Offline training for improving online performance of a genetic algorithm based optimization model for hourly multi-reservoir operation. (October 2017)

• Record Type: Journal Article
• Authors: Chen, Duan; Leon, Arturo S.; Engle, Samuel P.; Fuentes, Claudio; Chen, Qiuwen
• Is Part Of: Environmental modelling & software. Volume 96(2017)
• Page Start: 46
• Page End: 57
• Publication Date: 2017-10
• Subjects: Reservoir operation -- Genetic algorithm -- Model training -- Machine learning -- Karhunen-Loeve expansion; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.06.038
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A novel framework, which incorporates implicit stochastic optimization (Monte Carlo method), cluster analysis (machine learning algorithm), and Karhunen-Loeve expansion (dimension reduction technique) is proposed. The framework aims to train a Genetic Algorithm-based optimization model with synthetic and/or historical data) in an offline environment in order to develop a transformed model for the online optimization (i.e., real-time optimization). The primary output from the offline training is a stochastic representation of the decision variables that are constituted by a series of orthogonal functions with undetermined random coefficients. This representation preserves covariance structure of the simulated decisions from the offline training as gains some "knowledge" regarding the search space. Due to this gained "knowledge", better candidate solutions can be generated and hence, the optimal solutions can be obtained faster. The feasibility of the approach is demonstrated with a case study for optimizing hourly operation of a ten-reservoir system during a two-week period. Highlights: A model training framework incorporate Monte Carlo method, machine learning algorithm and dimension reduction technique. Trained model significantly improve the online performance of optimization. Generic representation allow a broad application in environmental and water resources system.