Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network

By Huaizhi Wang, Haiyan Yi, Jianchun Peng, Guibin Wang, Yitao Liu, Hui Jiang, Wenxin Liu. 2017

347 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/9f97b0a6799f250204b1fb806739ad873dfbf04b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Annamária R. Várkonyi-Kóczy
2. Timon Rabczuk
3. Shahaboddin Shamshirband
4. Amir Mosavi

In Electrical and Electronic Engineering

1. Timon Rabczuk
2. Shahaboddin Shamshirband
3. Amir Mosavi
4. Xiaoying Zhuang

In Renewable Energy, Sustainability and the Environment

1. Shahaboddin Shamshirband

Citations in the corpus

• Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism (2019, background): “Deep learning has been applied in the field of PV power forecasting and achieved outstanding results [18], [19].”
• State of the Art of Machine Learning Models in Energy Systems, a Systematic Review (2019, methodology): “(2017) [73] introduced a new method for photovoltaic electricity forecasting utilizing wavelet transform and deep convolutional neural network.”