能源互联环境下考虑需求响应的区域电网短期负荷预测

在区域能源互联系统中,需求响应改变了电力用户的常规用电习惯,增加了预测环境的不确定因素。针对这种特定的环境,提出一种考虑需求响应的区域电网短期负荷预测方法来满足企业对预测精度的需求,该方法通过依次构建数据处理模型、负荷预测模型和误差预测模型实现了预测精度的提升。更具体地,针对历史数据样本集,采用灰色关联分析法处理气象数据以获取输入预测模型的相似日特征变量;针对电力负荷预测,建立了长短期记忆网络模型,利用其特殊的门结构选择性地控制输入变量对模型参数的影响,从而改善了模型的预测性能;针对误差数据样本集,采用了动态模式分解技术来挖掘误差数据的潜在价值,并利用其数据驱动特性刻画了误差时间序列的趋势变化特征,实现了良好的误差预测。最后,结合实际的电网数据,对比验证了所提方法的有效性和优越性。

Short-term Load Forecasting of Regional Power Grid Considering Demand Response in Energy Interconnection Environment

In the regional energy interconnection system, the demand response has changed the conventional power consumption habits of power users, and increased the uncertainties in forecasting environment. In view of this specific environment, this paper proposes a short-term load forecasting method of regional power grids considering demand response to meet the demand of enterprises for forecasting accuracy. This method improves the forecasting accuracy by constructing data processing model, load forecasting model and error forecasting model in turn. More specifically, for the historical data sample set, the gray relation analysis method is used to process the meteorological data to obtain the similar daily characteristic variables of the input forecasting model. For the power load forecasting, a long short-term memory network model is established. Its special gate structure is used to selectively control the influence of input variables on model parameters, and the forecasting performance of the model is improved. For the error data sample set, the dynamic modal decomposition technology is used to mine the potential value of the error data. Its data-driven characteristics are used to characterize the variation trend characteristics of the error series, and a good error prediction is achieved. Finally, combined with the actual power grid data, the effectiveness and superiority of the proposed method are verified.