Affiliation:
1. School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Abstract
Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference66 articles.
1. Fatema, I., Kong, X., and Fang, G. (2020, January 28–30). Analyzing and forecasting electricity demand and price using deep learning model during the COVID-19 pandemic. Proceedings of the Parallel Architectures, Algorithms and Programming: 11th International Symposium, PAAP, Proceedings 11, Shenzhen, China.
2. Power system load forecasting using mobility optimization and multi-task learning in COVID-19;Liu;Appl. Energy,2022
3. (2022, August 20). Aggregated Demand and Price Data. Available online: https://aemo.com.au/.
4. (2023, January 20). Australian Energy Market Operator (AEMO 2022). Available online: https://aemo.com.au/.
5. Chen, Y., Yang, W., and Zhang, B. (2006). Using mobility for electrical load forecasting during the covid-19 pandemic. arXiv.
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