Optimization of Random Forest Prediction for Industrial Energy Consumption Using Genetic Algorithms

Abstract   Saving electrical energy consumption in industries is crucial; hence, the prediction of industrial energy consumption needs to be performed. The random forest method can be applied to steel industry data to predict energy consumption. The purpose of this prediction is to increase energy savings in industries and optimize the performance of the random forest method. The results of the random forest show that the algorithm can predict energy consumption in industries effectively; however, it needs further optimization to achieve better predictions. Therefore, the genetic algorithm method will be used to optimize the previous method. The optimization results indicate that it is successfully conducted in terms of accuracy and kappa level. This optimization is beneficial to society, especially industrial companies

A genetic algorithm for forest firefighting optimization

In recent years, a large number of fires have ravaged planet Earth. A forest fire is a natural phenomenon that destroys the forest ecosystem in a given area. There are many factors that cause forest fires, for example, weather conditions, the increase of global warming and human action. Currently, there has been a growing focus on determining the ignition sources responsible for forest fires. Optimization has been widely applied in forest firefighting problems, allowing improvements in the effectiveness and speed of firefighters’ actions. The better and faster the firefighting team performs, the less damage is done. In this work, a forest firefighting resource scheduling problem is formulated in order to obtain the best ordered sequence of actions to be taken by a single firefighting resource in combating multiple ignitions. The objective is to maximize the unburned area, i.e., to minimize the burned area caused by the ignitions. A problem with 10 fire ignitions located in the district of Braga, in Portugal, was solved using a genetic algorithm. The results obtained demonstrate the usefulness and validity of this approach.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R &D Units Project Scope UIDB/00319/2020 and PCIF/GRF/0141/2019: “O3F - An Optimization Framework to reduce Forest Fire” and the PhD grant reference UI/BD/150936/2021