Feeder-Level Deep Learning-based Photovoltaic Penetration Estimation Scheme. / Zhang, Xiaoyu; Kuenzel, Stefanie; Watkins, Chris.

2020. Paper presented at 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, Nanjing, China.

Research output: Contribution to conferencePaperpeer-review




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 Sep 202023 Sep 2020


Conference12th IEEE PES Asia-Pacific Power and Energy Engineering Conference
Internet address
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 38038098