Research on Wind Power Prediction Model Based on Random Forest and SVR

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-12 DOI:10.4108/ew.5758

Zehui Wang, Dianwei Chi

引用次数: 0

Abstract

Wind power generation is random and easily affected by external factors. In order to construct an effective prediction model based on wind power generation, a wind power prediction model based on principal component analysis (PCA) noise reduction, feature selection based on random forest model and support vector regression (SVR) algorithm is proposed. First, in the data preprocessing stage, PCA is used for sample data denoising; then the random forest model is used to calculate the importance evaluation value of each feature to optimize the selection of feature parameters; finally, The SVR algorithm is applied for training and prediction. Experiments show that the prediction effect of the model based on random forest and SVR is excellent, the root mean square error(RMSE) is 0.086, the average absolute percentage error(MAPE) is 23.47%, and the coefficient of determination(R2) is 0.991. Compared with the traditional SVR model, the root mean square error of the method proposed in this paper is reduced by 95.9%, and the prediction accuracy and the fit of the prediction curve are significantly improved.

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基于随机森林和 SVR 的风能预测模型研究

风力发电具有随机性，容易受到外部因素的影响。为了构建有效的风力发电量预测模型，本文提出了一种基于主成分分析（PCA）降噪、基于随机森林模型的特征选择和支持向量回归（SVR）算法的风力发电量预测模型。首先，在数据预处理阶段，利用 PCA 对样本数据进行去噪；然后，利用随机森林模型计算每个特征的重要性评估值，优化特征参数的选择；最后，应用 SVR 算法进行训练和预测。实验表明，基于随机森林和 SVR 模型的预测效果非常好，均方根误差（RMSE）为 0.086，平均绝对百分比误差（MAPE）为 23.47%，判定系数（R2）为 0.991。与传统的 SVR 模型相比，本文所提方法的均方根误差降低了 95.9%，预测精度和预测曲线拟合度均有显著提高。

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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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.