  This paper addresses the mid-term electricity load forecasting problem.
Solving this problem is necessary for power system operation and planning as
well as for negotiating forward contracts in deregulated energy markets. We
show that our proposed deep neural network modeling approach based on the deep
neural architecture is effective at solving the mid-term electricity load
forecasting problem. Proposed neural network has high expressive power to solve
non-linear stochastic forecasting problems with time series including trends,
seasonality and significant random fluctuations. At the same time, it is simple
to implement and train, it does not require signal preprocessing, and it is
equipped with a forecast bias reduction mechanism. We compare our approach
against ten baseline methods, including classical statistical methods, machine
learning and hybrid approaches, on 35 monthly electricity demand time series
for European countries. The empirical study shows that proposed neural network
clearly outperforms all competitors in terms of both accuracy and forecast
bias. Code is available here: https://github.com/boreshkinai/nbeats-midterm.
