# A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

arXiv: https://arxiv.org/abs/2302.12168 · HTML: https://mlsanity.com/paper/2302.12168/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 74 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [The Shapley Value in Machine Learning](https://mlsanity.com/paper/2202.05594/) (2022)
- [Darts: User-Friendly Modern Machine Learning for Time Series](https://mlsanity.com/paper/2110.03224/) (2021)
- [Deep Learning for Time Series Forecasting: The Electric Load Case](https://mlsanity.com/paper/1907.09207/) (2019)
- [N-BEATS neural network for mid-term electricity load forecasting](https://mlsanity.com/paper/2009.11961/) (2020)
- [A Global Modeling Approach for Load Forecasting in Distribution Networks](https://mlsanity.com/paper/2204.00493/) (2022)

## Built on by (most cited first, 6 of 6)

- [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)
- [Integrating the Expected Future in Load Forecasts with Contextually Enhanced Transformer Models](https://mlsanity.com/paper/2409.05884/) (2024)

## Abstract (arXiv)

Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets. However, the non-linearity and non-stationarity of electricity demand as well as its dependency on various external factors renders STLF a challenging task. To that end, several deep learning models have been proposed in the literature for STLF, reporting promising results. In order to evaluate the accuracy of said models in day-ahead forecasting settings, in this paper we focus on the national net aggregated STLF of Portugal and conduct a comparative study considering a set of indicative, well-established deep autoregressive models, namely multi-layer perceptrons (MLP), long short-term memory networks (LSTM), neural basis expansion coefficient analysis (N-BEATS), temporal convolutional networks (TCN), and temporal fusion transformers (TFT). Moreover, we identify factors that significantly affect the demand and investigate their impact on the accuracy of each model. Our results suggest that N-BEATS consistently outperforms the rest of the examined models. MLP follows, providing further evidence towards the use of feed-forward networks over relatively more sophisticated architectures. Finally, certain calendar and weather features like the hour of the day and the temperature are identified as key accuracy drivers, providing insights regarding the forecasting approach that should be used per case.