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LfEdNet A Task-based Day-ahead Load Forecasting Model for Stochastic Economic Dispatch

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Document pages: 8 pages

Abstract: Load forecasting is one of the most important and studied topics in modernpower systems. Most of the existing researches on day-ahead load forecastingtry to build a good model to improve the forecasting accuracy. The forecastedload is then used as the input to generation scheduling with the ultimate goalof minimizing the cost of generation schedules. However, existing day-aheadload forecasting models do not consider this ultimate goal at thetraining forecasting stage. This paper proposes a task-based day-ahead loadforecasting model labeled as LfEdNet that combines two individual layers in onemodel, including a load forecasting layer based on deep neural network (Lflayer) and a day-ahead stochastic economic dispatch (SED) layer (Ed layer). Thetraining of LfEdNet aims to minimize the cost of the day-ahead SED in the Edlayer by updating the parameters of the Lf layer. Sequential quadraticprogramming (SQP) is used to solve the day-ahead SED in the Ed layer. The testresults demonstrate that the forecasted results produced by LfEdNet can lead tolower cost of day-ahead SED while maintaining a relatively high forecastingaccuracy.

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