tried to optimize the energy consumption of the network by using Genetic Algorithm (GA) and SARSA algorithm, a type of reinforcement learning algorithm.