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

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.