# An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting

arXiv: https://arxiv.org/abs/1705.04378 · HTML: https://mlsanity.com/paper/1705.04378/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2017.

## Built on by (most cited first, 12 of 12)

- [Recurrent Neural Networks for Dynamical Systems: Applications to Ordinary Differential Equations, Collective Motion, and Hydrological Modeling](https://mlsanity.com/paper/2202.07022/) (2022)
- [Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case](https://mlsanity.com/paper/2203.07080/) (2022)
- [An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models](https://mlsanity.com/paper/2203.16290/) (2022)
- [Appliance Level Short-term Load Forecasting via Recurrent Neural Network](https://mlsanity.com/paper/2111.11998/) (2021)
- [Deep Long-Short Term Memory networks: Stability properties and Experimental validation](https://mlsanity.com/paper/2304.02975/) (2023)
- [Modeling Nonlinear Oscillator Networks Using Physics-Informed Hybrid Reservoir Computing](https://mlsanity.com/paper/2411.05867/) (2024)
- [RNN(p) for Power Consumption Forecasting](https://mlsanity.com/paper/2209.01378/) (2022)
- [Leveraging Non-Decimated Wavelet Packet Features and Transformer Models for Time Series Forecasting](https://mlsanity.com/paper/2403.08630/) (2024)
- [Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey](https://mlsanity.com/paper/2409.09106/) (2024)
- [Accident Impact Prediction based on a deep convolutional and recurrent neural network model](https://mlsanity.com/paper/2411.07537/) (2024)
- [Time Series Forecasting Using a Hybrid Deep Learning Method: A Bi-LSTM Embedding Denoising Auto Encoder Transformer](https://mlsanity.com/paper/2509.17165/) (2025)
- [Fundamentals of Regression](https://mlsanity.com/paper/2512.01920/) (2025)

## Abstract (arXiv)

The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementation of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. In this paper we perform a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. We test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. We provide a general overview of the most important architectures and we define guidelines for configuring the recurrent networks to predict real-valued time series.