Physical and hybrid methods comparison for the day ahead PV output power forecast
By Emanuele Giovanni Carlo Ogliari, Alberto Dolara, Giampaolo Manzolini, Sonia Leva. 2017
201 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/8af0c836a992d6eec86d8fafa9b4d67d586a03ef
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
- Fotios Petropoulos
- Christoph Bergmeir
- Spyros Makridakis
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Devon K. Barrow
- Yanfei Kang
- Feng Li
In Electrical and Electronic Engineering
- Pierre Pinson
- Thordis Linda Thorarinsdottir
- Fotios Petropoulos
- Christoph Bergmeir
- Ricardo J. Bessa
- Florian Ziel
- Muhammad Naveed Akhter
- Paul Goodwin
In Renewable Energy, Sustainability and the Environment
- Nikolaos Kourentzes
- Fotios Petropoulos
- M. Babai
- Stephan Kolassa
- Konstantinos Nikolopoulos
- A. Syntetos
- Spyros Makridakis
- Vassilios Assimakopoulos
Citations in the corpus
- Forecasting: theory and practice (2020, methodology): “Ogliari et al. (2017) compared the PV output power day-ahead forecasts performed by deterministic (based on three and five parameters electric equivalent circuit) and stochastic hybrid (based on artificial neural network models) methods aiming to find the best performance conditions.”
- Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, background): “Ogliari et al. [26] compared physical and hybrid methods for forecasting PV output power one day ahead, using real data from the existing power plant in Milan, Italy.”