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

Abstract Development of models based on the Internet of Things (IoT) for household framework leads to the establishment of smart appliances more and more for improving the living style and support of residents. Due to this reason, useful energy becomes an increase in demand for the past few decades that especially usages in smart homes and buildings as people of developing rapidly and enhancing their lifestyle based on modern technology. Various parameters like building characteristics, surrounding weather variables, and energy usage pattern are the reliable sources of buildings energy performance. In this paper, a predictive model is proposed by integrating the mechanisms of IoT and classifier ensemble techniques for forecasting the indoor temperature of the smart building. The online learning-methodology is used for training the predictive model for a successive performance over an unfamiliar dataset. Moreover, the recorded real-sensor data is applied in the experimental process, which is based on the classifier ensemble techniques for validating the model. Furthermore, the building works with an energy-efficient way by using the latest IoT architecture, which is based on Edge Computing. The simulation results compared with other existing approaches and models in which the proposed energy prediction techniques prove to be better.

[1]  Arun Kumar Sangaiah,et al.  Evidence-based personal applications of medical computing models in risk factors of cardiovascular disease for the middle-aged and elderly , 2018, Personal and Ubiquitous Computing.

[2]  Jukka Vanhala,et al.  Proactive Fuzzy Control and Adaptation Methods for Smart Homes , 2008, IEEE Intelligent Systems.

[3]  W. Cai,et al.  Estimating urban residential building-related energy consumption and energy intensity in China based on improved building stock turnover model. , 2019, The Science of the total environment.

[4]  Claudia Nadler,et al.  Energy efficiency in the German residential housing market: Its influence on tenants and owners , 2019, Energy Policy.

[5]  Mohammad Yusri Hassan,et al.  A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .

[6]  Wei Tian,et al.  A review of sensitivity analysis methods in building energy analysis , 2013 .

[7]  Yunsi Fei,et al.  Smart Home in Smart Microgrid: A Cost-Effective Energy Ecosystem With Intelligent Hierarchical Agents , 2015, IEEE Transactions on Smart Grid.

[8]  Oliver Brdiczka,et al.  Learning Situation Models in a Smart Home , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Lynne E. Parker,et al.  Energy and Buildings , 2012 .

[10]  Rachid Bennacer,et al.  Residential building energy demand and thermal comfort: Thermal dynamics of electrical appliances and their impact , 2016 .

[11]  Nikolaos G. Paterakis,et al.  Deep learning versus traditional machine learning methods for aggregated energy demand prediction , 2017, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[12]  Federico Milano,et al.  Demand response algorithms for smart-grid ready residential buildings using machine learning models , 2019, Applied Energy.

[13]  Nadeem Javaid,et al.  Minimizing Daily Electricity Cost Using Bird Chase Scheme with Electricity Management Controller in a Smart Home , 2019, AINA.

[14]  Ahmad Faris Ismail,et al.  An AI based self-moderated smart-home , 2006 .

[15]  Athanasios Tsanas,et al.  Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .

[16]  G. Usha Devi,et al.  Detecting spams in social networks using ML algorithms - a review , 2018 .

[17]  G. Williams,et al.  A systems approach to achieving CarerNet-an integrated and intelligent telecare system , 1998, IEEE Transactions on Information Technology in Biomedicine.

[18]  Thillainathan Logenthiran,et al.  A Novel Smart Energy Theft System (SETS) for IoT-Based Smart Home , 2019, IEEE Internet of Things Journal.

[19]  G. Usha Devi,et al.  Feature extraction using LR-PCA hybridization on twitter data and classification accuracy using machine learning algorithms , 2018, Cluster Computing.

[20]  Wil L. Kling,et al.  Comparison of machine learning methods for estimating energy consumption in buildings , 2014, 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[21]  Gregor Verbic,et al.  Towards a smart home energy management system - A dynamic programming approach , 2011, 2011 IEEE PES Innovative Smart Grid Technologies.

[22]  Soumya Kanti Datta,et al.  An edge computing architecture integrating virtual IoT devices , 2017, 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE).

[23]  Ching-Hsien Hsu,et al.  Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals , 2018, Journal of Medical Systems.

[24]  Christian Bonnet,et al.  CCT: Connect and Control Things: A novel mobile application to manage M2M devices and endpoints , 2014, 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[25]  Wei Wang,et al.  Incorporating machine learning with building network analysis to predict multi-building energy use , 2019 .

[26]  Nora El-Gohary,et al.  Predicting Energy Consumption of Office Buildings: A Hybrid Machine Learning-Based Approach , 2019 .

[27]  Nelson Fumo,et al.  A review on the basics of building energy estimation , 2014 .

[28]  Ching-Hsien Hsu,et al.  Emerging trends, issues, and challenges in Internet of Medical Things and wireless networks , 2018, Personal and Ubiquitous Computing.

[29]  Christian Bonnet,et al.  Extending DataTweet IoT Architecture for Virtual IoT Devices , 2017, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[30]  Zhengwei Li,et al.  Methods for benchmarking building energy consumption against its past or intended performance: An overview , 2014 .

[31]  Usha Devi Gandhi,et al.  Classifying streaming of Twitter data based on sentiment analysis using hybridization , 2018, Neural Computing and Applications.

[32]  G. Usha Devi,et al.  Detecting Streaming of Twitter Spam Using Hybrid Method , 2018, Wireless Personal Communications.

[33]  Diane J. Cook,et al.  Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm , 2007, IEEE Intelligent Systems.

[34]  Rafik A. Goubran,et al.  Determination of Sit-to-Stand Transfer Duration Using Bed and Floor Pressure Sequences , 2009, IEEE Transactions on Biomedical Engineering.

[35]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[36]  Jacques Demerjian,et al.  An efficient data model for energy prediction using wireless sensors , 2019, Comput. Electr. Eng..

[37]  Hao Zhou,et al.  BIM-based energy consumption assessment of the on-site construction of building structural systems , 2018, Built Environment Project and Asset Management.