.png){kind=icon}
返回
Robust functional regression for wind speed forecasting based on Sparse Bayesian learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.renene.2018.07.083.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate wind speed forecasting is helpful for reducing the instantaneous fluctuation of voltage, and also has great practical significance on power dispatching and plan. There are two problems in wind speed forecasting: loss of details when transforming the high-resolution data into the low-resolution data and outliers existing in our data. So, a sparse Bayesian-based robust functional regression model is proposed in this paper. First, both the low-resolution and high-resolution data are considered as inputs to forecast future wind speed. Specifically, besides the historical 10-min mean wind speed, the corresponding functional variables, constructed by wind speed data recorded every 5 s in each 10-min interval, are also taken as inputs to make 10-min-ahead wind speed forecasting. But, not all functional variables contribute to the accurate wind speed forecasts. So, a multi-Laplace prior is given to the corresponding coefficient functions to get sparse solutions, which can reduce the adverse effects of the redundant functional variables on the final forecasting results. Second, a multi-mixture of Gaussians prior is assumed for the forecasting error to enhance the robustness of the forecasting model. Results of spatial temporal and multi-step-ahead wind speed forecasting show that the proposed model provides more accurate forecasts than the compared models. (C) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Robustness
Sparsity
Functional regression
Variational Bayesian
Wind speed forecasting
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.1
论文数:
2.6W
被引数:
12.1W
机构
引用论文
A hybrid forecasting model based on outlier detection and fuzzy time series - A case study on Hainan wind farm of China.png)
ENERGY
ENERGYIF9.4
PREAI
分享收藏Stochastic complexities of general mixture models in variational Bayesian learningNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏分享收藏Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China利用混合模型对中国新疆三个不同地点进行中期风速预测RENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏Influence of local wind speed and direction on wind power dynamics - Application to offshore very short-term forecasting当地风速和风向对风电功率动力学的影响-在海上非常短期预测中的应用APPLIED ENERGY
OAAI
APPLIED ENERGYIF11
OAAI分享收藏Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model使用混合arimal-ann模型预测墨西哥三个不同地区的风速RENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏
查看更多内容