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GREENHOME: A Household Energy Consumption and CO2 Footprint Metering Environment

Published:22 January 2022Publication History
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Abstract

This article presents the GREENHOME environment, a toolkit providing several data analytical tools for metering household energy consumption and CO2 footprint under different perspectives. GREENHOME enables a multi-perspective analysis of household energy consumption and CO2 footprint using and combining several variables through various statistics and data mining algorithms. To test GREENHOME, the article reports on experiments conducted for modelling and forecasting energy consumption and CO2 footprint in the context of the Triple-A European project.

REFERENCES

  1. [1] Amayri Manar, Ploix Stephane, and Bandyopadhyay Sanghamitra. 2015. Estimating occupancy in an office setting. In Sustainable Human-Building Ecosystems. American Society of Civil Engineers, Reston, VA, 7280. https://doi.org/10.1061/9780784479681.008Google ScholarGoogle Scholar
  2. [2] Arif M., Brouard T., and Vincent N.. 2006. A fusion methodology based on dempster-shafer evidence theory for two biometric applications. In Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06). IEEE, 590593. https://doi.org/10.1109/ICPR.2006.68 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] Baragona R. and Battaglia F.. 2004. Projection methods for outlier detection in multivariate time series. Sis-Statistica.It (2004), 107118. Retrieved from http://old.sis-statistica.org/files/pdf/atti/sessioneplenarie2006_107-118.pdf.Google ScholarGoogle Scholar
  4. [4] Barghi Armin, Kosari Amir Reza, Shokri Maede, and Sheikhaei Samad. 2014. Intelligent lighting control with LEDS for smart home. In Proceedings of the Smart Grid Conference (SGC’14). 15. https://doi.org/10.1109/SGC.2014.7090861Google ScholarGoogle Scholar
  5. [5] Bas María del Carmen, Ortiz Josefina, Ballesteros Luisa, and Martorell Sebastián. 2017. Evaluation of a multiple linear regression model and SARIMA model in forecasting 7 Be air concentrations. Chemosphere 177 (2017), 326333. https://doi.org/10.1016/j.chemosphere.2017.03.029Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Breitung Jorg. 1994. Some simple tests of the moving-average unit root hypothesis. J. Time Series Anal. 15, 4 (July 1994), 351370. https://doi.org/10.1111/j.1467-9892.1994.tb00199.xGoogle ScholarGoogle ScholarCross RefCross Ref
  7. [7] Building EUObservatory Stock. 2014. Energy performance of buildings directive. Struct. Survey 23, 1 (2014), 17. https://doi.org/10.1108/ss.2005.11023aab.001Google ScholarGoogle Scholar
  8. [8] Chapman Lee, Bell Cassandra, and Bell Simon. 2017. Can the crowdsourcing data paradigm take atmospheric science to a new level? A case study of the urban heat island of London quantified using Netatmo weather stations. Int. J. Climatol. 37, 9 (2017), 35973605. https://doi.org/10.1002/joc.4940Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Chitsaz Hamed, Shaker Hamid, Zareipour Hamidreza, Wood David, and Amjady Nima. 2015. Short-term electricity load forecasting of buildings in microgrids. Energy Build. 99 (2015), 5060. https://doi.org/10.1016/j.enbuild.2015.04.011Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Davison A. C. and Hinkley D. V.. 1997. Bootstrap Methods and their Application. Cambridge University Press. https://doi.org/10.1017/CBO9780511802843Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Deligiannis Paraskevas, Koutroubinas Stelios, and Koronias George. 2019. Predicting energy consumption through machine learning using a smart-metering architecture. IEEE Potentials 38, 2 (Mar. 2019), 2934. https://doi.org/10.1109/MPOT.2018.2852564Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Dhar Narendra Kumar, Verma Nishchal Kumar, Behera Laxmidhar, and Jamshidi Mo M.. 2018. On an integrated approach to networked climate control of a smart home. IEEE Syst. J. 12, 2 (2018), 13171328. https://doi.org/10.1109/JSYST.2016.2619366Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Diversi Roberto, Guidorzi Roberto, and Soverini Umberto. 2010. Identification of ARX and ARARX Models in the presence of input and output noises. Eur. J. Control 16, 3 (2010), 242255. https://doi.org/10.3166/ejc.16.242-255Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Elz Dieter. 2007. Bioenergy systems. Quart. J. Int. Agric. 46, 4 (2007), 325332.Google ScholarGoogle Scholar
