Quantum mechanics

Engineering

Statistics

Probabilistic forecasting

Time series

Probabilistic logic

Economics

Power (physics)

Computer science

Geology

Oceanography

Physics

Operations research

Mean absolute percentage error

Mathematics

Electric power system

Econometrics

Artificial intelligence

Electricity market

Demand forecasting

Term (time)

Artificial neural network

Mean squared error

Electricity

Machine learning

Baseline (sea)

Electrical engineering

Applied Energy

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.

N-BEATS neural network for mid-term electricity load forecasting