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Power demand forecasting is a critical task for achieving efficiency andreliability in power grid operation. Accurate forecasting allows grid operatorsto better maintain the balance of supply and demand as well as to optimizeoperational cost for generation and transmission. This article proposes a novelneural network architecture PowerNet, which can incorporate multipleheterogeneous features, such as historical energy consumption data, weatherdata, and calendar information, for the power demand forecasting task. Comparedto two recent works based on Gradient Boosting Tree (GBT) and Support VectorRegression (SVR), PowerNet demonstrates a decrease of 33.3% and 14.3% inforecasting error, respectively. We further provide empirical results the twooperational considerations that are crucial when using PowerNet in practice,i.e., how far in the future the model can forecast with a decent accuracy andhow often we should re-train the forecasting model to retain its modelingcapability. Finally, we briefly discuss a multilayer anomaly detection approachbased on PowerNet.

PowerNet: Neural Power Demand Forecasting in Smart Grid