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6.1
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Clean Technol.
Special Issues
AI in Clean Energy Systems
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AI in Clean Energy Systems

A special issue of Clean Technologies (ISSN 2571-8797).

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 20001

Special Issue Editors

Dr. Marjan Goodarzi

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Lamar University, Beaumont, TX 77705, USA
Interests: fluid mechanics; computational fluid dynamics; numerical simulation; CFD simulation; numerical analysis; engineering thermodynamics; thermal engineering; numerical modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Reza Maihami

E-Mail Website
Guest Editor
School of Business and Leadership, Our Lady of the Lake University, San Antonio, TX 78207, USA
Interests: green supply chain; sustainability management; clean waste management; clean energy supply chain; renewable energy supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is applied in many fields, including clean energies. AI enables clean energy systems to collect, handle, and process a vast quantity of data. This leads to new technologies and policies that improve the efficiency, distribution, and conversion of clean energy systems. Machine learning as the most common approach in AI and new technologies are developed to study and analyze different aspects of clean energy systems, such as system control, optimization, system design, supply chain design, cost minimization, distribution management, policy design, and socio-economic planning. For example, new forecasting methods provide a better prediction of wind farm renewable energy production, making energy grid design easier. AI has improved automation in clean energy systems. This increases efficiency, particularly in solar and wind energy systems. Cost-saving and power generation increasing are advantages of using AI-based automation systems.

This Special Issue "AI in Clean Energy Systems" will publish new studies using AI approaches to explore, produce, distribute, and consume clean energies, including wind, solar, wave, geothermal, and hydropower.

We invite all researchers active in the broad and captivating AI and Clean Energy Systems domain to submit articles for this Special Issue of Clean Technologies journal.

Dr. Marjan Goodarzi
Dr. Reza Maihami
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. Clean Technologies is an international peer-reviewed open access quarterly 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 1600 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

  • clean energy
  • AI
  • machine learning
  • system design
  • generating and integration clean energy
  • plasma process
  • clean fuel production
  • clean energy life cycle assessment (LCA)
  • sustainable supply chain planning