  15. [15] Foucquier Aurélie, Robert Sylvain, Suard Frédéric, Stéphan Louis, and Jay Arnaud. 2013. State of the art in building modelling and energy performances prediction: A review. Renew. Sustain. Energy Rev. 23 (2013), 272288. https://doi.org/10.1016/j.rser.2013.03.004Google ScholarGoogle Scholar
  16. [16] Fumo Nelson and Biswas M. A. Rafe. 2015. Regression analysis for prediction of residential energy consumption. Renew. Sustain. Energy Rev. 47 (2015), 332343. https://doi.org/10.1016/j.rser.2015.03.035Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Gumbel E. J.. 1935. Les valeurs extrêmes des distributions statistiques. Annales de l’institut Henri Poincaré 2, 5 (1935), 115158. Retrieved from http://www.numdam.org/item/AIHP.Google ScholarGoogle Scholar
  18. [18] Heller Katherine A. and Ghahramani Zoubin. 2005. Bayesian hierarchical clustering. In Proceedings of the 22nd International Conference on Machine Learning (ICML’05). ACM Press, New York, New York, 297304. https://doi.org/10.1145/1102351.1102389 Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Hernandez Luis, Baladron Carlos, Aguiar Javier M., Carro Belen, Sanchez-Esguevillas Antonio J., Lloret Jaime, and Massana Joaquim. 2014. A survey on electric power demand forecasting: Future trends in smart grids, microgrids and smart buildings. IEEE Commun. Surveys Tutor. 16, 3 (2014), 14601495. https://doi.org/10.1109/SURV.2014.032014.00094Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Hu Yaocong, Lu MingQi, and Lu Xiaobo. 2018. Spatial-temporal fusion convolutional neural network for simulated driving behavior recognition. In Proceedings of the 15th International Conference on Control, Automation, Robotics and Vision (ICARCV’18). IEEE, 12711277. https://doi.org/10.1109/ICARCV.2018.8581201Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Iooss Bertrand and Lemaître Paul. 2014. A review on global sensitivity analysis methods. Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications (Apr. 2014). Retrieved from http://arxiv.org/abs/1404.2405.Google ScholarGoogle Scholar
  22. [22] Jaradat Manar, Jarrah Moath, Bousselham Abdelkader, Jararweh Yaser, and Al-Ayyoub Mahmoud. 2015. The internet of energy: Smart sensor networks and big data management for smart grid. Procedia Comput. Sci. 56, 1 (2015), 592597. https://doi.org/10.1016/j.procs.2015.07.250Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Alvarado Ernesto, Sandberg David, and Pickford S. G.. 1998. Modeling large forest fires as extreme events. Northwest Science 72 (1998), 6675.Google ScholarGoogle Scholar
  24. [24] Joseph Shibily, Jasmin E. A., and Chandran Soumya. 2015. Stream computing: Opportunities and challenges in smart grid. Procedia Technol. 21 (2015), 4953. https://doi.org/10.1016/j.protcy.2015.10.008Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Keemink H.. 2016. Detecting Central Heating Boiler Malfunctions Using Smart-Thermostat Data. Master thesis, TU Delft. http://resolver.tudelft.nl/uuid:85884118-faf6-42ed-9e90-a92813159367.Google ScholarGoogle Scholar
  26. [26] Khameis Abrar, Rashed Shaikhah, Abou-Elnour Ali, and Tarique Mohammed. 2015. Zigbee-based optimal scheduling system for home appliances in the united arab emirates. Netw. Protocols Algor. 7, 2 (2015), 6080. https://doi.org/10.5296/npa.v7i2.7676Google ScholarGoogle Scholar
  27. [27] Khan Saeed Uz Zaman, Shovon Tanvir Hasnain, Shawon Jubayer, Zaman Adeeb Shahriar, and Sabyasachi Saadi. 2013. Smart box: A TV remote controller based programmable home appliance manager. In Proceedings of the International Conference on Informatics, Electronics and Vision (ICIEV’13). https://doi.org/10.1109/ICIEV.2013.6572610Google ScholarGoogle Scholar
