Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model
- GE Digital, Bothell, WA (United States); Washington State University
- Washington State Univ., Pullman, WA (United States)
- NEC Labs America, Cupertino, CA (United States)
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
- Research Organization:
- Washington State Univ., Pullman, WA (United States)
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- Grant/Contract Number:
- OE0000878
- OSTI ID:
- 1810249
- Report Number(s):
- OE0000878--WSUpaper-1
- Journal Information:
- IEEE Transactions on Industry Applications, Journal Name: IEEE Transactions on Industry Applications Journal Issue: 6 Vol. 56; ISSN 0093-9994
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
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