# Deep Learning for Time Series Forecasting: The Electric Load Case

arXiv: https://arxiv.org/abs/1907.09207 · HTML: https://mlsanity.com/paper/1907.09207/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 30 of 31)

- [Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations](https://mlsanity.com/paper/2106.00006/) (2021)
- [Relational Conformal Prediction for Correlated Time Series](https://mlsanity.com/paper/2502.09443/) (2025)
- [A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers](https://mlsanity.com/paper/2302.12168/) (2023)
- [Time Series Analysis and Modeling to Forecast: a Survey](https://mlsanity.com/paper/2104.00164/) (2021)
- [Learning an Inventory Control Policy with General Inventory Arrival Dynamics](https://mlsanity.com/paper/2310.17168/) (2023)
- [Long Input Sequence Network for Long Time Series Forecasting](https://mlsanity.com/paper/2407.15869/) (2024)
- [Temporal Encoding Strategies for Energy Time Series Prediction](https://mlsanity.com/paper/2503.15456/) (2025)
- [A Distance Correlation-Based Approach to Characterize the Effectiveness of Recurrent Neural Networks for Time Series Forecasting](https://mlsanity.com/paper/2307.15830/) (2023)
- [Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey](https://mlsanity.com/paper/2101.08013/) (2021)
- [LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm](https://mlsanity.com/paper/2402.17120/) (2024)
- [MetaEformer: Unveiling and Leveraging Meta-patterns for Complex and Dynamic Systems Load Forecasting](https://mlsanity.com/paper/2506.12800/) (2025)
- [Entropy Guided Dynamic Patch Segmentation for Time Series Transformers](https://mlsanity.com/paper/2509.26157/) (2025)
- [Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines](https://mlsanity.com/paper/2303.10954/) (2023)
- [Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach](https://mlsanity.com/paper/2406.05887/) (2024)
- [On the Regularization of Learnable Embeddings for Time Series Forecasting](https://mlsanity.com/paper/2410.14630/) (2024)
- [Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting](https://mlsanity.com/paper/2505.17872/) (2025)
- [Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks](https://mlsanity.com/paper/2506.14472/) (2025)
- [On-Device Adaptive Battery Power Prediction for Electric Vehicles](https://mlsanity.com/paper/2607.09400/) (2026)
- [DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting](https://mlsanity.com/paper/2106.05860/) (2021)
- [Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach](https://mlsanity.com/paper/2206.15192/) (2022)
- [An evaluation of time series forecasting models on water consumption data: A case study of Greece](https://mlsanity.com/paper/2303.17617/) (2023)
- [TBDLNet: a network for classifying multidrug-resistant and drug-sensitive tuberculosis](https://mlsanity.com/paper/2310.18222/) (2023)
- [Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks](https://mlsanity.com/paper/2404.03948/) (2024)
- [A Policy Gradient-Based Sequence-to-Sequence Method for Time Series Prediction](https://mlsanity.com/paper/2406.09643/) (2024)
- [Breaking the Context Bottleneck on Long Time Series Forecasting](https://mlsanity.com/paper/2412.16572/) (2024)
- [BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting](https://mlsanity.com/paper/2501.19065/) (2025)
- [DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting](https://mlsanity.com/paper/2504.01531/) (2025)
- [Tiny-TSM: Efficiently Training a Lightweight SOTA Time Series Foundation Model](https://mlsanity.com/paper/2511.19272/) (2025)
- [Don't Look Back in Anger: MAGIC Net for Streaming Continual Learning with Temporal Dependence](https://mlsanity.com/paper/2603.08600/) (2026)
- [InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs](https://mlsanity.com/paper/2606.08601/) (2026)

## Abstract (arXiv)

Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating on two real-world datasets the most recent trends in electric load forecasting, by contrasting deep learning architectures on short term forecast (one day ahead prediction). Specifically, we focus on feedforward and recurrent neural networks, sequence to sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one.