Open Access

ARTICLE

Generating Synthetic Data to Reduce Prediction Error of Energy Consumption

Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun^*

Department of Computer Engineering, Jeju National University, Jeju-si, Korea

* Corresponding Author: Yung-Cheol Byun. Email:

(This article belongs to the Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2022, 70(2), 3151-3167. https://doi.org/10.32604/cmc.2022.020143

Received 11 May 2021; Accepted 18 June 2021; Issue published 27 September 2021

Abstract

Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a technique used for increasing the data available for training. Synthetic data are the generation of new data which can be trained to improve the accuracy of prediction models. In this paper, we propose a model that takes time series energy consumption data as input, pre-processes the data, and then uses multiple augmentation techniques and generative adversarial networks to generate synthetic data which when combined with the original data, reduces energy consumption prediction error. We propose TGAN-skip-Improved-WGAN-GP to generate synthetic energy consumption time series tabular data. We modify TGAN with skip connections, then improve WGAN-GP by defining a consistency term, and finally use the architecture of improved WGAN-GP for training TGAN-skip. We used various evaluation metrics and visual representation to compare the performance of our proposed model. We also measured prediction accuracy along with mean and maximum error generated while predicting with different variations of augmented and synthetic data with original data. The mode collapse problem could be handled by TGAN-skip-Improved-WGAN-GP model and it also converged faster than existing GAN models for synthetic data generation. The experiment result shows that our proposed technique of combining synthetic data with original data could significantly reduce the prediction error rate and increase the prediction accuracy of energy consumption.

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