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PowerNet: a smart energy forecasting architecture based on neural networks
2020
IET Smart Cities
Electricity demand forecasting is a critical task for efficient, reliable and economical operation of the power grid, which is one of the most essential building blocks of smart cities. Accurate forecasting allows grid operators to properly 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
doi:10.1049/iet-smc.2020.0003
fatcat:u7wo6jl5u5djrncx6nkphar5im