Author:
Meng Zhaorui,Xie Xiaozhu,Xie Yanqi,Sun Jinhua
Abstract
Deep learning is increasingly used in short-term load forecasting. However, deep learning models are difficult to train, and adjusting training hyper-parameters takes time and effort. Automated machine learning (AutoML) can reduce human participation in machine learning process and improve the efficiency of modelling while ensuring the accuracy of prediction. In this paper, we compare the usage of three AutoML approaches in short-term load forecasting. The experiments on a real-world dataset show that the predictive performance of AutoGluon outperforms that of AutoPytorch and Auto-Keras, according to three performance metrics: MAE, RMSE and MAPE. AutoPytorch and Auto-Keras have similar performance and are not easy to compare.
Reference12 articles.
1. Mamun A.A., et al. “A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models.” IEEE Access PP.99(2020):1–1.
2. Mujeeb S., et al. “Big Data Analytics for Load Forecasting in Smart Grids: A Survey.” International Conference on Cyber Security and Computer Science (ICONCS), 2018 2019.
3. Bae D.J., Kwon B.S., and Song K.B.. “XGBoost- Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation.” Energies 15(2021).
4. Shi H., Xu M., and Li R.. “Deep Learning for Household Load Forecasting - A Novel Pooling Deep RNN.” IEEE Transactions on Smart Grid (2017):1-1.
5. Zhang Y., et al. “Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network.” IEEE Transactions on Smart Grid (2019).