scholarly journals Data‐driven models for short‐term ocean wave power forecasting

2021 ◽  
Author(s):  
Chenhua Ni
Keyword(s):  
Ocean Wave ◽  
Data Driven ◽  
Wave Power ◽  
Short Term ◽  
Power Forecasting

scholarly journals Ocean wave power forecasting using convolutional neural networks

2021 ◽  
Author(s):  
Pedro Bento ◽  
José Pombo ◽  
Maria do Rosário Calado ◽  
Sílvio Mariano
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Ocean Wave ◽  
Wave Power ◽  
Power Forecasting

High dimensional very short-term solar power forecasting based on a data-driven heuristic method

Energy ◽  
2021 ◽  
Vol 219 ◽  
pp. 119647
Author(s):  
Amir Rafati ◽  
Mahmood Joorabian ◽  
Elaheh Mashhour ◽  
Hamid Reza Shaker
Keyword(s):  
Solar Power ◽  
Heuristic Method ◽  
Data Driven ◽  
High Dimensional ◽  
Short Term ◽  
Solar Power Forecasting ◽  
Power Forecasting

scholarly journals Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3992
Author(s):  
Mohamed Massaoudi ◽  
Ines Chihi ◽  
Lilia Sidhom ◽  
Mohamed Trabelsi ◽  
Shady S. Refaat ◽  
...  
Keyword(s):  
Random Forest ◽  
Smart Grids ◽  
Weather Conditions ◽  
Feature Weighting ◽  
Attribute Selection ◽  
Data Driven ◽  
Short Term ◽  
Pv System ◽  
Comparative Performance ◽  
Power Forecasting

Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstrates the superiority of the proposed technique versus several data-driven machine learning models when applied to a typical distributed PV system.

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