Short-term Wind Energy Forecasting with Advanced Recurrent Neural Network Models: A Comparative Study

Mindaugas Jankauskas, A. Serackis, Raimondas Pomornacki, Van Khang Hyunh, Martynas Šapurov, A. Baskys
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Abstract

For effective energy management and the integration of renewable energy sources into the grid, accurate wind power forecasting is crucial. The objective of this work is to construct and assess a variety of machine learning models to forecast the generation of electricity from wind farms using meteorological data, such as wind speed, wind direction, air temperature, humidity, and atmospheric pressure. We tested six models, of which three were based on a recurrent neural network approach. The most pertinent elements for precise predictions were found using feature selection algorithms after preprocessing the meteorological and wind farm power generating data. The results of the study indicate that the best option to predict the generation of electricity from wind farms using meteorological data is the model based on bidirectional LSTM. The study also emphasizes how crucial feature selection and hyperparameter adjustment are for enhancing model performance. Future work may investigate different machine learning models, include more features or data sources, and evaluate how well the models can be used in energy management systems in the real world.
先进递归神经网络模型短期风能预测的比较研究
为了实现有效的能源管理和可再生能源并网,准确的风电预测至关重要。这项工作的目标是构建和评估各种机器学习模型,以使用气象数据(如风速、风向、空气温度、湿度和大气压)预测风电场的发电量。我们测试了六个模型,其中三个基于递归神经网络方法。在对气象和风力发电数据进行预处理后,利用特征选择算法找到了最相关的精确预测元素。研究结果表明,利用气象数据预测风电场发电量的最佳选择是基于双向LSTM的模型。研究还强调了特征选择和超参数调整对于提高模型性能的重要性。未来的工作可能会研究不同的机器学习模型,包括更多的特征或数据源,并评估这些模型在现实世界中的能源管理系统中的应用效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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