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Journal ArticleDOI

Robust optimization based optimal chiller loading under cooling demand uncertainty

TLDR
This work proposes a robust optimization approach for uncertainty modeling of cooling demand in order to obtain robust chiller loading in the uncertain environment which cooling demand is supplied by multi-chiller system.
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This article is published in Applied Thermal Engineering.The article was published on 2019-02-05. It has received 329 citations till now. The article focuses on the topics: Robust optimization & Robust control.

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Citations
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Journal ArticleDOI

System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm

TL;DR: An optimized improved Elman neural network based on a new hybrid optimization algorithm is proposed for increasing their efficiency in the next designs of the proton exchange membrane fuel cell.
Journal ArticleDOI

Optimal parameter identification of PEMFC stacks using Adaptive Sparrow Search Algorithm

TL;DR: The proposed ASSA is utilized for minimizing the sum of squared error (SSE) between the empirical stack voltage and the calculated stack voltage by optimal selection of the mentioned parameters in the PEMFC stack.
Journal ArticleDOI

An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application

TL;DR: In this article, a new methodology has been proposed for optimal size selection of a hybrid energy system (HES) including lithium-ion battery and polymer electrolyte membrane (PEM) fuel cell to supply the driving force of a locomotive.
Journal ArticleDOI

A multi-objective home energy management system based on internet of things and optimization algorithms

TL;DR: A new optimal method for home energy management system based on the internet of things based on ZigBee, based on a new improved version of the butterfly algorithm for increasing the convergence speed and user satisfaction is presented.
Journal ArticleDOI

Tri-objective optimal scheduling of smart energy hub system with schedulable loads

TL;DR: The optimal scheduling problem of an EHS is modeled as a tri-objective optimization problem in which the operation cost, the emission pollution, and the deviation of the electrical load profile from its desired value is minimized.
References
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Journal ArticleDOI

Robust discrete optimization and network flows

TL;DR: This work proposes a robust integer programming problem of moderately larger size that allows controlling the degree of conservatism of the solution in terms of probabilistic bounds on constraint violation, and proposes an algorithm for robust network flows that solves the robust counterpart by solving a polynomial number of nominal minimum cost flow problems in a modified network.
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A new prediction model of battery and wind-solar output in hybrid power system

TL;DR: Short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component and includes a feature selection filter and hybrid forecast engine based on neural network and an intelligent evolutionary algorithm.
Journal ArticleDOI

Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve

TL;DR: In this paper, the authors proposed a framework to extract appropriate locational marginal prices for each type of reserve (up/down-going reserves at both generation- and demand-sides).
Journal ArticleDOI

A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets

TL;DR: A two-step approach that identifies a set of candidate features based on the data characteristics proposed and then selects a subset of them using correlation and instance-based feature selection methods, applied in a systematic way is presented.
Journal ArticleDOI

The price prediction for the energy market based on a new method

TL;DR: In this article, a feature selection approach was used to predict the behavior of price signalling in electricity markets. But, its prediction is difficult, where an accurate forecasting can play an important role in electricity market.
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