  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.
