A Deep Learning-Based Strategy to the Energy Management-Advice for Time-of-Use Rate of Household Electricity Consumption

Lu-Xian Wu, Shin-Jye Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


With the high industrialization at a rapid pace, the demand for energy increases exponentially, but it is difficult to meet the balance of demand and supply. Therefore, how to effectively meet the balance of demand and supply intelligently has become a popular issue in this century. In recent years, Taiwan Power Company (Taipower), the biggest electric company in Taiwan, is committed to the construction of Advanced Metering Infrastructure (AMI), which provides communication channels and enables demand-side users to participate in load dispatch. On the other hand, the construction of AMI is expected to generate a tremendous number of valuable data on electricity consumption, but it is not easy to convert these data into effective information by the conventional quantitative methods. In as much as the rapid progression of AI technology in the industrial field, the application of AI technology in the technology management has become an increasing issue as an interdisciplinary study. To address this task, this work applies the recurrent neural network based on deep learning to predict low-voltage usage shortly by the electricity information of low-voltage user and meteorological data. After many vicissitudes, the electricity consumption per hour can be predicted and a sound energy arrangement can be therefore planned. Through introducing the proposed model, Taipower Company will have an effective capability that schedules power, reduces unnecessary backup power, and provides time-consuming electricity prices for industrial enterprises accurately among high usage of Taiwan industries.

Original languageEnglish
Pages (from-to)305-311
Number of pages7
JournalJournal of Internet Technology
Issue number1
StatePublished - Jan 2020


  • Machine learning
  • Deep learning
  • Energy management


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