Special Issue "Artificial Intelligence in Smart Buildings"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2020.

Special Issue Editors

Prof. Anastasios Dounis
E-Mail Website
Guest Editor
Faculty of Engineering, University of West Attica, 12243 Athens, Greece
Interests: Computational Intelligence and Evolutionary computation, Fuzzy systems, Fuzzy control and modelling, Fuzzy cognitive maps and Petri nets in decision support systems, Intelligent control, Time series prediction, Automation systems in renewable energy resources, Intelligent energy management systems and smart buildings, Design and management of autonomous smart micro grids, Power electronics in photovoltaic systems, Control electrochromic devices, Modelling and control of reverse osmosis desalination
Special Issues and Collections in MDPI journals
Dr. Panagiotis Kofinas
E-Mail
Guest Editor
Department of Industrial Design and Production Engineering, University of West Attica, University Campus 2, P. Ralli & Thivon 250, 12244, Egaleo-Athens, Greece
Interests: artificial intelligence; computational intelligence; optimization; intelligent control; automation; energy management; sustainable energy sources; information technology systems; smart grids

Special Issue Information

Dear Colleagues,

Nowadays, global warming is encouraging many researchers to focus on smart energy solutions to deal with the problem of climate change. All over the world there are myriads of buildings that consume energy for space heating and cooling, indoor lighting, etc., which burden global warming even more. The challenge of reducing the energy requirements of a building in conjunction with maintaining or even improving the quality of the indoor environment leads to the combination of different technologies for developing “smart buildings”. The term “smart buildings” refers to buildings that use various technologies to communicate their systems in order to optimize their performance. A smart building uses sensors for online monitoring, gathers data to analyze its own energy use, and applies automated techniques to adjust the conditions throughout the entire building. The quality of life in buildings is determined by three basic factors: Thermal comfort, visual comfort, and indoor air quality. The optimization of performance mainly lies in the optimal living conditions for the occupants, the reduction of building expenses, and the reduction of energy consumption which burdens the environment.

With recent advances in artificial intelligence, energy and the built environment are becoming more and more closely linked. The term “artificial intelligence” refers to intelligence demonstrated by machines that perform cognitive functions associated with the human minds. The concept is related to machines that process large amounts of information either using historical data or data acquired via interaction with the environment, and which continually learn through the consequences of action–result combinations. Artificial techniques can be used in smart buildings in order to improve their performance in a fully automated concept. Artificial intelligence systems can process the information acquired by sensors, learn from patterns by historical data or learn online by interacting with the built environment. The acquired knowledge can be used to make either predictions or making decisions for solving problems concerning efficient energy management and the optimization of the inhabitants’ comfort.

This Special Issue encourages authors from both industry and academia to develop advanced artificial intelligence approaches in smart buildings and submit original research papers contributing to the aforementioned challenges.

Prof. Dr. Anastasios Dounis
Dr. Panagiotis Kofinas
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart buildings
  • Buildings and microgrids
  • Artificial general intelligence
  • Machine learning
  • Demand forecasting
  • Neural networks
  • Fuzzy logic systems
  • Intelligent energy management
  • Pattern recognition
  • Distributed AI
  • Reinforcement learning
  • Evolutionary computing
  • Muliti-agent systems in building control
  • Management of indoor microclimates
  • Indoor air quality management
  • Internet of things
  • Game theory

Published Papers (10 papers)

