Abstract
In our homes a lot of devices are powered by electricity without us knowing the specific amount. As electricity production has a large, negative environmental impact, we should be more aware about how devices consume power and how we can adapt our daily routine to decrease our electricity requirements. Methods such as Non-Intrusive Load Monitoring (NILM) can provide the user with precise device level electricity data by measuring at a single point in a houses’ electricity network. However, the time resolution of most off-the-shelf power meters is not sufficient for NILM or the meters are locked down for security reasons. Therefore, we have developed our own versatile energy metering framework which consists of a high frequency electricity metering device, a versatile backend for data processing and a webapp for data visualization. The developed hardware is capable of sampling up to 32 kHz, while the software framework allows to extract other power related metrics such as harmonic content. The system’s application ranges from providing transparent electricity usage to the user up to generating load forecasts with fine granularity.
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United States Environmental Protection Agency: Sources of greenhouse gas emissions (2014). https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions. Accessed 24 Jan 2018
Analog Devices: High Performance, Multiphase Energy, and Power Quality Monitoring IC, April 2017. Rev. A
Armel, K.C., Gupta, A., Shrimali, G., Albert, A.: Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy 52, 213–234 (2013)
Bouhouras, A.S., Chatzisavvas, K.C., Panagiotou, E., Poulakis, N., Parisses, C., Christoforidis, G.C.: Load signatures development via harmonic current vectors. In: 2017 52nd International Universities Power Engineering Conference (UPEC), pp. 1–6. IEEE (2017)
World Energy Council: Electricity use per household (2014). https://wec-indicators.enerdata.net/household-electricity-use.html. Accessed 24 Jan 2018
Emse, H.: Co2 calculator (2019). http://www.klimaneutral-handeln.de/php/kompens-berechnen.php#rechner. Accessed 23 Oct 2019
Felgueiras, M.C., Cruz, N., Martins, F., Martins, R.: Buildings sustainability-the non-intrusive load-identification system contribution. J. Clean Energy Technol. 4(5), 367–370 (2016)
Fischer, C.: Feedback on household electricity consumption: a tool for saving energy? Energ. Effi. 1(1), 79–104 (2008)
P3 International: Kill-a-watt (2017). http://www.p3international.com/products/p4400.html. Accessed 24 Jan 2019
Kelly, J., Knottenbelt, W.: The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2, 150007 (2015)
Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, vol. 25, pp. 59–62 (2011)
Kriechbaumer, T., Jorde, D., Jacobsen, H.A.: Waveform signal entropy and compression study of whole-building energy datasets. In: Proceedings of the Tenth ACM International Conference on Future Energy Systems, e-Energy 2019, pp. 58–67. ACM (2019)
Kriechbaumer, T., Ul Haq, A., Kahl, M., Jacobsen, H.A.: Medal: a cost-effective high-frequency energy data acquisition system for electrical appliances. In: Proceedings of the Eighth International Conference on Future Energy Systems, e-Energy 2017, pp. 216–221. ACM (2017)
Scholl, P.M., Völker, B., Becker, B., Laerhoven, K.V.: A multi-media exchange format for time-series dataset curation. In: Kawaguchi, N., Nishio, N., Roggen, D., Inoue, S., Pirttikangas, S., Van Laerhoven, K. (eds.) Human Activity Sensing. SSAE, pp. 111–119. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13001-5_8
Völker, B., Scholl, P.M., Becker, B.: Semi-automatic generation and labeling of training data for non-intrusive load monitoring. In: Proceedings of the Tenth International Conference on Future Energy Systems, e-Energy 2019. ACM (2019)
Völker, B., Scholls, P.M., Schubert, T., Becker, B.: Towards the fusion of intrusive and non-intrusive load monitoring: a hybrid approach. In: Proceedings of the Ninth International Conference on Future Energy Systems, e-Energy 2018, pp. 436–438. ACM (2018)
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Völker, B., Pfeifer, M., Scholl, P.M., Becker, B. (2020). A Versatile High Frequency Electricity Monitoring Framework for Our Future Connected Home. In: Afonso, J., Monteiro, V., Pinto, J. (eds) Sustainable Energy for Smart Cities. SESC 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-030-45694-8_17
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DOI: https://doi.org/10.1007/978-3-030-45694-8_17
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