Back-filling Missing Data When Predicting Domestic Electricity Consumption From Smart Meter Data
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913599014830080 |
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| author | Chen, Xianjuan Cai, Shuxiang Smeaton, Alan F. |
| author_facet | Chen, Xianjuan Cai, Shuxiang Smeaton, Alan F. |
| contents | This study uses data from domestic electricity smart meters to estimate annual electricity bills for a whole year. We develop a method for back-filling data smart meter for up to six missing months for users who have less than one year of smart meter data, ensuring reliable estimates of annual consumption. We identify five distinct electricity consumption user profiles for homes based on day, night, and peak usage patterns, highlighting the economic advantages of Time-of-Use (ToU) tariffs over fixed tariffs for most users, especially those with higher nighttime consumption. Ultimately, the results of this study empowers consumers to manage their energy use effectively and to make informed choices regarding electricity tariff plans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03574 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Back-filling Missing Data When Predicting Domestic Electricity Consumption From Smart Meter Data Chen, Xianjuan Cai, Shuxiang Smeaton, Alan F. Computers and Society Artificial Intelligence This study uses data from domestic electricity smart meters to estimate annual electricity bills for a whole year. We develop a method for back-filling data smart meter for up to six missing months for users who have less than one year of smart meter data, ensuring reliable estimates of annual consumption. We identify five distinct electricity consumption user profiles for homes based on day, night, and peak usage patterns, highlighting the economic advantages of Time-of-Use (ToU) tariffs over fixed tariffs for most users, especially those with higher nighttime consumption. Ultimately, the results of this study empowers consumers to manage their energy use effectively and to make informed choices regarding electricity tariff plans. |
| title | Back-filling Missing Data When Predicting Domestic Electricity Consumption From Smart Meter Data |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2412.03574 |