E3S Web Conf.
Volume 394, 20236th International Symposium on Resource Exploration and Environmental Science (REES 2023)
|Number of page(s)||9|
|Published online||02 June 2023|
Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting
Jiangnan University, Wuxi 214122, China.
* Corresponding author: firstname.lastname@example.org
With the improvement of data processing power and the continuous development of modern power grids, there is an increasing demand for accuracy in predicting power load. To study the field of power load forecasting, this article summarizes and categorizes different models into three types: traditional models, single machine learning models, and hybrid models, based on previous literature. Firstly, a general overview is provided of the application of different models in power load forecasting. Secondly, typical models from three categories are selected for a detailed introduction. In traditional models, the ARIMA model is chosen, while in single machine learning models, CNN, and LSTM are chosen. For the hybrid model, the ResNet-LSTM mixed neural network is selected for the introduction. Finally, four different datasets were used to test different models. The differences and patterns of the models were summarized, and suggestions were proposed for future research directions in the field of power load forecasting.
Key words: Power load forecasting / ARIMA / CNN / LSTM / ResNet-LSTM
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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