# In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance

arXiv: https://arxiv.org/abs/2302.13046 · HTML: https://mlsanity.com/paper/2302.13046/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 57 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Out of Distribution Generalization in Machine Learning](https://mlsanity.com/paper/2103.02667/) (2021)
- [Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France](https://mlsanity.com/paper/2009.06527/) (2020)
- [N-BEATS neural network for mid-term electricity load forecasting](https://mlsanity.com/paper/2009.11961/) (2020)

## Built on by (most cited first, 5 of 5)

- [Targeted demand response for flexible energy communities using clustering techniques](https://mlsanity.com/paper/2303.00186/) (2023)
- [Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series](https://mlsanity.com/paper/2310.15555/) (2023)
- [DeepTSF: Codeless machine learning operations for time series forecasting](https://mlsanity.com/paper/2308.00709/) (2023)
- [Data-driven building energy efficiency prediction using physics-informed neural networks](https://mlsanity.com/paper/2311.08035/) (2023)
- [Trustworthy artificial intelligence in the energy sector: Landscape analysis and evaluation framework](https://mlsanity.com/paper/2412.07782/) (2024)

## Abstract (arXiv)

In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market. STLF is a challenging task, due to the complex demand of active and reactive power from multiple types of electrical loads and their dependence on numerous exogenous variables. Amongst them, special circumstances, such as the COVID-19 pandemic, can often be the reason behind distribution shifts of load series. This work conducts a comparative study of Deep Learning (DL) architectures, namely Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN), with respect to forecasting accuracy and training sustainability, meanwhile examining their out-of-distribution generalization capabilities during the COVID-19 pandemic era. A Pattern Sequence Forecasting (PSF) model is used as baseline. The case study focuses on day-ahead forecasts for the Portuguese national 15-minute resolution net load time series. The results can be leveraged by energy companies and network operators (i) to reinforce their forecasting toolkit with state-of-the-art DL models; (ii) to become aware of the serious consequences of crisis events on model performance; (iii) as a high-level model evaluation, deployment, and sustainability guide within a smart grid context.