Author
Dac-Khuong Bui
Other affiliations: National Taiwan University of Science and Technology
Bio: Dac-Khuong Bui is an academic researcher from University of Melbourne. The author has contributed to research in topics: Firefly algorithm & Energy consumption. The author has an hindex of 6, co-authored 9 publications receiving 465 citations. Previous affiliations of Dac-Khuong Bui include National Taiwan University of Science and Technology.
Papers
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TL;DR: In this paper, the energy performance of buildings was estimated using various data mining techniques, including support vector regression (SVR), artificial neural network (ANN), classification and regression tree, chi-squared automatic interaction detector, general linear regression, and ensemble inference model.
308 citations
TL;DR: The result indicates that the MFA-ANN hybrid system can obtain a better prediction of the high-performance concrete properties and can provide an efficient and accurate tool to predict and design HPC.
187 citations
TL;DR: It is shown that EFA-ANN provides a faster and more accurate prediction of HL and CL, and can assist civil engineers and construction managers in the early designs of energy-efficient buildings.
106 citations
TL;DR: The improved performance of the EFA in comparison with the FA, EM as well as other optimization algorithms in the literature is demonstrated by six popular truss optimization problems with discrete variables.
59 citations
TL;DR: The modified firefly algorithm, an in-house optimization tool, is employed to design the adaptive facade system, which can adapt its thermal and visible transmittance for dynamically varying climatic conditions and can reduce the energy consumption by 14.9–29.3% compared to the static facades.
48 citations
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TL;DR: A review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation is provided in this paper.
Abstract: Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.
1,015 citations
TL;DR: A review of studies developing data-driven models for building scale applications with a focus on the input data characteristics and data pre-processing methods, the building typologies considered, the targeted energy end-uses and forecasting horizons, and accuracy assessment.
422 citations
TL;DR: An in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple singleAI-based prediction models improves the prediction accuracy manifold.
Abstract: Building energy use prediction plays an important role in building energy management and conservation as it can help us to evaluate building energy efficiency, conduct building commissioning, and detect and diagnose building system faults. Building energy prediction can be broadly classified into engineering, Artificial Intelligence (AI) based, and hybrid approaches. While engineering and hybrid approaches use thermodynamic equations to estimate energy use, the AI-based approach uses historical data to predict future energy use under constraints. Owing to the ease of use and adaptability to seek optimal solutions in a rapid manner, the AI-based approach has gained popularity in recent years. For this reason and to discuss recent developments in the AI-based approaches for building energy use prediction, this paper conducts an in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple single AI-based prediction models improves the prediction accuracy manifold. This paper elaborates the principles, applications, advantages and limitations of these AI-based prediction methods and concludes with a discussion on the future directions of the research on AI-based methods for building energy use prediction.
377 citations
TL;DR: The hybridization of the models with GWO improves the training and generalization capability of both ANN and ANFIS models and it is deduced that ANN models trained with Levenberg-Marquardt algorithm outperformed other ANN-based models as well as all ANfIS- based models.
246 citations
TL;DR: Examination of several Machine Learning models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms are examined.
241 citations