短期电力负荷预测的时间序列数据深度挖掘模型设计

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一致，对短期电力负荷预测的精确度较高。

Design of time series data deep mining model for short term power load forecasting

Short term power load forecasting data time series disorder phenomenon，resulting in low accuracy of short-term power load forecasting.Therefore，a time series data deep mining model for short-term power load forecasting is proposed.Design data preprocessing power data warehouse system，obtain power data，and sort power data.Based on the results of data processing，the time series of data is divided，and the deep mining model of time series data is established to predict short-term power load.The experimental results show that the short-term power load is predicted by collecting the power information of the same power bureau in the same area.The predicted short-term power load is consistent with the actual short-term power load，and the accuracy of short-term power load prediction is high.