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

arXiv: https://arxiv.org/abs/2009.11961 · HTML: https://mlsanity.com/paper/2009.11961/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 14 of 14)

- [TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis](https://mlsanity.com/paper/2410.04047/) (2024)
- [A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers](https://mlsanity.com/paper/2302.12168/) (2023)
- [In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance](https://mlsanity.com/paper/2302.13046/) (2023)
- [Continually learning out-of-distribution spatiotemporal data for robust energy forecasting](https://mlsanity.com/paper/2306.06385/) (2023)
- [Interpretable Multivariate Time Series Forecasting Using Neural Fourier Transform](https://mlsanity.com/paper/2405.13812/) (2024)
- [An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition](https://mlsanity.com/paper/2306.10826/) (2023)
- [Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand](https://mlsanity.com/paper/2404.17451/) (2024)
- [A Global Modeling Approach for Load Forecasting in Distribution Networks](https://mlsanity.com/paper/2204.00493/) (2022)
- [Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types](https://mlsanity.com/paper/2502.05104/) (2025)
- [Forecasting Electricity Prices](https://mlsanity.com/paper/2204.11735/) (2022)
- [TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data](https://mlsanity.com/paper/2408.16379/) (2024)
- [Data Model Design for Explainable Machine Learning-based Electricity Applications](https://mlsanity.com/paper/2505.23607/) (2025)
- [TriForecaster: A Mixture of Experts Framework for Multi-Region Electric Load Forecasting with Tri-dimensional Specialization](https://mlsanity.com/paper/2508.09753/) (2025)
- [Physical knowledge on historical data matters more than enforcing physical constraints on the forecast](https://mlsanity.com/paper/2609.19871/) (2026)

## Abstract (arXiv)

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.