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A prototype tool for automatically generating energy-saving advice based on smart meter data

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Abstract

As many countries and regions have started large-scale deployment of smart meters, there is a growing amount of data on electricity use available for energy efficiency services. We have developed a novel tool that, based on smart meter data, automatically generates customised energy-saving advice to commercial and industrial customers. This type of audit tool could enormously expand the target of energy audits to almost all small- and medium-sized enterprises (SMEs) with smart metering at a low cost per customer. In this paper, we explain the structure of and approaches that we used in our prototype tool, such as fault detection, energy disaggregation, social comparison and benchmarking and selective visualisation. We also show test case results for the tool by using smart meter data from 34 public buildings in Japan. While the prototype tool presented in this paper has some limitations, the approach and the basic structure of the tool are valuable and provide the basis for more sophisticated tools.

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Notes

  1. Nonetheless, data from gas smart meters would be useful for energy assessment and thus would become a valuable additional data stream to our tool, which in the current version uses only electricity demand data.

  2. In Japan, C&I customers are typically billed for electricity for two ways: demand charges, which are determined by the highest demand (or contracted demand) in a year and are measured in kilowatts (kW); and consumption charges, which are determined by the total amount of electricity used during a billing period and are measured in kilowatt-hours (kWh).

  3. The accuracy of lighting load disaggregation from smart meter data might be improved by applying some of the techniques from artificial intelligence (AI), using data gathered from metered equipment as teaching data.

  4. Note that the adoption of LED lighting has rapidly accelerated in the Japanese market since the publication of the report by ANRE in 2011, and so the electricity demand for lighting may have changed to some extent.

  5. Note no reports have been sent because this was only test output. However, a small field trial is being prepared in collaboration with the city government to send reports to some of those buildings and analyse their responses.

  6. This format of report is inspired by HER and BER (Mogilner 2014; Stewart 2015).

  7. This is not surprising because the outcome of HER is also estimated to be around 2 to 5% savings in well-controlled large-scale experiments (Allcott 2011; Allcott and Rogers 2014).

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Acknowledgements

The authors would like to thank the city government for permission to use the electricity demand data of their buildings. This paper is based on a Japanese report by the authors (Komatsu et al. 2016) with substantial modifications.

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  1. Central Research Institute of Electric Power Industry, 1-6-1 Otemachi Chiyoda-ku, Tokyo, 100-8126, Japan

    Osamu Kimura, Hidenori Komatsu, Ken-ichiro Nishio & Toshihiro Mukai

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  1. Osamu Kimura
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Correspondence to Osamu Kimura.

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Kimura, O., Komatsu, H., Nishio, Ki. et al. A prototype tool for automatically generating energy-saving advice based on smart meter data. Energy Efficiency 11, 1247–1264 (2018). https://doi.org/10.1007/s12053-018-9639-3

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