Load forecasting is one of the critical tasks for enhancing the energy efficiency of smart grids. Even though recent deep learning-based load forecasting models have shown excellent forecasting performance, one of the common problems they faced was that their forecasting accuracy was highly dependent on the data quality and quantity available for the model training. Collecting a sufficient amount of high-quality data is expensive and time-consuming. Recently, a generative adversarial network (GAN) has shown its potential as a solution to the data shortage problem by generating virtual data based on a small amount of real data, and several studies have used GAN to generate electric load data for training forecasting models. However, due to the noise data problem of GANs, their predictive performance also deteriorated. To solve this problem, in this study, we propose a two-stage data generation scheme that more effectively generates input and output variables for short-term load forecasting. In the first stage, we generate virtual calendar and temperature data used as input variables using a conditional tabular GAN (CTGAN). In the second stage, we generate electric load data corresponding to the input variables using a deep learning-based regression model. Lastly, we construct our forecasting model by training another regression model using a mixture of generated data and real data. To verify the effectiveness of our scheme, we conducted extensive experiments using various datasets and data generation models. We report some of the results.
Bibliographical noteFunding Information:
This work was supported in part by the Korea Electric Power Corporation under Grant R18XA05, and in part by the Energy Cloud Research and Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under Grant 2019M3F2A1073184.
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- Conditional generative adversarial networks
- Short-term load forecasting
- Smart grid
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)
- Electrical and Electronic Engineering