| 用于短期电力负荷预测的时间序列数据深度挖掘模型 |
投稿时间：2020-08-04 修订日期：2020-11-18  点此下载全文 |
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| 中文摘要:短期电力负荷预测存在数据时间序列紊乱现象,导致预测短期电力负荷精确度低,为此提出用于短期电力负荷预测的时间序列数据深度挖掘模型。设计数据预处理电力数据仓库体系,获取电力数据,并对电力数据进行排序处理；基于数据处理结果,划分数据时间序列,建立时间序列数据深度挖掘模型,预测短期电力负荷。实验结果显示,采集同一区域的同一电力局电力信息,对短期电力负荷进行预测,预测短期电力负荷功率与实际短期电力负荷功率一致,对短期电力负荷预测的精确度较高。 |
| 中文关键词:短期电力负荷 预测 时间序列 数据深度挖掘 |
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| Time series data mining model for short term load forecasting |
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| Abstract:The data time series of short-term load forecasting is disordered, which leads to the low accuracy of short-term power load forecasting. Therefore, a time series data deep mining model for short-term power load forecasting is proposed. The data preprocessing power data warehouse system is designed to obtain the power data and sort the power data; based on the data processing results, the data time series are divided, and the time series data deep mining model is established to predict the short-term power load. The experimental results show that the power information of the same power bureau in the same area is collected to forecast the short-term power load. The predicted short-term power load power is consistent with the actual short-term power load power, and the accuracy of short-term power load forecasting is high. |
| keywords:short term power load forecasting time series data deep mining |
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