IoT and Machine Learning Based Prediction of Smart Building Indoor Temperature

Paul, Debayan; Chakraborty, Tanmay; Datta, Soumya Kanti; Paul, Debolina

doi:10.1109/iccoins.2018.8510597

Cited by 24 publications

(6 citation statements)

References 14 publications

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“…In [49], the authors develop a solution that implements IoT devices and machine learning algorithms to create a predictive model for forecast indoor temperature in smart buildings. Since temperature control is key in the conservation of energy in buildings, the work proposes Edge Computing as the premises on which the system is developed.…”

Section: Edge Computing Iot and Smart Energymentioning

confidence: 99%

Edge Computing, IoT and Social Computing in Smart Energy Scenarios

Sittón-Candanedo

Alonso

García

et al. 2019

Sensors

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The Internet of Things (IoT) has become one of the most widely research paradigms, having received much attention from the research community in the last few years. IoT is the paradigm that creates an internet-connected world, where all the everyday objects capture data from our environment and adapt it to our needs. However, the implementation of IoT is a challenging task and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). EC allows for more secure and efficient data processing in real time, achieving better performance and results. Energy efficiency is one of the most interesting IoT scenarios. In this scenario sensors, actuators and smart devices interact to generate a large volume of data associated with energy consumption. This work proposes the use of an Edge-IoT platform and a Social Computing framework to build a system aimed to smart energy efficiency in a public building scenario. The system has been evaluated in a public building and the results make evident the notable benefits that come from applying Edge Computing to both energy efficiency scenarios and the framework itself. Those benefits included reduced data transfer from the IoT-Edge to the Cloud and reduced Cloud, computing and network resource costs.

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Section: Edge Computing Iot and Smart Energymentioning

confidence: 99%

Edge Computing, IoT and Social Computing in Smart Energy Scenarios

Sittón-Candanedo

Alonso

García

et al. 2019

Sensors

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“…Selanjutnya penelitian yang dilakukan oleh Paul ,et.al [22] menggabungkan antara IoT dengan machine learning untuk smart building. Penelitian ini didasari oleh kepedulian terhadap penggunaan energi di sebuah bangunan.…”

Section: Gambar 8 Strukturunclassified

“…Bagaimana agar pengguna yakin bahwa data yang digunakan adalah data yang valid dan aktual. Protokol dari IoT yang beragam juga merupakan isu dan tantangan tersendiri dalam sebuah penelitian yang melibatkan IoT dan machine learning [5], [22], [26], [27] [28].…”

Section: Isu Dan Tantangan Machine Learning Dan Iotunclassified

A Survey on Machine learning and IoT

Hardini

2019

ITEJ

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Internet of Things is an intelligence devices that can connect to any other devices. Based on that ability to connect and its intelligence, it makes people to create an intelligence device and capable to learn like human brain. To accomplish that goal, machine learning is the solution. Combination between machine learning and IoT generate a revolution in human life and application industry. The combination also generate new trend in market. Therefore, in this paper will be discuss about machine learning including machine learning technique and algorithm and also discuss about IoT including architecture and elements. Then in this paper will discuss about some researches that have already done that combine IoT with machine learning approach, including issue and challenge in those researches.

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“…Data sets are always produced at high speed and are available in a heterogeneous form. The data produced in a smart home of a smart city can be from various services, such as electricity, gas, water, protection, and security (Sharma, V., & Tiwari, R., 2016). In addition to maintaining heterogeneous data, the main challenge is processing such a huge volume of heterogeneous data in a limited time, which is very difficult, and data heterogeneity is still an unsolved challenge (Gohar, M., Ahmed, S. H., et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Provide a Method Improving Temperature Control in Smart Buildings Based in Slicing Technique and Clustering Iot Network Based on Composition

Rabieifard¹,

Naderlou²,

Qasabeh³

2022

AzJHPC

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Today, energy consumption is important in calculating the heating and cooling loads of residential, industrial, and other units. In order to calculate, design, and select the heating-cooling system, a suitable method of consumption and cost analysis is needed to prepare the required data for air conditioning motors and design an intelligent system. In this research, a method for balancing the temperature of an intelligent building in the context of the Internet of Things is presented based on a combination of network cutting and clustering techniques. In order to achieve the optimization of the algorithm in this method, it is necessary to convert heterogeneous data into homogeneous data, which was done by introducing a complex network and appropriate clustering techniques. In this method, information was collected by the IoT, and a graph matrix of these data was generated, then recorded by an artificial intelligence method and a combination of three methods of hierarchical clustering, Gaussian mixture, and K-means for comparison with the preliminary results. Finally, due to the reliability of the K-means method and the use of majority voting for weights, the K-means method reached 0.4 and was selected as the clustering method. The main part of the proposed method is based on different classifications in Appropriate criteria that were evaluated. Acceptable results were recorded so that with the minimum value of 88% and the highest value of about 100, the results of the proposed method can be confirmed. All hypotheses of the method can be declared possible and acceptable.

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IoT and Machine Learning Based Prediction of Smart Building Indoor Temperature

Cited by 24 publications

References 14 publications

Edge Computing, IoT and Social Computing in Smart Energy Scenarios

Edge Computing, IoT and Social Computing in Smart Energy Scenarios

A Survey on Machine learning and IoT

Provide a Method Improving Temperature Control in Smart Buildings Based in Slicing Technique and Clustering Iot Network Based on Composition

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