论文标题
LFEDNET：一种基于任务的日期负载预测模型，用于随机经济派遣
LfEdNet: A Task-based Day-ahead Load Forecasting Model for Stochastic Economic Dispatch
论文作者
论文摘要
负载预测是现代电力系统中最重要和研究的主题之一。大多数现有关于日益负载预测的研究试图建立一个良好的模型以提高预测准确性。然后，预测负载被用作生成计划的输入，其最终目标是最大程度地减少生成计划的成本。但是，现有的日期负载预测模型并未考虑在培训/预测阶段的最终目标。本文提出了一个基于任务的日期负载预测模型，该预测模型标记为LFEDNET，该模型将两个单独的层结合在一个模型中，包括基于深神经网络（LF层）和一个日前的随机经济调度（SED）层（ED层）的负载预测层。 LFEDNET的培训旨在通过更新LF层的参数来最大程度地降低ED层中日前的成本。顺序二次编程（SQP）用于求解ED层中的日前SED。测试结果表明，LFEDNET产生的预测结果可导致较低的日期成本，同时保持相对较高的预测准确性。
Load forecasting is one of the most important and studied topics in modern power systems. Most of the existing researches on day-ahead load forecasting try to build a good model to improve the forecasting accuracy. The forecasted load is then used as the input to generation scheduling with the ultimate goal of minimizing the cost of generation schedules. However, existing day-ahead load forecasting models do not consider this ultimate goal at the training/forecasting stage. This paper proposes a task-based day-ahead load forecasting model labeled as LfEdNet that combines two individual layers in one model, including a load forecasting layer based on deep neural network (Lf layer) and a day-ahead stochastic economic dispatch (SED) layer (Ed layer). The training of LfEdNet aims to minimize the cost of the day-ahead SED in the Ed layer by updating the parameters of the Lf layer. Sequential quadratic programming (SQP) is used to solve the day-ahead SED in the Ed layer. The test results demonstrate that the forecasted results produced by LfEdNet can lead to lower cost of day-ahead SED while maintaining a relatively high forecasting accuracy.
