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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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USDOE Office of Electricity (OE)
OE0000878
OE0000878-WSUpaper-1
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2020.3025742