Wind is a stochastic and intermittent renewable energy source. Due to its nature, it is extremely hard to forecast wind power. Accurate wind power forecasting can be encouraging and motivating for investors to shed light on future uncertainties caused by global warming. Thus, CO2 and other greenhouse gases (GHG) which are harmful to the environment will not be released into the atmosphere, while generating electrical energy. This paper presents a novel precise, fast and powerful hybrid metaheuristic wind power forecasting approach based on statistical and mathematical data from real weather stations. The model was developed as a hybrid metaheuristic algorithm based on artificial neural networks (ANNs), particle swarm optimization (PSO) and radial movement optimization (RMO). Real-time wind data was gathered from wind measuring stations (WMS) at two separate places in Burdur and Osmaniye cities, Turkey. The key contribution of this new model is the ability to perform wind power forecasting studies, without needing wind speed data, with high accuracy and rapid solutions. Also, wind power forecasting studies with high accuracy have been carried out despite the height differences between the sensors. That is, for WMS-1 and WMS-2, it has succeeded the wind power forecasting at 61 m and 60.3 m using temperature (3 m), humidity (3 m) and pressure (3.5 m) data. The performance results were presented in tables and graphs.

中文翻译：

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绿色能源研究：使用稳健混合元启发式模型预测更清洁环境的风力发电。

风是一种随机和间歇性的可再生能源。由于其性质，预测风力发电非常困难。准确的风能预测可以鼓励和激励投资者了解全球变暖导致的未来不确定性。因此，二氧化碳和其他对环境有害的温室气体（GHG）不会释放到大气中，同时产生电能。本文提出了一种基于真实气象站的统计和数学数据的新型精确、快速和强大的混合元启发式风能预测方法。该模型被开发为基于人工神经网络 (ANN)、粒子群优化 (PSO) 和径向运动优化 (RMO) 的混合元启发式算法。实时风数据来自土耳其布尔杜尔市和奥斯曼尼耶市两个不同地点的测风站 (WMS)。这种新模型的主要贡献是能够在不需要风速数据的情况下以高精度和快速的解决方案进行风能预测研究。此外，尽管传感器之间存在高度差异，但已经进行了高精度的风电预测研究。也就是说，对于WMS-1和WMS-2，它已经成功地利用温度（3 m）、湿度（3 m）和压力（3.5 m）数据进行了61 m和60.3 m的风电预测。性能结果以表格和图表的形式呈现。具有高精度和快速的解决方案。此外，尽管传感器之间存在高度差异，但已经进行了高精度的风电预测研究。也就是说，对于WMS-1和WMS-2，它已经成功地利用温度（3 m）、湿度（3 m）和压力（3.5 m）数据进行了61 m和60.3 m的风电预测。性能结果以表格和图表的形式呈现。具有高精度和快速的解决方案。此外，尽管传感器之间存在高度差异，但已经进行了高精度的风电预测研究。也就是说，对于WMS-1和WMS-2，它已经成功地利用温度（3 m）、湿度（3 m）和压力（3.5 m）数据进行了61 m和60.3 m的风电预测。性能结果以表格和图表的形式呈现。