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Household power consumption pattern modeling through a single power sensor

Author

Listed:
  • Yang, Lu
  • Xie, Pengli
  • Bi, Chongke
  • Zhang, Ronghui
  • Cai, Bowen
  • Shao, Xiaowei
  • Wang, Rongben

Abstract

Increasing concerns about energy shortage and environmental pollution revealed the necessity to fully use the limited electric power. For this purpose, a lot of researches have focus on establishing household power consumption model. Currently, most existing models are setting one sensor for each appliance. However, the prediction precision of such kind of multi-sensors-based models cannot fully satisfy users’ requirements due to the low resolution (frequency sampling). On the other hand, it is still a challenge task to design a Single-Sensor-Based prediction model for power consumption. Because it is difficult to divide the different appliances collected in the same period (parallel appliances) by a single sensor. In this paper, we proposed a Single-Sensor-Based power consumption model. Our model can successfully predict the possible power consumption with HIGH precision. Firstly, by using a single sensor for data collection, a Bayesian based method is proposed to detect and decompose the parallel appliances. Secondly, the prediction precision is greatly improved by using high resolution (our model is 1 s, while existing model is 10 min). Here, a Base & Event Power Consumption Model (BEPC model) is proposed to deal with the complicated data from the high resolution samplings. Finally, in order to demonstrate the effectiveness of the proposed model, several experiments have been carried out through comparing with the ground truth data. Note that, the ground truth data is collected from 90 days consecutive daily life.

Suggested Citation

  • Yang, Lu & Xie, Pengli & Bi, Chongke & Zhang, Ronghui & Cai, Bowen & Shao, Xiaowei & Wang, Rongben, 2020. "Household power consumption pattern modeling through a single power sensor," Renewable Energy, Elsevier, vol. 155(C), pages 121-133.
    • Handle: RePEc:eee:renene:v:155:y:2020:i:c:p:121-133
    DOI: 10.1016/j.renene.2020.03.118
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    References listed on IDEAS

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