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Research

Open AccessArticle
Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils
Appl. Sci. 2019, 9(23), 5264; https://doi.org/10.3390/app9235264 - 03 Dec 2019
Abstract
This study outlines the applicability of four metaheuristic algorithms, namely, whale optimization algorithm (WOA), league champion optimization (LCA), moth–flame optimization (MFO), and ant colony optimization (ACO), for performance improvement of an artificial neural network (ANN) in analyzing the bearing capacity of footings settled [...] Read more.
This study outlines the applicability of four metaheuristic algorithms, namely, whale optimization algorithm (WOA), league champion optimization (LCA), moth–flame optimization (MFO), and ant colony optimization (ACO), for performance improvement of an artificial neural network (ANN) in analyzing the bearing capacity of footings settled on two-layered soils. To this end, the models estimate the stability/failure of the system by taking into consideration soil key factors. The complexity of each network is optimized through a sensitivity analysis process. The performance of the ensembles is compared with a typical ANN to evaluate the efficiency of the applied optimizers. It was shown that the incorporation of the WOA, LCA, MFO, and ACO algorithms resulted in 14.49%, 13.41%, 18.30%, and 35.75% reductions in the prediction error of the ANN, respectively. Moreover, a ranking system is developed to compare the efficiency of the used models. The results revealed that the ACO–ANN performs most accurately, followed by the MFO–ANN, WOA–ANN, and LCA–ANN. Lastly, the outcomes demonstrated that the ACO–ANN can be a promising alternative to traditional methods used for analyzing the bearing capacity of two-layered soils. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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Open AccessArticle
Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil
Appl. Sci. 2019, 9(22), 4738; https://doi.org/10.3390/app9224738 - 06 Nov 2019
Abstract
Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the [...] Read more.
Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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Open AccessArticle
A Case-Based Reasoning Model for Retrieving Window Replacement Costs through Industry Foundation Class
Appl. Sci. 2019, 9(22), 4728; https://doi.org/10.3390/app9224728 - 06 Nov 2019
Abstract
Building information modeling (BIM) provides facility managers with a large database consisting of 3D geometric data as well as management data. In particular, Industry Foundation Class (IFC) has been applied in many studies as it provides extensive and diverse information regarding building components. [...] Read more.
Building information modeling (BIM) provides facility managers with a large database consisting of 3D geometric data as well as management data. In particular, Industry Foundation Class (IFC) has been applied in many studies as it provides extensive and diverse information regarding building components. With the use of BIM combined with case-based reasoning (CBR), in this study, a model was developed to estimate replacement costs by retrieving cost information from IFC. This study focused on the replacement of windows for office buildings, and the costs associated with that replacement. Two main advantages were identified in the proposed approach. First, the replacement information required for the comparison of different cases is automatically obtained from a BIM file and parsed for predicting a cost estimate using IFC. Next, the accuracy is increased by matching various cost-related data such as contractors and manufacturers in the estimation of replacement costs with the help of CBR. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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Open AccessArticle
Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength
Appl. Sci. 2019, 9(21), 4643; https://doi.org/10.3390/app9214643 - 01 Nov 2019
Abstract
This paper focuses on the prediction of soil shear strength (SSS), which is one of the most fundamental parameters in geotechnical engineering. Consisting of 12 influential factors, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture [...] Read more.
This paper focuses on the prediction of soil shear strength (SSS), which is one of the most fundamental parameters in geotechnical engineering. Consisting of 12 influential factors, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic Index, and liquidity index as input variables, as well as the shear strength as the desired output, the dataset is provided through a field survey in Vietnam. Thereafter, as for used intelligent techniques, the main focus of the current study is on evaluating the efficiency of three novel optimization techniques for optimizing an artificial neural network (ANN) in predicting the SSS. To this end, the dragonfly algorithm (DA), whale optimization algorithm (WOA), and invasive weed optimization (IWO) are synthesized with ANN to prevail its computational drawbacks. The complexity of the models is optimized by sensitivity analysis. The results confirmed the effectiveness of all three applied algorithms, as the learning error was reduced by nearly 17%, 27%, and 32%, respectively by functioning the DA, WOA, and IWO. As for the testing phase, the IWO and DA achieved a close prediction accuracy. Overall, due to the superiority of the IWO-ANN ensemble, this model could be a promising alternative to traditional methods of shear strength determination. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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Open AccessArticle
Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure
Appl. Sci. 2019, 9(21), 4638; https://doi.org/10.3390/app9214638 - 31 Oct 2019
Abstract
In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal [...] Read more.
In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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Open AccessArticle
Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing
Appl. Sci. 2019, 9(21), 4594; https://doi.org/10.3390/app9214594 - 29 Oct 2019
Abstract
In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. [...] Read more.
In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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Open AccessArticle
Application of Three Metaheuristic Techniques in Simulation of Concrete Slump
Appl. Sci. 2019, 9(20), 4340; https://doi.org/10.3390/app9204340 - 15 Oct 2019
Cited by 1
Abstract
Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) [...] Read more.
Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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Open AccessFeature PaperArticle
Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
Appl. Sci. 2019, 9(20), 4338; https://doi.org/10.3390/app9204338 - 15 Oct 2019
Abstract
The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random [...] Read more.
The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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Open AccessArticle
Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models
Appl. Sci. 2019, 9(17), 3543; https://doi.org/10.3390/app9173543 - 29 Aug 2019
Cited by 3
Abstract
Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the [...] Read more.
Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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Open AccessArticle
Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost
Appl. Sci. 2019, 9(13), 2714; https://doi.org/10.3390/app9132714 - 04 Jul 2019
Cited by 8
Abstract
In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization [...] Read more.
In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R2, VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities. Full article
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
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