IoT Data based Predictive Modeling for Energy Usage of Appliances in Smart Home

  • Authors

    • Mrs. Archana Shirke
    • Dr M.M. Chandane
    https://doi.org/10.14419/ijet.v7i3.34.19701
  • Predictive Modeling, IoT, Regression, GBM, XGBM

  • Internet of things (IoT) has emerged as the new trends in the wireless technology in last few years. This new area has greatly influenced the working of humans. IoT has applications in every domains of human being. The growth rate of IoT devices is exponential due to its wide applicability. Therefore, the data generated by these devices is huge and contains high variability. Such a huge amount of data needs to be modeled precisely. The effectiveness of the IoT applications lies in the preciseness of the data represented by the models. Predictive analysis helps business analysts to build models to predict trends, make tradeoff decisions, and model the real world for decision ­making support system. This paper presents the study on various models used for IoT data analytics. Various predictive models such as Multiple Linear Regression (LR), Support Vector Machine for regression (SVR), Random Forest (RF), Gradient Boosting Machine (GBM) and extreme Gradient Boosting Machine (XGBM) are applied on the sensor data collected from Smart Home. The comparative results produced by these models have been analyzed with reference to energy consumption and prediction. The implementation of the models is carried out on R language. The results show that XGBM model perform better based on RMSE, R-squared and MAE for given data set. It has less RMSE and high R-squared which indicates it has captured high variability in the data.

     

     

  • References

    1. [1] Mohsen Marjani, Fariza Nasaruddin2, Abdullah Gani1, (Senior Member, IEEE), Ahmad Karim, Ibrahim AbakerTargioHashem, Aisha Siddiqa, AndIbrarYaqoob “Big IoT Data Analytics: Architecture, Opportunities, and OpenResearch Challengesâ€, IEEE Access, 29 March 2017, page 5247 - 5261.

      [2] Chun-Wei Tsai, Chin-Feng Lai, Ming-Chao Chiang, and Laurence T. Yang “Data Mining for Internet of Things: A Survey†IEEE Communications Surveys & Tutorials, Vol. 16, No. 1, First Quarter 2014, page 77-97.

      [3] HongmingCAI,BoyiXu, Lihong Jiang, and Athanasios V. Vasilakos, “IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges†IEEE Internet of Things Journal, Vol. 4, No. 1, February 2017 Page 75-87.

      [4] Shanzhi Chen, HuiXu, Dake Liu, Bo Hu, and Hucheng Wang “A Vision of IoT: Applications, Challenges, and Opportunities with China Perspectiveâ€, IEEE Internet of Things Journal, Vol. 1, No. 4, August 2014 Page 349-359.

      [5] Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari, and MoussaAyyash, “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications†IEEE Communication Surveys & Tutorials, Vol. 17, No.4, Fourth Quarter 2015,Page 2347-2376.

      [6] Huadong Ma, Liang Liu, Anfu Zhou, and Dong Zhao “On Networking of Internet of Things: Explorations and Challenges†IEEE Internet of Things Journal, Vol. 3, No. 4, August 2016, Page 441-452.

      [7] ShikharVerma, Yuichi Kawamoto, Zubair Md. Fadlullah, Hiroki Nishiyama, and Nei Kato,“A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data andOpen Research Issuesâ€, IEEE Communications Surveys & Tutorials, Vol: 19, Issue: 3, April 2017, Page 1457-1477.

      [8] A. Akbar, A. khan, F. Carrez, and K. Moessner, “Predictive analytics for complex IoT data streams,†IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1–1, 2017.

      [9] A. Akbar, F. Carrez, K. Moessner, and A. Zoha, “Predicting complex events for pro-active IoT applications,†in Internet of Things (WF-IoT), 2015 IEEE 2nd World Forum on, Dec 2015, pp. 327–332.

