Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings

Ghasemkhani, Bita; Yilmaz, Reyat; Birant, Derya; Kut, Alp

doi:10.3390/sym14081553

Cited by 10 publications

(8 citation statements)

References 58 publications

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“…There are several types of ANN including feedforward, long shortterm memory, and recurrent. Some studies have applied SVM and ANN algorithms, or compared them with other ML algorithms, in classification problems related to the energy efficiency of buildings [27,40,41]. A hybrid method, called group support vector regression (GSVR), consisting of SVM algorithms (used for model verification) and a number of ML methods based on ANN and regression (used for early model building), has been proposed [40] to predict the thermal loads of residential buildings based on [8]'s dataset.…”

Section: Support Vector Machine and Artificial Neural Network Learnersmentioning

confidence: 99%

“…A further study investigated thermal load prediction in smart buildings using an ANN algorithm with feature selection [27]. The primary motivation behind this research was to assist stakeholders in resource allocation, in both the short-and long-term, for providing a healthy environment, optimising energy consumption, and leading to reduced energy-related costs.…”

Section: Support Vector Machine and Artificial Neural Network Learnersmentioning

confidence: 99%

“…One of the key benefits of smart home systems is their ability to provide real-time data and insights based on embedded techniques and using stored data about a home's heating and cooling usage [27]. This information can be used to identify areas where energy consumption can be reduced, such as by adjusting temperature settings during periods of low usage or using sensors to detect when rooms are unoccupied.…”

mentioning

confidence: 99%

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A Review of Cooling and Heating Loads Predictions of Residential Buildings Using Data-Driven Techniques

Abdel-Jaber,

Dirks

2024

Buildings

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Energy efficiency is currently a hot topic in engineering due to the monetary and environmental benefits it brings. One aspect of energy efficiency in particular, the prediction of thermal loads (specifically heating and cooling), plays a significant role in reducing the costs associated with energy use and in minimising the risks associated with climate change. Recently, data-driven approaches, such as artificial intelligence (AI) and machine learning (ML) techniques, have provided cost-effective and high-quality solutions for solving energy efficiency problems. This research investigates various ML methods for predicting energy efficiency in buildings, with a particular emphasis on heating and cooling loads. The review includes many ML techniques, including ensemble learning, support vector machines (SVM), artificial neural networks (ANN), statistical models, and probabilistic models. Existing studies are analysed and compared in terms of new criteria, including the datasets used, the associated platforms, and, more importantly, the interpretability of the models generated. The results show that, despite the problem under investigation being studied using a range of ML techniques, few have focused on developing interpretable classifiers that can be exploited by stakeholders to support the design of energy-efficient residential buildings for climate impact minimisation. Further research in this area is required.

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Section: Support Vector Machine and Artificial Neural Network Learnersmentioning

confidence: 99%

Section: Support Vector Machine and Artificial Neural Network Learnersmentioning

confidence: 99%

mentioning

confidence: 99%

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A Review of Cooling and Heating Loads Predictions of Residential Buildings Using Data-Driven Techniques

Abdel-Jaber,

Dirks

2024

Buildings

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“…For example, the approach presented in [30] used an ensemble machine learning model based on three Random Forest models that achieved 0.999 R 2 for the heating load prediction and 0.997 R 2 for the cooling load prediction using a 10-fold cross-validation approach. On the other hand, the authors of [31] used an approach based on the Tri-Layered Neural Network and Maximum Relevance Minimum Redundancy that led to 0.289 mean absolute error for heating load and 0.535 mean absolute error for cooling load, respectively.…”

Section: Research Backgroundmentioning

confidence: 99%

Multi-Objective Plum Tree Algorithm and Machine Learning for Heating and Cooling Load Prediction

Slowik,

Moldovan

2024

Energies

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The prediction of heating and cooling loads using machine learning algorithms has been considered frequently in the research literature. However, many of the studies considered the default values of the hyperparameters. This manuscript addresses both the selection of the best regressor and the tuning of the hyperparameter values using a novel nature-inspired algorithm, namely, the Multi-Objective Plum Tree Algorithm. The two objectives that were optimized were the averages of the heating and cooling predictions. The three algorithms that were compared were the Extra Trees Regressor, the Gradient Boosting Regressor, and the Random Forest Regressor of the sklearn machine learning Python library. We considered five hyperparameters which were configurable for each of the three regressors. The solutions were ranked using the MOORA method. The Multi-Objective Plum Tree Algorithm returned a root mean square error value for heating equal to 0.035719 and a root mean square error for cooling equal to 0.076197. The results are comparable to the ones obtained using standard multi-objective algorithms such as the Multi-Objective Grey Wolf Optimizer, Multi-Objective Particle Swarm Optimization, and NSGA-II. The results are also performant concerning the previous studies, which considered the same experimental dataset.

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“…Defect identification of frictionstir welds on metal surfaces was performed using machine vision and classifiers in which Discriminant Analysis and Nearest Neighbor approaches were adopted for defect classification by Praveen Kumar et al in 2021 [5]. Ghasemkhani et al adopted an ensemble approach using a machine-learning based entitled logistic model tree (LMT) forest with edited nearest neighbor (ENN) to achieve an accuracy of 86.655% in classifying surface defects on stainless steel plates [6]. From these bodies of work, it could be inferred that machine learning algorithms are well suited for handling numerical data with minimized computational efforts and high accuracy of results for applications that aim at solving diverse challenges [7].…”

Section: Introductionmentioning

confidence: 99%

Hot rolled steel surface defect detection and classification using an automatic ensemble approach

Vasan,

Sridharan,

Sugumaran

et al. 2024

Eng. Res. Express

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This study introduces an ensemble-based Deep Neural Network (DNN) model for detecting defects on steel surfaces. The method suggested in this study classifies steel surface conditions into six possible fault categories, namely, crazing, inclusion, rolled in, pitted surface, scratches, and patches. The images undergo preprocessing and extraction of features in spatial and frequency domains using image segmentation techniques such as grey level difference method (GLDM), fast Fourier Transform (FFT), grey level co-occurrence matrix (GLCM), texture analysis and discrete wavelet transform (DWT). The ensembling of image features into a fused feature pool is carried out after the preprocessing of input images that are provided as input to a light-weight neural network model for training and testing. The performance of the model is comprehensively evaluated via an ablation study both before and after ensembling. In addition, the model capability is effectively analyzed using receiver operating characteristics (ROC) curve, confusion matrix from which classification accuracy of the model could be obtained and other parameters including precision and f1-score. It was observed that the proposed deep learning network presents phenomenally high accuracy of 99.72% for detection and classification of steel surface faults. This result was found to be superior when compared with the performance of the same neural network over each feature type individually. This study also compares the classification results of the model built based on the ensembled feature set with the results of various other classification approaches available in literature. The ensemble-based model could potentially be integrated into existing inspection systems for real-time, efficient and robust condition monitoring of steel surfaces.

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Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings

Cited by 10 publications

References 58 publications

A Review of Cooling and Heating Loads Predictions of Residential Buildings Using Data-Driven Techniques

A Review of Cooling and Heating Loads Predictions of Residential Buildings Using Data-Driven Techniques

Multi-Objective Plum Tree Algorithm and Machine Learning for Heating and Cooling Load Prediction

Hot rolled steel surface defect detection and classification using an automatic ensemble approach

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