  28. [28] Lloret Jaime, Tomas Jesus, Canovas Alejandro, and Parra Lorena. 2016. An integrated IoT architecture for smart metering. IEEE Commun. Mag. 54, 12 (Dec. 2016), 5057. https://doi.org/10.1109/MCOM.2016.1600647CM Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Lomet Aurore, Suard Frédéric, and Chèze David. 2015. Statistical modeling for real domestic hot water consumption forecasting. Energy Procedia 70 (2015), 379387. https://doi.org/10.1016/j.egypro.2015.02.138Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Lorincz J., Capone A., and Wu Jinsong. 2019. Greener energy-efficient and sustainable networks: State-of-the-art and new trends. Sensors 19, 22 (2019), 4864. https://doi.org/10.3390/s19224864Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Ma Zhanyu, Li Hailong, Sun Qie, Wang Chao, Yan Aibin, and Starfelt Fredrik. 2014. Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems. Energy Build. 85 (2014), 664672. https://doi.org/10.1016/j.enbuild.2014.09.048Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Mauri Andrea, Psyllidis Achilleas, and Bozzon Alessandro. 2018. Social smart meter: Identifying energy consumption behavior in user-generated content. In Proceedings of the World Wide Web Conference (WWW’18). ACM Press, New York, New York, 195198. https://doi.org/10.1145/3184558.3186977 Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. [33] Mena R., Rodríguez F., Castilla M., and Arahal M. R.. 2014. A prediction model based on neural networks for the energy consumption of a bioclimatic building. Energy Build. 82 (2014), 142155. https://doi.org/10.1016/j.enbuild.2014.06.052Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Modi Krishna and Oza Bhavesh. 2017. Outlier analysis approaches in data mining. Int. J. Innovat. Res. Technol. 3, 7 (2017), 23496002.Google ScholarGoogle Scholar
  35. [35] Montgomery Douglas C., Jennings Cheryl L., and Kulahci Murat. 2015. Introduction to Time Series Analysis and Forecasting. Wiley-Blackwell.Google ScholarGoogle Scholar
  36. [36] Reliability Office of Electricity Delivery and Energy. 2016. Advanced metering infrastructure and customer systems. Results from the Smart Grid Investment Grant Program. Retrieved from https://www.energy.gov/sites/prod/files/2016/12/f34/AMISummaryReport_09-26-16.pdf.Google ScholarGoogle Scholar
  37. [37] Patgiri Ripon and Ahmed Arif. 2016. Big data: The V’s of the game changer paradigm. In Proceedings of the IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS’16). IEEE, 1724. https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0014Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Picardie Energie. 2019. Retrieved from https://www.pass-renovation.picardie.fr/Google ScholarGoogle Scholar
  39. [39] Provost Foster and Fawcett Tom. 2014. Authors’ response to Gong’s, “Comment on data science and its relationship to big data and data-driven decision making.”Big Data 2, 1 (2014), 1. https://doi.org/10.1089/big.2014.1516Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Ramseur Jonathan L.. 2019. U.S. carbon dioxide emissions in the electricity sector: Factors, trends, and projections. CRS Report No. R45453. Retrieved from Congressional Research Service website: https://crsreports.congress.gov/product/pdf/R/R45453.Google ScholarGoogle Scholar
  41. [41] Salina B. C. and Malathi P.. 2014. An efficient data fusion architecture for location estimation using FPGA. International Journal of Engineering Research and Technology (IJERT). 3. https://www.ijert.org/research/an-efficient-data-fusion-architecture-for-location-estimation-using-fpga-IJERTV3IS10849.pdf.Google ScholarGoogle Scholar
  42. [42] Sendra Sandra, Lloret Jaime, García Miguel, and Toledo José F.. 2011. Power saving and energy optimization techniques for wireless sensor networks. J. Commun. 6, 6 (2011), 439459. https://doi.org/10.4304/jcm.6.6.439-459Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Shi Heng, Xu Minghao, and Li Ran. 2018. Deep learning for household load forecasting-a novel pooling deep RNN. IEEE Trans. Smart Grid 9, 5 (2018), 52715280. https://doi.org/10.1109/TSG.2017.2686012Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Suganthi L. and Samuel Anand A.. 2012. Energy models for demand forecasting - a review. Renew. Sustain. Energy Rev. 16, 2 (2012), 12231240. https://doi.org/10.1016/j.rser.2011.08.014Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Utility Analytics Institute. 2017. The current state of smart grid analytics. [Report] ABB. Retrieved from ABB Publication Library: https://library.e.abb.com/public/76885e5685124f3d97e27b5e146825ac/The%20Current%20State%20of%20Smart%20Grid%20Analytics%20Report%20-%20June%202017.pdf.Google ScholarGoogle Scholar
