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Energies
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Advanced Data Modeling for Sustainable Energy Systems
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Advanced Data Modeling for Sustainable Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (21 November 2022) | Viewed by 4236

Special Issue Editors

Dr. Andrea Aquino

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy
Interests: energy; engineering thermodynamics; computational fluid dynamics; CFD simulation; energy saving; civil engineering; renewable energy technologies; heat exchangers; thermal engineering; adsorption
Special Issues, Collections and Topics in MDPI journals
Dr. Valerio Paolini

E-Mail Website
Guest Editor
National Research Council of Italy, Institute of Atmospheric Pollution Research (CNR-IIA), Via Salaria 29,300, 00015 Monterotondo, Italy
Interests: air pollution; renewable energy; sustainability; environmental analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Gallucci

E-Mail Website
Guest Editor
CREA-IT, Council for Agricultural Research and Economics, Center of Engineering and Agro-Food Processing, Via della Pascolare 16, 00015 Monterotondo, RM, Italy
Interests: biomass; combustion; renewable energies; pollution; biofuel
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The substitution of fossil fuels with renewable energy sources is rapidly increasing due to the urgency of mitigating climate change; most research efforts have focused on sustainable fuels and renewable systems for electricity and heat generation. However, the operation of these systems presents intermittency due to their strong dependence on the environmental conditions of the installation site, resulting in potential risks of disruption of the energy supply or strain of the energy grid. Thus, a societal, industrial, business, and environmental challenge is emerging.

Applications of artificial intelligence (AI) methods coupled to earth observation (EO) data are promising solutions for a more robust and stable operation of renewable energy systems (solar energy, wind energy, geothermal energy, biomass, and others). This Special Issue aims to collect the most recent AI and EO applications for energy systems installed in buildings and industries. The final goal is to contribute to the current design practice of renewable energy systems by identifying the critical points, choosing optimal components and configurations, identifying best practices, and assessing the environmental benefits.

Dr. Andrea Aquino
Dr. Valerio Paolini
Dr. Francesco Gallucci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • energy modeling
  • renewable energy systems

Published Papers (2 papers)

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Research

12 pages, 937 KiB  
Article
Energy Performance, Environmental Impacts and Costs of a Drying System: Life Cycle Analysis of Conventional and Heat Recovery Scenarios
by Dario Giuseppe Urbano, Andrea Aquino and Flavio Scrucca
Energies 2023, 16(3), 1523; https://0-doi-org.brum.beds.ac.uk/10.3390/en16031523 - 03 Feb 2023
Cited by 4 | Viewed by 1679
Abstract
High energy consumption is one of the main problems of drying, a critical process for many industrial sectors. The optimization of drying energy use results in significant energy saving and has become a topic of interest in recent decades. We investigate benefits of [...] Read more.
High energy consumption is one of the main problems of drying, a critical process for many industrial sectors. The optimization of drying energy use results in significant energy saving and has become a topic of interest in recent decades. We investigate benefits of heat recovery in a convective drying system by comparing two different scenarios. The Baseline Scenario is a conventional industrial dryer, and Scenario 1 includes the preheating of drying air by exhausts from the drying chamber. We show that the energy efficiency of the drying cycle is strictly related to the properties of the dried material and operative conditions, and performance improves significantly (by 59% to 87%) when installing a heat recovery unit (Scenario 1). Additionally, the temperature of drying air affects performance. We assess both scenarios by LCA analysis, measuring the environmental impacts and externalities of four different fuels (natural gas, light fuel oil, biomethane, and hardwood chips). Our findings indicate that heat recovery reduces environmental impacts, both when fossil and renewable fuels feed the system, but unexpected impact arises for some categories when renewable fuels are used. Full article
(This article belongs to the Special Issue Advanced Data Modeling for Sustainable Energy Systems)
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11 pages, 5021 KiB  
Article
Concentrated Solar Power with Thermoelectric Generator—An Approach Using the Cross-Entropy Optimization Method
by João Ider, Adhimar Oliveira, Rero Rubinger, Ana Karoline Silva, Aluízio Assini, Geraldo Tiago-Filho and Marcia Baldissera
Energies 2022, 15(13), 4774; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134774 - 29 Jun 2022
Cited by 3 | Viewed by 2043
Abstract
In this research, a Concentrated Solar Power (CSP) as a Parabolic Trough Collector (PTC), using Peltier cooling modules for power generation was analyzed by the Cross-Entropy method. When comparing conventional solar electric generators with this system, we have the advantage that it is [...] Read more.
In this research, a Concentrated Solar Power (CSP) as a Parabolic Trough Collector (PTC), using Peltier cooling modules for power generation was analyzed by the Cross-Entropy method. When comparing conventional solar electric generators with this system, we have the advantage that it is compact and lightweight and can be easily assembled and used as low-cost power generation equipment. For this system, we perform I(V) measurements and use fit models to accurately extract the model parameters. This is all in a standalone, robust, and simultaneous fit of three equations, through the global optimization method called Cross-Entropy. This is a robust method that had never been applied to extract parameters in a thermoelectric generation. Full article
(This article belongs to the Special Issue Advanced Data Modeling for Sustainable Energy Systems)
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