考虑分时段负荷行为的短期负荷预测方法

Short?term load forecasting method considering load behavior in different periods

中文关键词: 负荷预测  分时段  长短时记忆网络模型  行为相似度

英文关键词: load forecast  time period  long short?term memory network model  behavior similarity

中文摘要:

      随着电力大数据时代的到来，对电力负荷预测的精度提出了更高的要求，准确的电力负荷预测对电力系统安全稳定运行，降低成本开销具有重要意义。针对短期电力负荷在不同时间段下呈现不同的负荷运行规律的特点，在日范围内计算不同的时间段的负荷行为相似度。在考虑天气维度和时间维度的基础上，增加考虑行为维度，将不同时间段的行为相似度因素引入长短时记忆网络模型，根据历史负荷数据对未来的负荷数据进行预测。通过实验仿真证明，考虑不同时间段的负荷行为特点可以有效提高负荷预测的准确度。

英文摘要:

      With the advent of the era of big data in power,higher requirements have been placed on the accuracy of power load forecasting. Accurate power load forecasting is of great significance for the safe and stable operation of power systems and reducing costs. Aiming at the characteristics of short term power load showing different load operation rules in different time periods, the similarity of load behavior in different time periods is calculated in the daily range. Based on the consideration of the weather dimension and the time dimension, the behavior dimension is added. The behavior similarity factors of different time periods are introduced into the long short term memory network model, and the future load data is predicted based on the historical load data. Experimental simulation proves that considering the characteristics of load behavior in different time periods can effectively improve the accuracy of load prediction.