  Power demand forecasting is a critical task for achieving efficiency and
reliability in power grid operation. Accurate forecasting allows grid operators
to better maintain the balance of supply and demand as well as to optimize
operational cost for generation and transmission. This article proposes a novel
neural network architecture PowerNet, which can incorporate multiple
heterogeneous features, such as historical energy consumption data, weather
data, and calendar information, for the power demand forecasting task. Compared
to two recent works based on Gradient Boosting Tree (GBT) and Support Vector
Regression (SVR), PowerNet demonstrates a decrease of 33.3% and 14.3% in
forecasting error, respectively. We further provide empirical results the two
operational considerations that are crucial when using PowerNet in practice,
i.e., how far in the future the model can forecast with a decent accuracy and
how often we should re-train the forecasting model to retain its modeling
capability. Finally, we briefly discuss a multilayer anomaly detection approach
based on PowerNet.
