Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. However, uncertain wea...

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Published in:	IEEE transactions on industry applications Vol. 56; no. 6; pp. 7185 - 7192
Main Authors:	Zhang, Yue, Qin, Chuan, Srivastava, Anurag K., Jin, Chenrui, Sharma, Ratnesh K.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Autoencoder long short-term memory (AE-LSTM) data processing automation day-ahead forecasting Deep learning Economic forecasting Electric power generation Electric power grids Electronic devices ENGINEERING Estimation Forecasting hybrid model Machine learning Mathematical models Meteorological data Meteorology Model accuracy photovoltaic (PV) power estimation Photovoltaic cells Predictive models renewable energy integration Training Uncertainty Weather forecasting
ISSN:	0093-9994, 1939-9367
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Abstract	Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. However, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data are becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this article, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-min interval. The proposed model is motivated by the recent advancement of long-short-term-memory networks and autoencoder, which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations.
AbstractList	Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. Furthermore, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data is becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this paper, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-minute-interval. The proposed model is motivated by the recent advancement of Long-Short-Term-Memory (LSTM) networks and AutoEncoder (AE), which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model (PM) is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. However, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data are becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this article, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-min interval. The proposed model is motivated by the recent advancement of long-short-term-memory networks and autoencoder, which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations.
Author	Sharma, Ratnesh K. Zhang, Yue Srivastava, Anurag K. Qin, Chuan Jin, Chenrui
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SubjectTerms	Algorithms Autoencoder long short-term memory (AE-LSTM) data processing automation day-ahead forecasting Deep learning Economic forecasting Electric power generation Electric power grids Electronic devices ENGINEERING Estimation Forecasting hybrid model Machine learning Mathematical models Meteorological data Meteorology Model accuracy photovoltaic (PV) power estimation Photovoltaic cells Predictive models renewable energy integration Training Uncertainty Weather forecasting
Title	Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model
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