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Convolutional Neural Network with Genetic Algorithm for Predicting Energy Consumption in Public Buildings
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Orientador(es)
Resumo(s)
Due to their capacity to improve energy consumption performance, intelligent applications have recently assumed a pivotal position in the energy management of public buildings. Because of their irregular energy consumption patterns and the lack of design criteria for energy efficiency and sustainability solutions, keeping these buildings’ energy consumption under control is a significant issue. As a result, it is important to analyze public building energy consumption patterns and forecast future energy demands. Evidence like this highlights the need to identify and categorize energy use trends in commercial and institutional structures. This research aims to identify the most effective intelligent method for categorizing and forecasting the energy consumption levels of public buildings and, ultimately, to identify the scientific rules (If-Then rules) that will aid decision-makers in establishing the energy consumption level in each building. The goals of this research were accomplished by employing two intelligent models, the Elbow technique and the Davis and Boulden approach, to count the number of clusters of energy consumption patterns. It was determined what the clustering levels would be in each structure using K-means and a genetic algorithm. In this step, the genetic algorithm was utilized to find the best centroid points for each cluster, allowing the fitting model to function better. Determining which buildings use the most energy has been made easier thanks to the extraction of If-Then rules from cluster analysis. Convolutional neural networks (CNNs) and CNNs combined with a genetic algorithm were also employed as intelligent models for energy consumption forecasting. At this point, we utilized a genetic algorithm to fine-tune some of CNN’s settings. CNN with genetic algorithm outperforms on CNN model in terms of accuracy and standard error. Using a genetic algorithm, CNN achieves a 99.01% accuracy on the training dataset and a 97.74% accuracy on the validation dataset, with accuracy and an error of 0.02 and 0.09, respectively. CNN achieves a 98.03% accuracy and a 0.05 standard error on the training dataset and a 94.91% accuracy and a 0.26 standard error on the validation dataset. This research is useful for policymakers in the energy sector because it allows them to make informed decisions about the timing of energy supply and demand for public buildings.
Descrição
Abdelaziz, A., Santos, V., & Dias, M. S. (2023). Convolutional Neural Network with Genetic Algorithm for Predicting Energy Consumption in Public Buildings. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3284470---This work has been supported by Portuguese funds through FCT - Fundação para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04466/2020, and this work has been supported by Information Management Research Center (MagIC) - NOVA Information Management School.
Palavras-chave
Energy Consumption of Public Buildings Convolutional Neural Network K-Means Genetic Algorithm General Computer Science General Materials Science General Engineering SDG 7 - Affordable and Clean Energy