Accurate photovoltaic power forecasting models using deep LSTM-RNN

By Mohamed Abdel-Nasser, Karar Mahmoud. Neural computing & applications (Print), 2017

676 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/36674d9380ca57c6599ea71951f0ea9c423a84ef

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. Olufemi A. Omitaomu
2. Haoran Niu

In Electrical and Electronic Engineering

1. Olufemi A. Omitaomu
2. Haoran Niu

Citations in the corpus

• Artificial Intelligence Techniques in Smart Grid: A Survey (2021, background): “DL techniques have been rapidly developed in recent years, and numerous successful structures have been proposed to solve smart grid problems, including deep belief networks [23], convolution neural networks (CNNs) [24], recurrent neural networks (RNNs) [25], generative adversarial networks [26], an”