A Proposed Intelligent Model with Optimization Algorithm for Clustering Energy Consumption in Public Buildings

Abdelaziz, A., Santos, V., & Dias, M. S. (2023). A Proposed Intelligent Model with Optimization Algorithm for Clustering Energy Consumption in Public Buildings. International Journal of Advanced Computer Science and Applications, 14(9), 136-152. [15]. https://doi.org/10.14569/IJACSA.2023.0140915 --- This work has been supported by Portuguese funds through FCT-Fundação para a Ciência e Tecnologia, Instituto Público (IP), under the project FCT UIDB/04466/2020 by Information Sciences and Technologies and Architecture Research Center (ISTAR-IUL), and this work has also been supported by Information Management Research Center (MagIC)-Information Management School of NOVA University LisbonRecently, intelligent applications gained a significant role in the energy management of public buildings due to their ability to enhance energy consumption performance. Energy management of these buildings represents a big challenge due to their unexpected energy consumption characteristics and the deficiency of design guidelines for energy efficiency and sustainability solutions. Therefore, an analysis of energy consumption patterns in public buildings becomes necessary. This reveals the significance of understanding and classifying energy consumption patterns in these buildings. This study seeks to find the optimal intelligent technique for classifying energy consumption of public buildings into levels (e.g., low, medium, and high), find the critical factors that influence energy consumption, and finally, find the scientific rules (If-Then rules) to help decision-makers for determining the energy consumption level in each building. To achieve the objectives of this study, correlation coefficient analysis was used to determine critical factors that influence on energy consumption of public buildings; two intelligent models were used to determine the number of clusters of energy consumption patterns which are Self Organizing Map (SOM) and Batch-SOM based on Principal Component Analysis (PCA). SOM outperforms Batch-SOM in terms of quantization error. The quantization error of SOM and Batch-SOM is 8.97 and 9.24, respectively. K-means with a genetic algorithm were used to predict cluster levels in each building. By analyzing cluster levels, If-Then rules have been extracted, so needs that decision-makers determine the most energyconsuming buildings. In addition, this study helps decisionmakers in the energy field to rationalize the consumption of occupants of public buildings in the times that consume the most energy and change energy suppliers to those buildings.publishersversionpublishe

Convolutional Neural Network with Genetic Algorithm for Predicting Energy Consumption in Public Buildings

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.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.authorsversionepub_ahead_of_prin

Modelling and Predicting Energy Usage from Smart Meter Data and Consumer behaviours in Residential Houses.

Efforts of electrical utilities to respond to climate change requires the development of increasingly sophisticated, integrated electrical grids referred to as the smart grids. Much of the smart grid effort focuses on the integration of renewable generation into the electricity grid and on increased monitoring and automation of electrical transmission functions. However, a key component of smart grid development is the introduction of the smart electrical meter for all residential electrical customers. Smart meter deployment is the corner stone of the smart grid. In addition to adding new functionality to support system reliability, smart meters provide the technological means for utilities to institute new programs to allow their customers to better manage and reduce their electricity use and to support increased renewable generation to reduce greenhouse emissions from electricity use. As such, this thesis presents our research towards the study of how the data (energy usage profiles) produced by the smart meters within the smart grid system of residential homes is used to profile energy usage in homes and detect users with high fuel consumption levels. This project concerns the use of advanced machine learning algorithms to model and predict household behaviour patterns from smart meter readings. The aim is to learn and understand the behavioural trends in homes (as demonstrated in chapter 5). The thesis shows the trends of how energy is used in residential homes. By obtaining these behavioural trends, it is possible for utility companies to come up with incentives that can be beneficial to home users on changes that can be adopted to reduce their carbon emissions. For example consumers would be more likely prompted to turn of unusable appliances that are consuming high energy around the home e.g., lighting in rooms which are un occupied. The data used for the research is constructed from a digital simulation model of a smart home environment comprised of 5 residential houses. The model can capture data from this simulated network of houses, hence providing an abundance set of information for utility companies and data scientist to promote reductions in energy usage. The simulation model produces volumes of outliers such as high periods (peak hours) of energy usage and low periods (Off peak hours) of anomalous energy consumption within the residential setting of five homes. To achieve this, performance characteristics on a dataset comprised of wealthy data readings from 5 homes is analysed using Area under ROC Curve (AUC), Precision, F1 score, Accuracy and Recall. The highest result is achieved using the Two-Class Decision Forest classifier, which achieved 87.6% AUC