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

TL;DR

This paper introduces a deep neural network model called N-BEATS for mid-term electricity load forecasting, demonstrating superior accuracy and bias reduction over traditional and machine learning methods on European demand data.

Contribution

The paper presents a novel neural network architecture that effectively models non-linear, stochastic time series with trends and seasonality for electricity load forecasting.

Findings

Outperforms ten baseline methods in accuracy.

Reduces forecast bias effectively.

Applicable to European monthly electricity demand data.

Abstract

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.