Error analysis of hybrid photovoltaic power forecasting models: A case study of mediterranean climate

2015 ◽  
Vol 100 ◽  
pp. 117-130 ◽  
Author(s):  
Maria Grazia De Giorgi ◽  
Paolo Maria Congedo ◽  
Maria Malvoni ◽  
Domenico Laforgia
Keyword(s):  
Error Analysis ◽  
Mediterranean Climate ◽  
Forecasting Models ◽  
Photovoltaic Power ◽  
Power Forecasting

scholarly journals Data on photovoltaic power forecasting models for Mediterranean climate

Data in Brief ◽  
2016 ◽  
Vol 7 ◽  
pp. 1639-1642 ◽  
Author(s):  
M. Malvoni ◽  
M.G. De Giorgi ◽  
P.M. Congedo
Keyword(s):  
Mediterranean Climate ◽  
Forecasting Models ◽  
Photovoltaic Power ◽  
Power Forecasting

scholarly journals The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Abdelhakim El hendouzi ◽  
Abdennaser Bourouhou ◽  
Omar Ansari
Keyword(s):  
Absolute Error ◽  
Skill Score ◽  
Weather Data ◽  
Short Term ◽  
Pv System ◽  
Pv Systems ◽  
Forecasting Models ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Power Forecasting

The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.

Accurate photovoltaic power forecasting models using deep LSTM-RNN

2017 ◽  
Vol 31 (7) ◽  
pp. 2727-2740 ◽  
Author(s):  
Mohamed Abdel-Nasser ◽  
Karar Mahmoud
Keyword(s):  
Forecasting Models ◽  
Photovoltaic Power ◽  
Power Forecasting

Day-Ahead Probabilistic Photovoltaic Power Forecasting Models Based on Quantile Regression Neural Networks

2017 ◽  
Author(s):  
L. Alfredo Fernandez-Jimenez ◽  
Sonia Terreros-Olarte ◽  
Montserrat Mendoza-Villena ◽  
Eduardo Garcia-Garrido ◽  
Enrique Zorzano-Alba ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quantile Regression ◽  
Forecasting Models ◽  
Photovoltaic Power ◽  
Power Forecasting

scholarly journals Photovoltaic power forecasting method based on adaptive classification strategy and HO-SVR algorithm

2020 ◽  
Vol 6 ◽  
pp. 921-928
Author(s):  
T. Xun ◽  
S.H. Lei ◽  
X.C. Ding ◽  
K. Chen ◽  
K. Huang ◽  
...  
Keyword(s):  
Adaptive Classification ◽  
Photovoltaic Power ◽  
Forecasting Method ◽  
Classification Strategy ◽  
Power Forecasting
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