EPFG: Electricity Price Forecasting with Enhanced GANS Neural Network

Abstract

Power load forecasting in Data Analytics is an emerging technology. In this paper, we have proposed the Generative Adversarial Networks (GANS) neural network model as the classifier for probabilistic electricity price forecasting. To assess the performance of these frameworks, we apply our models on the dataset cater by (IESO) in Ontario, Canada. We have compared our proposed model with Random Forest, Support vector machine (SVM), and XG-Boost. MSE, RMSE, MAE metrices are considered for the evaluation of the model’s performance. The outcome indicates that the mean squared error (MSE) of our proposed model is 687.513 whereas the MSE of existing methodologies is 830.15, 746.812, and 776.201 which is more than our proposed methodology. Mean absolute error (MAE) of SVM and our proposed GANS Neural Network (EPFEG) have the lowest MAE that is 8%. Furthermore, EPFEG achieved almost 7% better accuracy than existing schemes.

KEYWORDS:

• Electricity price forecasting
• Generative Adversarial Networks (GANS)
• Load Consumption
• Random Forest (RF)
• Support Vector Machine (SVM)
• XG-Boost

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Maria Hanif

Maria Hanif did her bachelors in software engineering from UET-Taxila, MS (Software Engineering) from Bahria University Islamabad, Pakistan. She is pursuing PhD in computer science from the National University of Science and Technology (NUST-SEECS), Islamabad, Pakistan. She served at various positions both in industry and educational institutions. She published research papers in leading international conferences and journals.

Muhammad K. Shahzad

Muhammad K Shahzad received the BE and MS degrees in information technology from The University of Lahore (2004) and National University of Science and Technology (NUST), Islamabad, Pakistan (2007), respectively. He earned a PhD degree in computer engineering from Sungkyunkwan University (SKKU), Suwon, South Korea. He joined MONET Lab in SKKU as postdoctoral researcher after graduation in 2016. Later, he worked as assistant professor with the Electrical Energy Engineering Department from 2017 to 2018 at Keimyung University, Daegu, South Korea. Currently, he is working as an assistant professor at the Department of Computing, School of Electrical Engineering and Computer Science, NUST, Islamabad, Pakistan. His responsibilities include; Head PG DoC Coordinator, and worked as QS Ranking Rubrics lead among other administrative services. His research interests include artificial intelligence, data science and Graph Theory. Email: [email protected]

Vaneeza Mehmood

Vaneeza Mehmood did her Bachelors in information technology from International Islamic University (IIUI), Islamabad, Pakistan (2020). She is pursuing MS in data science from the National University of Science and Technology, Islamabad, Pakistan. Email: [email protected]

Inshaal Saleem

Inshal Saleem did his BE in Software Engineering from SEECS-NUST, Islamabad, Pakistan (2016). Currently pursuing his MS degree in Data Science from SEECS-NUST, Islamabad, Pakistan. He is a working as a software engineer at a software house. Email: [email protected]