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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 conference › Paper

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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 conference › Paper

BibTeX

@conference{270bfc91e89f45ec9a3b3adc995f3024,

title = "Feeder-Level Deep Learning-based Photovoltaic Penetration Estimation Scheme",

abstract = "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.",

keywords = "deep neural network, photovoltaic penetration, energy disaggregation",

author = "Xiaoyu Zhang and Stefanie Kuenzel and Chris Watkins",

year = "2020",

month = oct,

day = "13",

doi = "10.1109/APPEEC48164.2020.9220536",

language = "English",

note = "12th IEEE PES Asia-Pacific Power and Energy Engineering Conference ; Conference date: 20-09-2020 Through 23-09-2020",

url = "https://ieee-appeec.org/",

}

RIS

TY - CONF

T1 - Feeder-Level Deep Learning-based Photovoltaic Penetration Estimation Scheme

AU - Zhang, Xiaoyu

AU - Kuenzel, Stefanie

AU - Watkins, Chris

PY - 2020/10/13

Y1 - 2020/10/13

N2 - 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.

AB - 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.

KW - deep neural network

KW - photovoltaic penetration

KW - energy disaggregation

U2 - 10.1109/APPEEC48164.2020.9220536

DO - 10.1109/APPEEC48164.2020.9220536

M3 - Paper

T2 - 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference

Y2 - 20 September 2020 through 23 September 2020

ER -