Abstract
Efforts of electrical utilities to respond to climate change require the development of increasingly sophisticated, integrated electrical grids referred to as the “smart grids”. Much of the smart grid effort focuses on integration of renewable generation into the electricity grid and on increased monitoring and automation of electrical transmission functions. However, a key component of smart grid development is the introduction of the smart electrical meter for all residential electrical customers. Smart meter (SM) deployment is the corner stone of the smart grid. In addition to adding new functionality to support system reliability, SMs provide the technological means for utilities to institute new programs to allow their customers to better manage and reduce their electricity use and to support increased renewable generation to reduce greenhouse emissions from electricity use. As such, this paper presents our research towards the study of a smart home environment and how the data produced is used to profile energy usage in homes. The validity of the data is justified through analysis of the profiles generated while consumers use energy during off peak and peak periods. By learning, understanding and feeding patterns of home behaviour, it is possible to educate the consumer regarding their energy usage, helping them to reduce costs but also the emissions from their home.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nicolson, M., Huebner, G., Shipworth, D.: Are consumers willing to switch to smart time of use electricity tariffs? The importance of loss-aversion and electric vehicle ownership. Energy Res. Soc. Sci. 23, 82–96 (2017)
Malekian, R., Bogatinoska, D.C., Karadimce, A., Ye, N., Trengoska, J., Nyako, W.A.: A novel smart ECO model for energy consumption optimization. Elektron. Elektrotech. 21(6), 75–80 (2015)
Makonin, S., Popowich, F., Bartram, L., Gill, B., Bajić, I.V.: AMPds: a public dataset for load disaggregation and eco-feedback research. In: 2013 IEEE Electrical Power & Energy Conference, pp. 1–6. IEEE, August 2013
Ullah, M.N., Javaid, N., Khan, I., Mahmood, A., Farooq, M.U.: Residential energy consumption controlling techniques to enable autonomous demand side management in future smart grid communications. In: 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 545–550. IEEE, October 2013
Birol, F., Director, I.E.: Energy Efficiency Market Report 2016. International Energy Agency (IEA) (2016)
Bhati, A., Hansen, M., Chan, C.M.: Energy conservation through smart homes in a smart city: a lesson for Singapore households. Energy Policy 104, 230–239 (2017)
Karlstrøm, H., Ryghaug, M.: Public attitudes towards renewable energy technologies in Norway. The role of party preferences. Energy Policy 67, 656–663 (2014)
Dutta, G., Mitra, K.: A literature review on dynamic pricing of electricity. J. Oper. Res. Soc. 68(10), 1131–1145 (2017)
Jonker, R.T., Przydatek, P.B., Gunn, C.N., Teachman, M.E., Antoniou, C.A.: Revenue meter with power quality features. U.S. Patent 7,006,934. Power Measurement Ltd. (2006)
da Graça Carvalho, M.: EU energy and climate change strategy. Energy 40(1), 19–22 (2012)
Kaboutari, A., et al.: An evaluation of feature selection methods for positive-unlabeled learning in text classification (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mwansa, M., Hurst, W., Shen, Y. (2020). Towards Smart Meter Energy Analysis and Profiling to Support Low Carbon Emissions. In: Khalaf, M., Al-Jumeily, D., Lisitsa, A. (eds) Applied Computing to Support Industry: Innovation and Technology. ACRIT 2019. Communications in Computer and Information Science, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-030-38752-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-38752-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38751-8
Online ISBN: 978-3-030-38752-5
eBook Packages: Computer ScienceComputer Science (R0)