Work

Trust-aware adjustment for accurate prediction of distributed renewable energy resources toward sustainable virtual power plant system

Year: 2025

Type: article

Abstract: Distributed renewable energy resources (DERs) like solar and wind have become key sustainable energy sources. However, their small scale and variability challenge stable energy supply, and thus DERs should be managed collectively as virtual energy plants (VPPs). Existing energy prediction uncertainties reduce trust in VPPs, limiting market growth. To address this, we propose a trust-aware prediction adjustment method. By defining DER reliability as a quantitative metric, our approach improves prediction accuracy. Experiments on real-world data show the method outperforms existing techniques in reducing non-trust hit rates.

Source: Nonlinear Theory and Its Applications IEICE

DOI: 10.1587/nolta.16.715

Authors: Jaekyeong Kim, Sejin Chun, Jungkyu Han

Institutions: Dong A University, Dong-A University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: diamond