# Deep Learning for Energy Time-Series Analysis and Forecasting

arXiv: https://arxiv.org/abs/2306.09129 · HTML: https://mlsanity.com/paper/2306.09129/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 64 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Building Energy Load Forecasting using Deep Neural Networks](https://mlsanity.com/paper/1610.09460/) (2016)
- [Short-term Load Forecasting with Deep Residual Networks](https://mlsanity.com/paper/1805.11956/) (2018)
- [Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based Ensembles](https://mlsanity.com/paper/2107.03825/) (2021)
- [Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space](https://mlsanity.com/paper/1807.05666/) (2018)

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

- [TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster](https://mlsanity.com/paper/2503.07649/) (2025)
- [PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering](https://mlsanity.com/paper/2602.23161/) (2026)
- [Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision](https://mlsanity.com/paper/2510.16980/) (2025)
- [Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting](https://mlsanity.com/paper/2605.24911/) (2026)
- [Efficient Deterministic Renewable Energy Forecasting Guided by Multiple-Location Weather Data](https://mlsanity.com/paper/2404.17276/) (2024)

## Abstract (arXiv)

Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future. Different tasks are involved in the general field of energy time-series analysis and forecasting, with electric load demand forecasting, personalized energy consumption forecasting, as well as renewable energy generation forecasting being among the most common ones. Following the exceptional performance of Deep Learning (DL) in a broad area of vision tasks, DL models have successfully been utilized in time-series forecasting tasks. This paper aims to provide insight into various DL methods geared towards improving the performance in energy time-series forecasting tasks, with special emphasis in Greek Energy Market, and equip the reader with the necessary knowledge to apply these methods in practice.