  Accurate load prediction is an effective way to reduce power system operation
costs. Traditionally, the mean square error (MSE) is a common-used loss
function to guide the training of an accurate load forecasting model. However,
the MSE loss function is unable to precisely reflect the real costs associated
with forecasting errors because the cost caused by forecasting errors in the
real power system is probably neither symmetric nor quadratic. To tackle this
issue, this paper proposes a generalized cost-oriented load forecasting
framework. Specifically, how to obtain a differentiable loss function that
reflects real cost and how to integrate the loss function with regression
models are studied. The economy and effectiveness of the proposed load
forecasting method are verified by the case studies of an optimal dispatch
problem that is built on the IEEE 30-bus system and the open load dataset from
the Global Energy Forecasting Competition 2012 (GEFCom2012).
