A DEEP LEARNING LOAD FORECASTING METHOD BASED ON LOAD TYPE RECOGNITION

机译：基于负载类型识别的深度学习负载预测方法

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In this paper, a short-term load forecasting model based on load type recognition is established. According to the characteristics of electric power load, the load data can be divided into three types: peak load, valley load and normal load. During the forecasting process, the support vector machine recognition model is used to determine load type on the predicted time point. After then, load samples of the same type are selected as the training sample to establish deep learning forecasting model. Finally, the trained model is applied to forecasting the load value. The experimental comparison shows that its prediction accuracy is higher than that of other models.

机译：本文建立了一种基于负载类型识别的短期负荷预测模型。根据电力负荷的特点，负载数据可分为三种类型：峰值负荷，谷负载和正常负载。在预测过程中，支持向量机识别模型用于确定预测的时间点上的负载类型。然后，选择相同类型的加载样本作为培训样本，以建立深度学习预测模型。最后，培训的模型应用于预测负载值。实验比较表明，其预测精度高于其他模型的预测精度。

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《International Conference on Machine Learning and Cybernetics》|2018年|336p|共5页
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作者
JINGJIE YANG; QIANG WANG;
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中图分类 TP181-53;
关键词
Short term load forecasting; Support vector machine; Deep belief network (DBN); Load type recognition; Deep learning;

机译：短期负荷预测;支持向量机;深度信仰网络（DBN）;负载类型识别;深入学习;

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A DEEP LEARNING LOAD FORECASTING METHOD BASED ON LOAD TYPE RECOGNITION

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