.png){kind=icon}
返回
Multistep power load forecasting using iterative neural network-based prediction intervals
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2024.109518.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Our study investigates the multistep forecasting problem under power load fluctuation. This paper proposes a multistep prediction interval (PI) construction approach that is developed using an iterative neural network (NN) to handle data uncertainty. First, a new data processing method is extended that combines variational modal decomposition (VMD) and partial autocorrelation analysis (PACF). The iterative lower upper bound estimation (ILUBE) model is developed that uses an NN with interval input, output, and feedback iteration. It is necessary to formulate a new performance index and adapt it to the requirements of multistep PIs by introducing a prediction horizon factor and a prediction deviation value. Finally, a particle swarm optimization (PSO) algorithm is employed to search for the optimal parameters of the ILUBE. Comparative experiments are carried out using the power load data from two regions in a case study. The results indicate that the proposed approach increases the quality of PIs and the prediction horizon.
Keyword:
Power load
Multistep forecasting
Prediction Interval (PI)
Neural Network (NN)
Iterative network
Uncertainty
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines基于支持向量回归机的短期负荷预测策略
PREAI
分享收藏The short-term interval prediction of wind power using the deep learning model with gradient descend optimization.png)
RENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏
分享收藏An Evolutionary Multiobjective Knee-Based Lower Upper Bound Estimation Method for Wind Speed Interval Forecast基于Knee的进化多目标风速区间下界估计方法
PREAI
分享收藏Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications基于神经网络的不确定性量化: 方法与应用综述IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday StructureOAAI
OAAI分享收藏
查看更多内容