Statistical investigation of a dehumidification system performance using Gaussian process regression-Reference-Cited by-同舟云学术

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Statistical investigation of a dehumidification system performance using Gaussian process regression

Published:2019-11 Issue: Volume:202 Page:109406
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Akhlaghi Yousef Golizadeh,Zhao Xudong,Shittu Samson,Badiei Ali,Cattaneo Marco E.G.V.,Ma Xiaoli

Funder

European Commission H2020

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference35 articles.

1. Sorption and regeneration performance of novel solid desiccant based on PVA-LiCl electrospun nanofibrous membrane;Dai;Polym. Test,2017

2. Numerical investigation of a solar/waste energy driven sorption/desorption cycle employing a novel adsorbent bed;Bi;Energy,2018

3. Two-stage air-dehumidification system for the tropics–experimental and theoretical analysis of a lab system;Safizadeh;Energy Procedia,2014

4. Control performance of a dedicated outdoor air system adopting liquid desiccant dehumidification;Xiao;Appl. Energy,2011

5. Desiccant-based AHU interacting with a CPVT collector: simulation of energy and environmental performance;Calise;Solar Energy,2014

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1. Evaluation and optimization of a new condensation dehumidification system with fan-coil units using low-cost cold source for greenhouses;Journal of Cleaner Production;2023-12

2. Combined Rankine Cycle and dew point cooler for energy efficient power generation of the power plants - A review and perspective study;Energy;2022-01

3. Review of Desiccant in the Drying and Air-Conditioning Application;International Journal of Heat and Technology;2021-10-31

4. Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050;Applied Energy;2021-01

5. A New Predictive Model for Evaluating Chlorophyll-a Concentration in Tanes Reservoir by Using a Gaussian Process Regression;Water Resources Management;2020-11-04

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