Proceedings Article10.1109/conecct57959.2023.10234682
A Comparative Study on Deep Learning Methods for Forecasting Load in Smart Grid
Suman Mondal,Shruti Gatade,M. S. Sreekar,Aman Raj,Rishab K Mogral +4 more
- 14 Jul 2023
pp 1-5
2
TL;DR: A comparative study on deep learning methods for forecasting load in smart grids finds that RNN-LSTM is effective in capturing temporal patterns, DenseNet and ResNet excel in feature extraction, and the hybrid model offers a balance of precision and effectiveness.
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Abstract: This study compares deep learning models such as RNN-LSTM, DenseNet, and ResNet, along with a hybrid model combining SVM, XGBoost, and DTC, for load forecasting in power systems. The results demonstrate the strengths and weaknesses of each approach, with RNN-LSTM capturing temporal patterns effectively, DenseNet and ResNet excelling in feature extraction, and the hybrid model offering a balanced combination of precision and effectiveness. These findings offer valuable insights for power system operators and researchers in selecting suitable load forecasting models.
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Citations
Energy-Efficient IoT with Deep Learning: Optimizing Resource Allocation in Smart Grids
Rahul Mishra,Vaibhava Vasantrao Desai,Ramesh Krishnamoorthy,M. Amina Begum,Jarabala Ranga,Syed Noeman Taqui +5 more
- 23 Nov 2023
TL;DR: Energy-efficient IoT with deep learning optimizes resource allocation in smart grids, enhancing energy efficiency through adaptive, data-driven strategies.
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Short-Term Load Forecasting via a Multi-Layer Network Based on Feature Weight Optimization
Yan Huang,Jieqiong Gan,Zhongqi Wang,Zhaozhe Zhong,Jian Liu,Junwei Cao +5 more
- 20 Oct 2023
TL;DR: Short-term load forecasting via a multi-layer network based on feature weight optimization achieves high accuracy, outperforming conventional methods.
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