Review: Multi-objective optimization methods and application in energy saving

TL;DR: Different sub-models, hybridization strategies, structural designs, screening criteria, and new directions in hybrid modeling are reviewed, with focus on the corresponding applications in chemical, petroleum, and energy systems.

TL;DR: A methodology both to optimize and to perform a sensitivity analysis of an autonomous hybrid PV-diesel-battery energy system and it was found that the PSO based approach is more cost effective with more PV penetration than HOMER.

TL;DR: A broad literature survey of the intelligent predictors in the field of wind energy forecasting, including five types of shallow predictors and four types of deep learning-based predictors, to show the frameworks of mainstream predictive models in artificial intelligence.

TL;DR: In this paper , the authors review energy-saving solutions with a focus on the actual energy crisis, green alternatives to fossil fuel heating, energy saving in buildings and transportation, artificial intelligence for sustainable energy, and implications for the environment and society.

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.

TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.