Uncertainty-Aware Forecasting of Renewable Energy Sources

Pekaslan, Direnc; Wagner, Christian; Garibaldi, Jonathan M.; Mar�n, Luis G.; S�ez, Doris

Abstract

Smart grid systems are designed to enable the efficient capture and intelligent distribution of electricity across a distributed set of utilities. They are an essential component of increasingly important renewable energy sources, where it is vital to forecast the levels of energy being fed into and drawn from the grid. However, because of the high levels of uncertainty affecting real-world environments, accurate forecasting for example of wind power generation-being directly dependent on meteorological parameters and climatic conditions-is extremely challenging. Fuzzy Logic systems are frequently used in control systems to leverage their capacity for handling varying levels of uncertainty. In most cases, while uncertainty affecting the systems is captured in fuzzy sets (FSs), the final output of such systems is reduced to a crisp number (e.g. a control output). The latter process, while providing an efficient pathway to generating a specific control output, at the same time implies substantial information loss, as the uncertainty information captured in the FS outputs of these systems is effectively discarded. In this paper, we explore the potential of Mamdani fuzzy logic system based forecasting in order to generate not only a numeric forecast of the energy generated, but to also generate uncertainty intervals around said forecast indicating the level of uncertainty associated with the prediction. The proposed model is explored using both synthetic and smart-grid specific real-world (wind power) time series datasets. The results of the study indicate that utilising the 'complete' FS output can provide valuable additional information in terms of the reliability of the forecast without any extra computational cost. At a general level, the approach indicates strong potential for leveraging the uncertainty information in fuzzy system outputs-which is commonly discarded-in real world applications.

Citation

Pekaslan, D., Wagner, C., Garibaldi, J. M., Marín, L. G., & Sáez, D. (2020). Uncertainty-Aware Forecasting of Renewable Energy Sources. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). https://doi.org/10.1109/bigcomp48618.2020.00-68

• Conference Name: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp)
• Start Date: Feb 19, 2020
• End Date: Feb 22, 2020
• Acceptance Date: Nov 25, 2019
• Online Publication Date: Feb 22, 2020
• Publication Date: Apr 20, 2020
• Deposit Date: Mar 24, 2020
• Publicly Available Date: Mar 24, 2020
• Book Title: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp)
• ISBN: 9781728160344
• DOI: https://doi.org/10.1109/bigcomp48618.2020.00-68
• Keywords: Index Terms-Forecasting; Uncertainty Intervals; Smart-Grid; Renewable Energy
• Public URL: https://nottingham-repository.worktribe.com/output/4197371
• Publisher URL: https://ieeexplore.ieee.org/document/9070376
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.