Summary of Short-term Electricity-load Forecasting by Deep Learning: a Comprehensive Survey, By Qi Dong et al.
Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey
by Qi Dong, Rubing Huang, Chenhui Cui, Dave Towey, Ling Zhou, Jinyu Tian, Jianzhou Wang
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning has revolutionized Short-Term Electricity-Load Forecasting (STELF) by accurately predicting immediate electricity demand despite fluctuations caused by external factors like weather changes. Over the past decade, researchers have developed deep-learning-based models to forecast electricity demand with high accuracy. This paper provides a comprehensive review of STELF’s forecasting process, covering data pre-processing, feature extraction, model development and optimization, and evaluation metrics. The authors examine the current state-of-the-art in deep learning for STELF, highlighting the challenges and potential research directions for future work. Specifically, they analyze the impact of external factors on load data, the role of feature engineering in improving forecasting accuracy, and the effectiveness of various deep-learning architectures for STELF tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Have you ever wondered how power companies predict energy demand? It’s called Short-Term Electricity-Load Forecasting (STELF). External factors like weather changes can make it hard to predict energy demand. In recent years, a type of artificial intelligence called deep learning has helped improve STELF predictions. This paper looks at the last ten years of research on using deep learning for STELF and how it works. The authors explore what makes good forecasting models and what challenges still need to be solved. |
Keywords
» Artificial intelligence » Deep learning » Feature engineering » Feature extraction » Optimization
