%0 Journal Article %T 基于RNN模型的新能源发电功率预测研究
Research on Power Prediction of New Energy Power Generation Based on RNN Model %A 范彤昕 %A 全洁 %A 曾美洁 %A 杨文卓 %J Modeling and Simulation %P 379-387 %@ 2324-870X %D 2025 %I Hans Publishing %R 10.12677/mos.2025.141036 %X 随着全球对可再生能源需求的不断增长，准确预测新能源发电功率对于提升电网稳定性、优化资源配置及推动绿色能源发展至关重要。然而，新能源发电功率受多种复杂因素影响，传统预测方法难以达到高精度。因此，本文旨在利用循环神经网络(RNN)模型在处理时间序列数据上的优势，设计并实现一个能够高效预测新能源发电功率的模型。首先进行数据预处理工作，对数据清洗、归一化和特征提取。随后，确定RNN模型的隐藏层数、神经元数量以及激活函数等关键参数。实验结果表明通过调整RNN模型的复杂度和训练轮数，找到最优参数，降低loss值，使模型预测值与真实值达到较好的拟合效果，以提高新能源发电功率预测精度。本研究表明，RNN模型在新能源发电功率预测上具有高精度和稳定性，对推动能源结构转型和可持续发展具有重要意义。
As the global demand for renewable energy continues to grow, accurate prediction of new energy generation power is crucial to improve grid stability, optimize resource allocation and promote the development of green energy. However, the power of new energy generation is affected by many complicated factors, and the traditional forecasting method is difficult to achieve high precision. Therefore, this paper aims to use the advantages of recurrent neural network (RNN) model in processing time series data to design and implement a model that can efficiently predict the power of new energy generation. Firstly, data preprocessing is carried out, including data cleaning, normalization and feature extraction. Then, the key parameters of the RNN model such as the number of hidden layers, the number of neurons and the activation function are determined. This study shows that by adjusting the complexity of the RNN model and the number of training rounds, the optimal parameters can be found, the loss value can be reduced, and the predicted value of the model can achieve a better fitting effect with the real value, so as to improve the power prediction accuracy of new energy generation. This study shows that RNN model has high accuracy and stability in new energy power generation prediction, which is of great significance in promoting energy structure transformation and sustainable development. %K RNN模型， %K 新能源， %K 发电功率预测
RNN Model %K New Energy %K Power Generation Forecasting %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=104971