Smart Building: Use of the ANN Approach for Indoor Temperature Forecasting

Attoue, Nivine; Shahrour, Isam; Younès, Rafic

doi:10.20944/preprints201801.0051.v1

Cited by 9 publications

(1 citation statement)

References 23 publications

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“…The results obtained revealed the firefly algorithm's contribution in designing energyefficient buildings, particularly in the early stage. Nivine et al, [15] used an artificial neural network to predict hourly the house model's indoor temperature made of a hollow cement block. These reviews show that researching thermal analysis of insulation materials is very significant and can increase and maintain the sustainability of the product's economic value [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Performance Investigation of Polyurethane Foam as Thermal Insulation Material for Roofing Sheet Using Artificial Neural Network

Essien

Bolu

Okokpujie

et al. 2021

ARFMTS

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The prediction and application of Polyurethane Foam in developing roofing sheets cannot be over-emphasized when considering the environmental changes coursed by thermal radiation. This paper presents an artificial neural network application to model and predict the indoor temperature resistance of polyurethane (PU) roofing in residential buildings. The study employed a data logger to measure the indoor and outdoor temperatures for three simulation environments (i.e., morning, afternoon, and evening) for two hours each. Furthermore, the authors employed the Levenberg-Marquardt algorithm to transform and predict the indoor temperature obtained in the residential building's polyurethane roofing house. The result shows that the PU roofing system could absorb the heat and reduce the house model's temperature with 6.9% in the morning, afternoon 15.8%, and 6.8% in the evening when compared with the temperature outdoor environment. The ANN was also able to train, test, and validate the experimental temperature results with 92.86%, 93.92%, and 95%, respectively. The mean square error and a testing error occurs at 0.1707 and 0.1689. Therefore, this study concluded that ANN's application in predicting the thermal insulation material such as the PU roofing system is highly efficient and will increase the manufacturer's performance evaluation. It has also created significant awareness of the community in employing the PU roofing system for residential buildings, which will reduce the rate of energy consumption in buildings.

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Section: Introductionmentioning

confidence: 99%

Prediction and Performance Investigation of Polyurethane Foam as Thermal Insulation Material for Roofing Sheet Using Artificial Neural Network

Essien

Bolu

Okokpujie

et al. 2021

ARFMTS

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The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

et al. 2022

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Machine learning can be used to automate a wide range of tasks. Smart buildings, which use the Internet of Things (IoT) to connect building operations, enable activities, such as monitoring temperature, safety, and maintenance, for easier controlling via mobile devices and computers. Smart buildings are becoming core aspects in larger system integrations as the IoT is becoming increasingly widespread. The IoT plays an important role in smart buildings and provides facilities that improve human security by using effective technology-based life-saving strategies. This review highlights the role of IoT devices in smart buildings. The IoT devices platform and its components are highlighted in this review. Furthermore, this review provides security challenges regarding IoT and smart buildings. The main factors pertaining to smart buildings are described and the different methods of machine learning in combination with IoT technologies are also described to improve the effectiveness of smart buildings to make them energy efficient.

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Unsteady Heat Flux Measurement and Predictions Using Long Short-Term Memory Networks

Park

Kim

2023

Buildings

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Energy consumption modeling has evolved along with building technology. Modeling techniques can be largely classified into white box, gray box, and black box. In this study, the thermal behavior characteristics of building components were identified through time-series data analysis using LSTM neural networks. Sensors were installed inside and outside the test room to measure physical quantities. As a result of calculating the overall heat transfer coefficient according to the international standard ISO 9869-1, the U value of the multi-window with antireflection coating was 1.84 W/(m2∙K). To understand the thermal behavior of multiple windows, we constructed a neural network using an LSTM architecture and used the measured data-set to predict and evaluate the heat flux through deep learning. From the measurement data, a wavelet transform was used to extract features and to find appropriate control time-step intervals. Performance was evaluated according to multistep measurement intervals using the error metric method. The multistep time interval for control monitoring is preferably no more than 240 s. In addition, multivariate analysis with several input variables was performed. In particular, the thermal behavior of building components can be analyzed through heat flux and temperature measurements in the transient state of physical properties of pre-installed building components, which were difficult to access with conventional steady-state measurement methods.

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Smart Building: Use of the ANN Approach for Indoor Temperature Forecasting

Cited by 9 publications

References 23 publications

Prediction and Performance Investigation of Polyurethane Foam as Thermal Insulation Material for Roofing Sheet Using Artificial Neural Network

Prediction and Performance Investigation of Polyurethane Foam as Thermal Insulation Material for Roofing Sheet Using Artificial Neural Network

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

Unsteady Heat Flux Measurement and Predictions Using Long Short-Term Memory Networks

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