  Data privacy and security have become a non-negligible factor in load
forecasting. Previous researches mainly focus on training stage enhancement.
However, once the model is trained and deployed, it may need to `forget' (i.e.,
remove the impact of) part of training data if the these data are found to be
malicious or as requested by the data owner. This paper introduces the concept
of machine unlearning which is specifically designed to remove the influence of
part of the dataset on an already trained forecaster. However, direct
unlearning inevitably degrades the model generalization ability. To balance
between unlearning completeness and model performance, a performance-aware
algorithm is proposed by evaluating the sensitivity of local model parameter
change using influence function and sample re-weighting. Furthermore, we
observe that the statistical criterion such as mean squared error, cannot fully
reflect the operation cost of the downstream tasks in power system. Therefore,
a task-aware machine unlearning is proposed whose objective is a trilevel
optimization with dispatch and redispatch problems considered. We theoretically
prove the existence of the gradient of such an objective, which is key to
re-weighting the remaining samples. We tested the unlearning algorithms on
linear, CNN, and MLP-Mixer based load forecasters with a realistic load
dataset. The simulation demonstrates the balance between unlearning
completeness and operational cost. All codes can be found at
https://github.com/xuwkk/task_aware_machine_unlearning.
