.png){kind=icon}
返回
Short-Term Wind Power Prediction Based on a Modified Stacking Ensemble Learning Algorithm
.png){kind=icon}
OA
AI
DOI：10.3390/su16145960.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A high proportion of new energy has become a prominent feature of modern power systems. Due to the intermittency, volatility, and strong randomness in wind power generation, an accurate and reliable method for the prediction of wind power is required. This paper proposes a modified stacking ensemble learning method for short-term wind power predictions to reduce error and improve the generalization performance of traditional single networks in tackling the randomness of wind power. Firstly, the base learners including tree-based models and neural networks are improved based on the Bagging and Boosting algorithms, and a method for determining internal parameters and iterations is provided. Secondly, the linear integration and stacking integration models are combined to obtain deterministic prediction results. Since the modified stacking meta learner can change the weight, it will enhance the strengths of the base learners and optimize the integration of the model prediction to fit the second layer prediction, compared to traditional linear integration models. Finally, a numerical experiment showed that the modified stacking ensemble model had a decrease in MAPE from about 8.3% to 7.5% (an absolute decrease of 0.8%) compared to a single learner for the 15 min look-ahead tests. Changing variables such as the season and predicting the look-ahead time showed satisfactory improvement effects under all the evaluation criteria, and the superiority of the modified stacking ensemble learning method proposed in this paper regarding short-term wind power prediction performance was validated.
Keyword:
wind power prediction
stacking
ensemble learning
bagging and boosting algorithms
fusion models
期刊
IF:
3.3
论文数:
10.7W
被引数:
28.4W
机构
引用论文
Analgesic activities of ethanolic extract of the root of Carpolobia luteaCarpolobia lutea根乙醇提取物的镇痛活性
OAAI
OAAI分享收藏Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model基于改进Stacking集成算法的lstm-informer模型多时间尺度光伏功率预测.png)
ENERGY
ENERGYIF9.4
PREAI
分享收藏An Adaptive Ensemble Data Driven Approach for Nonparametric Probabilistic Forecasting of Electricity Load一种自适应集成数据驱动的电力负荷非参数概率预测方法OAAI
OAAI分享收藏Short-Term Interval Prediction of Wind Power Based on KELM and a Universal Tabu Search Algorithm基于KELM和通用禁忌搜索算法的风电功率短期区间预测SUSTAINABILITYOAAI
SUSTAINABILITYIF3.3
OAAI分享收藏Data-Driven Correction Approach to Refine Power Curve of Wind Farm Under Wind Curtailment弃风条件下风电场功率曲线精化的数据驱动修正方法
PREAI
分享收藏A new boosting algorithm for improved time-series forecasting with recurrent neural networksINFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏
查看更多内容