Review of photovoltaic power forecasting

By Javier Antoñanzas, N. Osório, R. Escobar, Rubén Urraca, F. J. Martínez-de-Pisón, Fernando Antoñanzas-Torres. 2016

1,182 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/18ce24f2c2a89ffa56692043dca9b604c49a6668

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. Karar Mahmoud
2. Mohamed Abdel-Nasser
3. Long Wang
4. Muhammad Naveed Akhter
5. Ruben Loaiza-Maya
6. Gary Koop
7. Gael M. Martin
8. Chao Huang

In Electrical and Electronic Engineering

1. Karar Mahmoud
2. Mohamed Abdel-Nasser
3. Muhammad Naveed Akhter
4. Hazlie Mokhlis
5. Noraisyah Mohamed Shah
6. Anastasios Panagiotelis
7. Saad Mekhilef
8. Giampaolo Manzolini

In Renewable Energy, Sustainability and the Environment

1. Muhammad Naveed Akhter
2. Karar Mahmoud
3. Gary Koop
4. Hazlie Mokhlis
5. Noraisyah Mohamed Shah
6. Saad Mekhilef
7. Mohamed Abdel-Nasser
8. Adel Mellit

Citations in the corpus

• Accurate photovoltaic power forecasting models using deep LSTM-RNN (2017, methodology): “These methods can be classified into four categories: (1) statistical methods, (2) artificial intelligence methods, (3) physical methods, and (4) hybrid methods [1, 51].”
• Data-Driven Short-Term Solar Irradiance Forecasting Based on Information of Neighboring Sites (2019, background): “[5] comprehensively reviewed forecasting techniques according to forecasting horizons.”
• Bayesian forecasting in economics and finance: A modern review (2022, methodology): “(2019) for load forecasting, Antonanzas et al. (2016) for solar power forecasting and Giebel and Kariniotakis (2017) for wind power forecasting. These reviews indicate that the majority of work in electricity forecasting does not employ a Bayesian approach; however notwithstanding this, Bayesian met”
• Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, background): “Due to these limitations, machine learning (such as support vector machine – SVM, artificial neural network – ANN, extreme learning machine – ELM) and metaheuristic techniques are of recent interest, with few review papers found related to these topics [3, 18–21].”
• Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques (2019, background): “In addition, forecasting leads to a lower number of units in hot standby and a reduced cost for the operation of the entire power system [18].”
• Physical and hybrid methods comparison for the day ahead PV output power forecast (2017, background): “Yet in a review [1] they are proved to be the most applied techniques.”