Deep neural networks for energy load forecasting

By Kasun Amarasinghe, Daniel L. Marino, Milos Manic. International Symposium on Industrial Electronics, 2017

305 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4fefb31ac103bca23091c6e81f219c21a2b1ff1c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. .. Nahid-Al-Masood
2. Cesare Alippi
3. Slobodan Lukovic
4. Shohana Rahman Deeba
5. Alberto Gasparin
6. Eklas Hossain
7. Shafiul Hasan Rafi

In Electrical and Electronic Engineering

1. .. Nahid-Al-Masood
2. Cesare Alippi
3. Slobodan Lukovic
4. Shohana Rahman Deeba
5. Alberto Gasparin
6. Eklas Hossain
7. Shafiul Hasan Rafi

Citations in the corpus

• Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting (2020, result): “In both [17,18], the authors proposed CNN models for short-term load forecasting that provides comparable results to LSTM models.”
• Deep Learning for Time Series Forecasting: The Electric Load Case (2019, methodology): “In [16] a CNN is compared to recurrent and feed forward approaches showing promising results on a benchmark dataset.”
• A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network (2021, methodology): “In [33], a load forecasting methodology based on CNN model is introduced and compared with various artificial neural networks.”