基于虚拟相似日与DA-LSTPNet的地区电网短期负荷预测

针对短期负荷预测精细化的需求,提出一种基于虚拟相似日与双阶段注意力机制的长短期时序神经网络(DA-LSTPNet)的地区级短期负荷预测方法.为获得与负荷相匹配的细粒度实时气象数据,首先基于粗粒度的气象数据,利用灰色关联度和关联度加权法获取含细粒度气象数据的气象虚拟相似日.然后,采用最大信息系数(MIC)对气象特征信息与负荷进行非线性关联性分析,构建MIC加权下的负荷虚拟相似日选取算法,解决选取历史日作为传统负荷相似日而导致的过分局部相似乃至非相似的问题.最后,针对过往预测模型缺乏考虑特征因素与局部负荷细粒度变化之间联系特性的问题,构建能够有效挖掘负荷特征数据长期宏观以及短期局部变化特性的DA-LSTPNet进行日前短期负荷预测.以中国南方某地区电网实际负荷数据为例,采用多种形式的仿真验证了所提预测方法具有更高的预测精度和普适性.

Short-term Load Forecasting for Regional Power Grid Based on Virtual Similar Days and Dual-stage Attention-based Long and Short Time Pattern Network

A regional short-term load forecasting method based on virtual similar days and dual-stage attention-based long and short time pattern network (DA-LSTPNet) is proposed for the demand of the refinement of short-term load forecasting. To obtain the fine-grained real-time meteorological data matching the load, a meteorological virtual similar day containing fine-grained meteorological data is firstly obtained using gray correlation and correlation weighting method based on the coarse-grained meteorological data. Then, the maximum information coefficient (MIC) is used to analyze the nonlinear correlation between meteorological feature information and load. And the MIC-weighted selection algorithm for the load virtual similar day is constructed to solve the problem of excessive local similarity or even non-similarity caused by selecting historical days as traditional load similar days. Finally, in order to address the problem that the relationship between characteristic factors and local load fine-grained variation, the DA-LSTPNet is constructed to effectively explore the characteristics of long-term macroscopic and short-term local variation of load feature data for the day-ahead short-term load forecasting. Based on the actual load data of the power grid in a certain area of southern China, the various forms of simulation are used to demonstrate the higher prediction accuracy and universality of the proposed forecasting method.