      [10] Paula Ta-Shma, A. Akbar, Guy Gerson-Golan, Guy Hadash, F. Carrez and K. Moessner, “An Ingestion and Analytics Architecture for IoT applied to smart City Use Cases,†IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1–1, 2017.

      [11] Luis Candanedo, V. Feldheim, D. Deramaix, “Data driven prediction models of energy use of appliances in a low energy house†Energy and Buildings, Volume 140, 1 april 2017, pp. 327-341.

      [12] Kecskemeti, G and Casale, G and Jha, DN and Lyon, J and Ranjan, R (2017), “Modelling and Simulation Challenges in Internet of Thingsâ€, IEEE Cloud Computing, 4 (1). pp. 62-69. ISSN 2325-6095.

      [13] Friedman J, Hastie T, Tibshirani R, et al. (2000). “Additive logistic regression: a statistical view of boosting,†the annals of statistics, 28(2), 337–407.

      [14] Friedman JH (2001). “Greedy function approximation: a gradient boosting machine.†Annals of Statistics, pp. 1189–1232.

      [15] Luis Candanedo,Appliances energy prediction Data Set, Online: http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction,April 2017.

      [16] Chris Raphael, “Why Edge Computing Is Crucial for the IoTâ€,Online:https://www.rtinsights.com/why-edge-computing-and-analytics-is-crucial-for-the-iot/, 12 November 2015.

      [17] Jennifer Sewell, 5 Myths of IoT analytics, Online:http://mnubo.com/5-myths-iot-analytics, 26 September 2016.

      [18] The 4 stages of an IoT architecture, Online: https://techbeacon.com/4-stages-iot-architecture, Jan 2017.

      [19] Jonathan Holdowsky, Monika Mahto, Michael E. Raynor, Mark Cotteleer, “Inside the Internet of Thingsâ€,Online:https://dupress.deloitte.com/dup-us-en/focus/internet-of-things/iot-primer-iot-technologies-applications.html, 21 August, 2015.

      [20] B. Dong, C. Cao, S.E. Lee, Applying support vector machines to predict buildingenergy consumption in tropical region, Energy Build. 37 (5) (2005) 545–553.

      [21] G. James, D. Witten, T. Hastie, R. Tibshirani, an Introduction to Statistical Learning, Springer, 2013.

      [22] Gradient Boosting Machine, M. Kuhn, caret: Classification and Regression Training, 2015.

      [23] Tianqi Chen, Tong He, “boost: eXtreme Gradient Boostingâ€, Package Version: 0.6-4, 4 January, 2017.

      [24] Edwin de Jonge, Mark van der Loo, “An introduction to data cleaning with Râ€, statistics Netherland, Discussion paper, 2013.

      [25] Nerurkar, P., Shirke A., Chandane M., Bhirud S., “Empirical Analysis of Data Clustering Algorithms†in Procedia Computer Science 2018 Vol. 125 Pg. 770-779

      [26] Nerurkar, P., Shirke A., Chandane M., Bhirud S., “ANovel Heuristic for Evolutionary Clustering†in Procedia Computer Science 2018 Vol. 125 Pg. 780-789

      [27] G. Indrajith and K.Vijayakumar, “Automatic Mathematical and Chronological Prediction in Smartphone Keyboard†International Journal of Engineering and Computer Science ISSN: 2319-7242Volume 5 Issue 5 May 2016, Page No. 16714-16718.

      [28] K. Vijayakumar and C. Arun, “Continuous Security Assessment of Applications in Cloud Environmentâ€, International Journal of Control Theory and Applications, ISSN: 0974-5645 volume No. 9(36), Sep 2016, Page No. 533-541.

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  • How to Cite

    Archana Shirke, M., & M.M. Chandane, D. (2018). IoT Data based Predictive Modeling for Energy Usage of Appliances in Smart Home. International Journal of Engineering & Technology, 7(3.34), 931-934. https://doi.org/10.14419/ijet.v7i3.34.19701