One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models

By Guochang Wang, Yan Su, Lianjie Shu. 2016

124 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/60e8ee09c9d80280b73ead600869ae4f9310fdc8

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. Muhammad Naveed Akhter
2. Hazlie Mokhlis
3. Noraisyah Mohamed Shah
4. Saad Mekhilef

In Electrical and Electronic Engineering

1. Muhammad Naveed Akhter
2. Hazlie Mokhlis
3. Noraisyah Mohamed Shah
4. Saad Mekhilef
5. Yousef A. Mahmoud
6. Ehab F. El-Saadany
7. Mohamed A. Abdelwahed
8. Zengshi Chen

In Renewable Energy, Sustainability and the Environment

1. Muhammad Naveed Akhter
2. Hazlie Mokhlis
3. Noraisyah Mohamed Shah
4. Saad Mekhilef
5. Yousef A. Mahmoud
6. Ehab F. El-Saadany
7. Mohamed A. Abdelwahed
8. Zengshi Chen

Citations in the corpus

• Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, methodology): “[61] developed a regularised partial functional linear regression model (PFLRM) for one day-ahead forecasting of PV power output.”