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Front. Energy Res.
Sec. Wind Energy
Volume 11 - 2023 | doi: 10.3389/fenrg.2023.1239973

Interval Reservoir Computing: Theory and Case Studies

 Lan-Da Gao1 Zhen-Hua Li1 Meng-Yi Wu1* Qing-Lan Fan1 Ling Xu1 Zhuo-Min Zhang1 Yi-Peng Zhang1  Yanyue Liu1
  • 1Research Institute of Highway, Ministry of Transport, China

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The time series data in many applications, for example, wind power and vehicle trajectory, shows significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing a novel interval reservoir computing. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using Recurrent Neural Network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. Besides, the confidence probability over the feasibility goes to $1$ very quickly as the sample number increases.

Keywords: Uncertain dynamical systems, Probabilistic prediction, time series data, wind power forecasting, Vehicle Trajectory

Received: 14 Jun 2023; Accepted: 25 Jul 2023.

Copyright: © 2023 Gao, Li, Wu, Fan, Xu, Zhang, Zhang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Meng-Yi Wu, Research Institute of Highway, Ministry of Transport, Beijing, China