The present study focuses on wind power forecasting (WPF) models based on deep neural networks (DNNs), aiming to evaluate current approaches, identify gaps, and provide insights into their importance for the integration of Renewable Energy Sources (R...
Currently, the privileged position of wind energy producers is being weakened by their enforced participation in the market on equal terms. This requires accurate production forecasting. The main aim of this study is to comparatively examine the wind...
To meet the increasing wind power forecasting (WPF) demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD) pre-calculated flow fields (CPFF)-based WPF is a promising phy...
Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain be...
Accurate wind power forecasting (WPF) is crucial to enhance availability and reap the benefits of integration into power grids. The time lag of wind power generation lags the time of wind speed changes, especially in ultra-short-term forecasting. The...
Accurate wind speed and power forecasting are key to optimizing renewable wind station management, which is essential for smart and zero-energy cities. This paper presents a novel integrated wind speed–power forecasting system (WSPFS) that oper...
Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management dif...
Accurate wind power forecasting (WPF) is critical in optimizing grid operations and efficiently managing wind energy resources. Challenges arise from the inherent volatility and non-stationarity of wind data, particularly in short-to-medium-term WPF,...
Large-scale wind power access may cause a series of safety and stability problems. Wind power forecasting (WPF) is beneficial to dispatch in advance. In this paper, a new extreme gradient boosting (XGBoost) model with weather similarity analysis and...
In order to enhance the accuracy of short-term wind power forecasting (WPF), a short-term wind power forecasting method based on historical wind resources by data mining has been designed. Firstly, the spoiled data resulting from wind turbine and met...
A more accurate hourly prediction of day-ahead wind power can effectively reduce the uncertainty of wind power integration and improve the competitiveness of wind power in power auction markets. However, due to the inherent stochastic and intermitten...
This paper develops a Sustainable Artificial Intelligence-Driven Wind Power Forecasting System (SAI-WPFS) to enhance the integration of renewable energy while minimizing the environmental footprint of deep learning computations. Although deep learnin...