Baba, AbdullatifBaba, Abdullatif2024-06-252024-06-2520210957-4174https://acikarsiv.thk.edu.tr/handle/123456789/1316In this paper, we compare the efficiency of three different techniques used to predict the daily power consumption for a local industrial region (the studied case). At first, a variant of the Multiple Model Particle Filter is suggested as a probabilistic approach. Then, two different ANNs with one and two hidden layers respectively are designed and tested. Finally, we demonstrate a developed ANN-based design that has the ability to adapt its own structure according to the historical fluctuations provided by a given dataset that contains the consumed power for the same regarded region between 2011 and 2015; 1825 days. The potential of AI-based techniques will be emphasized by summarizing a complement heuristic study that employs the genetic algorithm to suggest an optimal outage schedule for the generators supplying the upper-mentioned region to accomplish maintenance activities that could be needed from time to time or to rest some of the units if the predicted consumption for a given period doesn't require the total produced power.EnglishPrediction; Consumed energy; Multiple Model Particle Filter; Artificial Neural Network; Genetic algorithm; Smart gridsBACKPROPAGATION; ALGORITHM; DEMANDArticle