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Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model
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DOI：10.1109/TIA.2020.3025742.png)
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Abstract
En 中文
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.
Keywords:
Estimation
Predictive models
Meteorology
Forecasting
Machine learning
Training
Uncertainty
Autoencoder long short-term memory (AE-LSTM)
data processing automation
day-ahead forecasting
deep learning
hybrid model
photovoltaic (PV) power estimation
renewable energy integration
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