  46. [46] Vargas-Solar G., Zechinelli-Martini J. L., and Espinosa-Oviedo J. A.. 2017. Big data management: What to keep from the past to face future challenges?Data Sci. Eng. 2, 4 (Dec. 2017), 328345. https://doi.org/10.1007/s41019-017-0043-3Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Wang Xiping and Meng Ming. 2012. A hybrid neural network and ARIMA model for energy consumption forecasting. J. Comput. 7, 5 (2012), 11841190. https://doi.org/10.4304/jcp.7.5.1184-1190Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Wang Yi, Chen Qixin, Hong Tao, and Kang Chongqing. 2018. Review of smart meter data analytics: Applications, methodologies, and challenges. IEEE Trans. Smart Grid(June2018), 124. https://doi.org/10.1109/TSG.2018.2818167 Retrieved from https://arxiv:arXiv:1802.04117v2.Google ScholarGoogle Scholar
  49. [49] Weng Yang and Rajagopal Ram. 2015. Probabilistic baseline estimation via Gaussian process. IEEE Power and Energy Society General Meeting 2015-Septe (2015). https://doi.org/10.1109/PESGM.2015.7285756Google ScholarGoogle Scholar
  50. [50] Wijaya Tri Kurniawan, Vasirani Matteo, and Aberer Karl. 2014. When bias matters: An economic assessment of demand response baselines for residential customers. IEEE Trans. Smart Grid 5, 4 (2014), 17551763. https://doi.org/10.1109/TSG.2014.2309053Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Working Group on Big Data Analytics and Machine Learning and Artificial Intelligence in the Smart Grid. 2017. Big data analytics in the smart grid. IEEE Smart Grid [White Paper]. Retrieved from https://smartgrid.ieee.org/images/files/pdf/big_data_analytics_white_paper.pdf.Google ScholarGoogle Scholar
  52. [52] Wu Jinsong, Guo Song, Huang Huawei, Liu William, and Xiang Yong. 2018. Information and communications technologies for sustainable development goals: State-of-the-art, needs and perspectives. IEEE Commun. Surveys Tutor. 20, 3 (2018), 23892406.Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Xie Jingrui, Hong Tao, and Stroud Joshua. 2015. Long-term retail energy forecasting with consideration of residential customer attrition. IEEE Trans. Smart Grid 6, 5 (2015), 22452252. https://doi.org/10.1109/TSG.2014.2388078Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Yang Junjing, Ning Chao, Deb Chirag, Zhang Fan, Cheong David, Lee Siew Eang, Sekhar Chandra, and Tham Kwok Wai. 2017. k-shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement. Energy Build. 146 (2017), 2737. https://doi.org/10.1016/j.enbuild.2017.03.071Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Wang Yi, Chen Qixin, Kang Chongqing, Zhang Mingming, Wang Ke, and Zhao Yun. 2015. Load profiling and its application to demand response: A review. Tsinghua Sci. Technol. 20, 2 (apr 2015), 117129. https://doi.org/10.1109/TST.2015.7085625Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Yu Wei, Li Baizhan, Lei Yarong, and Liu Meng. 2011. Analysis of a residential building energy consumption demand model. Energies 4, 3 (2011), 475487. https://doi.org/10.3390/en4030475Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Zhao Hai Xiang and Magoulès Frédéric. 2012. A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16, 6 (2012), 35863592. https://doi.org/10.1016/j.rser.2012.02.049Google ScholarGoogle ScholarCross RefCross Ref

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  1. GREENHOME: A Household Energy Consumption and CO2 Footprint Metering Environment

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          • Published in

            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 22, Issue 3
            August 2022
            631 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/3498359
            • Editor:
            • Ling Liu
            Issue’s Table of Contents

            ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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            Publication History

            • Published: 22 January 2022
            • Accepted: 1 December 2021
            • Revised: 1 August 2021
            • Received: 1 June 2020
            Published in toit Volume 22, Issue 3

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