Evolving Fuzzy Prediction Intervals in Nonstationary Environments

This work aims at designing a novel evolving fuzzy prediction interval able to model nonlinear time-variant systems. To achieve this goal, we integrate a passive mechanism meant to update the model when new data are available with an active mechanism able to trigger ad-hoc adaptation mechanisms when changes are detected in the data-generating process. The base model considered for this proposal was the prediction interval based on fuzzy numbers, which allow handling the characterization of the uncertainty of a system through additional parameters focused on the computation of the interval width. The performance of the proposed solution has been tested on both synthetic and real data. In this work, the synthetic data was generated from a generic nonlinear time-variant system, while the real data correspond to measurements of solar power generation obtained from photovoltaic panels. The simulation results confirm the effectiveness of the proposed evolving fuzzy prediction interval for modeling systems that present changes in their dynamics over time.

Automated summary

This work aims to design a novel evolving fuzzy prediction interval for modeling nonlinear time-variant systems. It integrates a passive mechanism to update the model when new data are available and an active mechanism to trigger adaptation mechanisms when changes are detected in the data-generating process. The proposed solution is based on a prediction interval using fuzzy numbers to handle the uncertainty of a system and has been tested on synthetic and real data, confirming its effectiveness for modeling systems with dynamic changes over time.