Improved short-term load forecasting using bagged neural networks

Khwaja A. S.; Naeem M.; Anpalagan A.; Venetsanopoulos A.; Venkatesh B.

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Improved short-term load forecasting using bagged neural networks

机译：使用袋装神经网络改进短期负荷预测

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In this paper we present improved short-term load forecasting using bagged neural networks (BNNs). The BNNs consist of creating multiple sets of data by sampling randomly with replacement, training a neural network on each data set, and averaging the results obtained from each trained neural network. The bagging process reduces estimation errors and variation range of errors compared to using a single neural network for load forecasting. Examples with real data show the effectiveness of our proposed techniques by demonstrating that using BNNs can reduce load forecasting errors, compared to various existing techniques. (C) 2015 Elsevier B.V. All rights reserved.

机译：在本文中，我们介绍了使用袋装神经网络（BNN）的改进的短期负荷预测。 BNN包括通过替换随机抽样来创建多组数据，在每个数据集上训练神经网络，以及平均从每个训练后的神经网络获得的结果。与使用单个神经网络进行负荷预测相比，套袋过程减少了估计误差和误差变化范围。带有实际数据的示例通过证明与各种现有技术相比，使用BNN可以减少负荷预测误差，从而展示了我们提出的技术的有效性。（C）2015 Elsevier B.V.保留所有权利。

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来源
《Electric power systems research》 |2015年第8期|109-115|共7页
作者
Khwaja A. S.; Naeem M.; Anpalagan A.; Venetsanopoulos A.; Venkatesh B.;
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作者单位

Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada;

Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada;

Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada;

Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada;

Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Smart grid; Short-term load forecasting; Non-linear regression; Artificial neural networks; Bagging; Power systems;

机译：智能电网;短期负荷预测;非线性回归;人工神经网络;装袋;电力系统;

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1. Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting [J] . Khwaja A. S., Anpalagan A., Naeem M., Electric power systems research . 2020,第Feba期

机译：联合袋装增强人工神经网络：使用集成机器学习来改善短期电力负荷预测
2. Design of artificial neural networks for short-term load forecasting. II. Multilayer feedforward networks for peak load and valley load forecasting [J] . Yuan-Yih Hsu, Chien-Chuen Yang IEE proceedings. Part C . 1991,第5期

机译：短期负荷预测的人工神经网络设计。二。多层前馈网络，用于预测峰值负荷和谷底负荷
3. Boosted neural networks for improved short-term electric load forecasting [J] . Khwaja A. S., Zhang X., Anpalagan A., Electric power systems research . 2017,第Feba期

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4. An improved fuzzy neural networks approach for short-term electrical load forecasting [C] . Liao Gwo-Ching 8th Asian Control Conference, 2011 . 2011

机译：一种改进的模糊神经网络短期电力负荷预测方法
5. Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Multivariable Inputs [D] . Buitrago, Jaime H. 2017

机译：使用带有外源多变量输入的非线性自回归人工神经网络进行电力负荷的短期预测
6. Improved Neural Networks with Random Weights for Short-Term Load Forecasting [O] . Kun Lang, Mingyuan Zhang, Yongbo Yuan 2010

机译：改进的具有随机权重的神经网络，用于短期负荷预测
7. A Hybrid Short-Term Load Forecasting Model Based on Improved Fuzzy C-Means Clustering, Random Forest and Deep Neural Networks [O] . Fu Liu, Tian Dong, Tao Hou, 2021

机译：一种基于改进模糊C型聚类，随机林和深神经网络的混合短期负荷预测模型
8. Short-term electric load forecasting using neural networks [R] . Daugherty, E. , Bartlett, E. 1993

机译：使用神经网络进行短期电力负荷预测

Improved short-term load forecasting using bagged neural networks

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