Department of Electronic and Power Engineering, National University of Sciences and Technology, Karachi, Pakistan.
PLoS One. 2024 May 31;19(5):e0302664. doi: 10.1371/journal.pone.0302664. eCollection 2024.
The ever-increasing demand for electricity has presented a grave threat to traditional energy sources, which are finite, rapidly depleting, and have a detrimental environmental impact. These shortcomings of conventional energy resources have caused the globe to switch from traditional to renewable energy sources. Wind power significantly contributes to carbon-free energy because it is widely accessible, inexpensive, and produces no harmful emissions. Better and more efficient renewable wind power production relies on accurate wind speed predictions. Accurate short-term wind speed forecasting is essential for effectively handling unsteady wind power generation and ensuring that wind turbines operate safely. The significant stochastic nature of the wind speed and its dynamic unpredictability makes it difficult to forecast. This paper develops a hybrid model, L-LG-S, for precise short-term wind speed forecasting to address problems in wind speed forecasting. In this research, state-of-the-art machine learning and deep learning algorithms employed in wind speed forecasting are compared with the proposed approach. The effectiveness of the proposed hybrid model is tested using real-world wind speed data from a wind turbine located in the city of Karachi, Pakistan. Moreover, the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are used as accuracy evaluation indices. Experimental results show that the proposed model outperforms the state-of-the-art legacy models in terms of accuracy for short-term wind speed in training, validation and test predictions by 98% respectively.
不断增长的电力需求对传统能源构成了严重威胁，传统能源有限、消耗迅速且对环境有不利影响。这些传统能源的缺点促使全球从传统能源向可再生能源转变。风力发电对无碳能源的贡献巨大，因为它广泛可用、价格低廉且不会产生有害排放。更好、更高效的可再生风力发电依赖于准确的风速预测。准确的短期风速预测对于有效处理不稳定的风力发电和确保风力涡轮机安全运行至关重要。风速具有显著的随机性质和动态不可预测性，这使得风速预测变得困难。本文开发了一种混合模型 L-LG-S，用于精确的短期风速预测，以解决风速预测中的问题。在这项研究中，将先进的机器学习和深度学习算法应用于风速预测，并与提出的方法进行了比较。使用来自巴基斯坦卡拉奇市风力涡轮机的真实风速数据测试了所提出的混合模型的有效性。此外，还使用均方误差 (MSE)、均方根误差 (RMSE) 和平均绝对误差 (MAE) 作为准确性评估指标。实验结果表明，所提出的模型在训练、验证和测试预测方面的短期风速准确性方面分别优于 98%的最先进的传统模型。
