The increasing penetration of renewable energy to the distribution grids, especially photovoltaic (PV), helps smooth out supply and demand, and reduces greenhouse gas emissions. However, the PV generation is behind-the-meter, and cannot be detected by the smart meter. To address this problem, a hybrid regression multi-layer perceptron (MLP) deep neural network (DNN) model is designed to separate the PV generation from the overall grid measurements. The model utilizes grid measurements, weather-related measurements, satellite-driven irradiance measurements, and temporal information as inputs to evaluate the PV generation in real-time. We also examine the performance of the model with different levels of PV penetration. We show the proposed model reduces the mean square error by 49% compared to single variable input models.
Original languageEnglish
Publication statusPublished - 13 Oct 2020
Event12th IEEE PES Asia-Pacific Power and Energy Engineering Conference - Nanjing, China
Duration: 20 Sept 202023 Sept 2020


Conference12th IEEE PES Asia-Pacific Power and Energy Engineering Conference
Internet address


  • deep neural network
  • photovoltaic penetration
  • energy disaggregation

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