.png){kind=icon}
返回
Long Short-Term Memory-Based Twin Support Vector Regression for Probabilistic Load Forecasting
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2023.3335355.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A probabilistic load forecast that is accurate and reliable is crucial to not only the efficient operation of power systems but also to the efficient use of energy resources. In order to estimate the uncertainties in forecasting models and nonstationary electric load data, this study proposes a probabilistic load forecasting model, namely BFEEMD-LSTM-TWSVRSOA. This model consists of a data filtering method named fast ensemble empirical model decomposition (FEEMD) method, a twin support vector regression (TWSVR) whose features are extracted by deep learning-based long short-term memory (LSTM) networks, and parameters optimized by seeker optimization algorithms (SOAs). We compared the probabilistic forecasting performance of the BFEEMD-LSTM-TWSVRSOA and its point forecasting version with different machine learning and deep learning algorithms on Global Energy Forecasting Competition 2014 (GEFCom2014). The most representative month data of each season, totally four monthly data, collected from the one-year data in GEFCom2014, forming four datasets. Several bootstrap methods are compared in order to determine the best prediction intervals (PIs) for the proposed model. Various forecasting step sizes are also taken into consideration in order to obtain the best satisfactory point forecasting results. Experimental results on these four datasets indicate that the wild bootstrap method and 24-h step size are the best bootstrap method and forecasting step size for the proposed model. The proposed model achieves averaged 46%, 11%, 36%, and 44% better than suboptimal model on these four datasets with respect to point forecasting, and achieves averaged 53%, 48%, 46%, and 51% better than suboptimal model on these four datasets with respect to probabilistic forecasting.
Keyword:
Bootstrap method
fast ensemble empirical model decomposition (FEEMD)
long short-term memory (LSTM) network
probabilistic load forecasting (PLF)
seeker optimization algorithm (SOA)
twin support vector regression (TWSVR)
期刊
IF:
8.9
论文数:
7.5K
被引数:
7.2W
机构
引用论文
Feature selection for probabilistic load forecasting via sparse penalized quantile regression基于稀疏惩罚分位数回归的概率负荷预测特征选择
OAAI
OAAI分享收藏Rotated Sphere Haar Wavelet and Deep Contractive Auto-Encoder Network With Fuzzy Gaussian SVM for Pilot's Pupil Center Detection旋转球面Haar小波和模糊高斯支持向量机深度压缩自编码网络在飞行员瞳孔中心检测中的应用
PREAI
分享收藏Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination基于分位数回归森林和分位数确定的并行可靠概率负荷预测.png)
ENERGY
ENERGYIF9.4
PREAI
分享收藏
查看更多内容