Prediction of thermal energy inside smart homes using IoT and classifier ensemble techniques

Xu, Hong; He, Yuan; Sun, Xiang; He, Jinghan; Xu, Qiongmei

doi:10.1016/j.comcom.2019.12.020

Cited by 31 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Using cloud computing is sometimes impossible due to the distant placement of IoT devices. Fog computing has emerged to bring services closer to the data source [6]. Therefore, the computational power of fog computing can be used to process this high data demand more efficiently.…”

Section: Introductionmentioning

confidence: 99%

Supporting availability evaluation of a smart building monitoring system aided by fog computing

Silva

Fé

Brito

et al. 2022

Electronics Letters

View full text Add to dashboard Cite

Internet of Things (IoT) is increasingly present in people's lives, mainly to provide security and well-being. In homes and buildings, IoT sensors can monitor various factors such as temperature and the smoke presence. In case of danger, some action can be activated. However, a sensor network is a complex and fault-prone architecture. Evaluating an IoT architecture in this context is essential. Thus, the objective of this paper is twofold: (i) suggest a continuous time Markov chain (CTMC) model for a smart building monitoring system as a starting point; (ii) based on the results of a sensitivity analysis, an expanded CTMC with improved system availability and shorter outage periods is proposed. The proposed solution reduced the downtime by 98% compared with the baseline architecture. Systems administrators can use our models to evaluate their solutions.

show abstract

Section: Introductionmentioning

confidence: 99%

Supporting availability evaluation of a smart building monitoring system aided by fog computing

Silva

Fé

Brito

et al. 2022

Electronics Letters

View full text Add to dashboard Cite

show abstract

“…MPC has been implemented successfully in several directions of building control and operation strategies [9], [10]. Better thermal comfort and more energy savings compared to other control techniques can be achieved by combining MPC and ML such as neural networks [11], random forest [12], support vector machines [13]. However, designing accurate building energy/temperature models is the cornerstone to developing MPC for whole building operation and control due to the presence of external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Predictive and probabilistic modelling using machine learning for building indoor climate control

Abdufattokhov

Mahamatov

Ibragimova

et al. 2022

IJEECS

View full text Add to dashboard Cite

For the last few decades, thermal comfort has been considered an aspect of sustainable building evaluation methods and tools. However, estimating the indoor air temperature of buildings is a complicated task due to the nonlinear behaviour of heating, ventilation and air conditioning systems combined with complex dynamics characterized by the time-varying environment with disturbances. This issue can be alleviated by modelling the building dynamics using Gaussian processes since it also measures the uncertainty bounds. The main focus of this paper is designing a predictive and probabilistic room temperature model of buildings using Gaussian processes and incorporating it into model predictive control to minimize energy consumption and provide thermal comfort satisfaction. We exploited the Gaussian processes’ full probabilistic capabilities as the mean prediction for the room temperature model and used the model uncertainty in the objective function not to lose the desired performance and to design a robust control scheme. We illustrated the potentials of the proposed method in a numerical example with simulation results.

show abstract

“…Using cloud computing is sometimes impossible by the distant placement of IoT devices. Edge and fog computing emerged to place services closer to the data source [ 14 ]. The central research problem in this work is that such addition of processing layers in the IoT systems architecture requires performance evaluations from the earliest stages of development.…”

Section: Introductionmentioning

confidence: 99%

IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models

Santos

Soares

Nguyen

et al. 2021

Sensors

View full text Add to dashboard Cite

Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people’s daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks can severely cause property damage and perhaps loss of life. Therefore, it is important to quantify different metrics related to the operational performance of the systems that make up such computational architecture even in advance of the building construction. Previous studies used analytical models considering different aspects to assess the performance of building monitoring systems. However, some critical points are still missing in the literature, such as (i) analyzing the capacity of computational resources adequate to the data demand, (ii) representing the number of cores per machine, and (iii) the clustering of sensors by location. This work proposes a queuing network based message exchange architecture to evaluate the performance of an intelligent building infrastructure associated with multiple processing layers: edge and fog. We consider an architecture of a building that has several floors and several rooms in each of them, where all rooms are equipped with sensors and an edge device. A comprehensive sensitivity analysis of the model was performed using the Design of Experiments (DoE) method to identify bottlenecks in the proposal. A series of case studies were conducted based on the DoE results. The DoE results allowed us to conclude, for example, that the number of cores can have more impact on the response time than the number of nodes. Simulations of scenarios defined through DoE allow observing the behavior of the following metrics: average response time, resource utilization rate, flow rate, discard rate, and the number of messages in the system. Three scenarios were explored: (i) scenario A (varying the number of cores), (ii) scenario B (varying the number of fog nodes), and (iii) scenario C (varying the nodes and cores simultaneously). Depending on the number of resources (nodes or cores), the system can become so overloaded that no new requests are supported. The queuing network based message exchange architecture and the analyses carried out can help system designers optimize their computational architectures before building construction.

show abstract

Prediction of thermal energy inside smart homes using IoT and classifier ensemble techniques

Cited by 31 publications

References 25 publications

Supporting availability evaluation of a smart building monitoring system aided by fog computing

Supporting availability evaluation of a smart building monitoring system aided by fog computing

Predictive and probabilistic modelling using machine learning for building indoor climate control

IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models

Contact Info

Product

Resources

About