期刊
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
卷 56, 期 6, 页码 7185-7192出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2020.3025742
关键词
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
资金
- NEC Lab
- U.S. Department of Energy AGGREGATE project
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据