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

60 pages, 22002 KiB  
Article
The Energy Efficiency Post-COVID-19 in China’s Office Buildings
by Carlos C. Duarte and Nuno D. Cortiços
Clean Technol. 2022, 4(1), 174-233; https://doi.org/10.3390/cleantechnol4010012 - 2 Mar 2022
Cited by 9 | Viewed by 4345
Abstract
China promptly took the leading step to mitigate the spread of COVID-19, producing the first scientific guidelines assuming health above energy consumption and significantly changing HVAC/AHU operation. The research intended to fulfill the gap by measuring the impact of the guidelines on energy [...] Read more.
China promptly took the leading step to mitigate the spread of COVID-19, producing the first scientific guidelines assuming health above energy consumption and significantly changing HVAC/AHU operation. The research intended to fulfill the gap by measuring the impact of the guidelines on energy use intensity, CO2 emissions, and energy operation costs related to workplaces. The guidelines are long-term sector and industry trends following occupants’ health and safety concerns, and today they are applied to nursing homes. The research extended the study to post-COVID-19 scenarios by crossing those settings with published reports on telework predictions. The methodology resorts to Building Energy Simulation software to assess the Chinese standard large office building on 8 climate zones and 17 subzones between pre- and post-COVID-19 scenarios under those guidelines. The outcomes suggest an upward trend in energy use intensity (11.70–12.46%), CO2 emissions (11.13–11.76%), and costs (9.37–9.89%) for buildings located in “warm/mixed” to “subarctic” climates, especially in colder regions with high heating demands. On the other hand, the figures for “very hot” to “hot/warm” climates lower the energy use intensity (14.76–15.47%), CO2 emissions (9%), and costs (9.64–9.77%). Full article
(This article belongs to the Special Issue AI in Clean Energy Systems)
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21 pages, 19579 KiB  
Article
Application of Machine Learning to Accelerate Gas Condensate Reservoir Simulation
by Anna Samnioti, Vassiliki Anastasiadou and Vassilis Gaganis
Clean Technol. 2022, 4(1), 153-173; https://doi.org/10.3390/cleantechnol4010011 - 1 Mar 2022
Cited by 9 | Viewed by 3499
Abstract
According to the roadmap toward clean energy, natural gas has been pronounced as the perfect transition fuel. Unlike usual dry gas reservoirs, gas condensates yield liquid which remains trapped in reservoir pores due to high capillarity, leading to the loss of an economically [...] Read more.
According to the roadmap toward clean energy, natural gas has been pronounced as the perfect transition fuel. Unlike usual dry gas reservoirs, gas condensates yield liquid which remains trapped in reservoir pores due to high capillarity, leading to the loss of an economically valuable product. To compensate, the gas produced on the surface is stripped from its heavy components and reinjected back to the reservoir as dry gas thus causing revaporization of the trapped condensate. To optimize this gas recycling process compositional reservoir simulation is utilized, which, however, takes very long to complete due to the complexity of the governing differential equations implicated. The calculations determining the prevailing k-values at every grid block and at each time step account for a great part of total CPU time. In this work machine learning (ML) is employed to accelerate thermodynamic calculations by providing the prevailing k-values in a tiny fraction of the time required by conventional methods. Regression tools such as artificial neural networks (ANNs) are trained against k-values that have been obtained beforehand by running sample simulations on small domains. Subsequently, the trained regression tools are embedded in the simulators acting thus as proxy models. The prediction error achieved is shown to be negligible for the needs of a real-world gas condensate reservoir simulation. The CPU time gain is at least one order of magnitude, thus rendering the proposed approach as yet another successful step toward the implementation of ML in the clean energy field. Full article
(This article belongs to the Special Issue AI in Clean Energy Systems)
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13 pages, 2459 KiB  
Article
Solar Photovoltaic System-Based Reduced Switch Multilevel Inverter for Improved Power Quality
by Madhu Andela, Ahmmadhussain Shaik, Saicharan Beemagoni, Vishal Kurimilla, Rajagopal Veramalla, Amritha Kodakkal and Surender Reddy Salkuti
Clean Technol. 2022, 4(1), 1-13; https://doi.org/10.3390/cleantechnol4010001 - 2 Jan 2022
Cited by 11 | Viewed by 4017
Abstract
This paper deals with a reduced switch multi-level inverter for the solar photovoltaic system-based 127-level multi-level inverter. The proposed technique uses the minimum number of switches to achieve the maximum steps in staircase AC output voltage when compared to the flying capacitor multi-level [...] Read more.
This paper deals with a reduced switch multi-level inverter for the solar photovoltaic system-based 127-level multi-level inverter. The proposed technique uses the minimum number of switches to achieve the maximum steps in staircase AC output voltage when compared to the flying capacitor multi-level inverter, cascaded type multilevel inverter and diode clamped multi-level inverter. The use of a minimum number of switches decreases the cost of the system. To eliminate the switching losses, in this topology a square wave switch is used instead of pulse width modulation. Thereby the total harmonic distortion (THD) and harmonics have been reduced in the pulsating AC output voltage waveform. The performance of 127-level MLI is compared with 15 level, 31-level and 63-level multilevel inverters. The outcomes of the solar photovoltaic system-based 127-level multi-level inverter have been simulated in a MATLAB R2009b environment. Full article
(This article belongs to the Special Issue AI in Clean Energy Systems)
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23 pages, 2337 KiB  
Article
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation
by Valentina Sessa, Edi Assoumou, Mireille Bossy and Sofia G. Simões
Clean Technol. 2021, 3(4), 858-880; https://doi.org/10.3390/cleantechnol3040050 - 20 Dec 2021
Cited by 11 | Viewed by 3216
Abstract
Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better [...] Read more.
Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture. Full article
(This article belongs to the Special Issue AI in Clean Energy Systems)
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18 pages, 4096 KiB  
Article
Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence
by Abdulelah D. Alhamayani, Qiancheng Sun and Kevin P. Hallinan
Clean Technol. 2021, 3(4), 743-760; https://doi.org/10.3390/cleantechnol3040044 - 12 Oct 2021
Cited by 7 | Viewed by 3623
Abstract
Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 [...] Read more.
Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 to 85%. The present research advances prior research to enable thermal comfort control in residential buildings using a smart Wi-Fi thermostat. “Fanger’s Predicted Mean Vote model” is used to define thermal comfort. A machine learning model leveraging historical smart Wi-Fi thermostat data and outdoor temperature is trained to predict indoor temperature. A Long Short-Term-Memory neural network algorithm is employed for this purpose. The model considers solar heat input estimations to a residence as input features. The results show that this approach yields a substantially improved ability to accurately model and predict indoor temperature. Secondly, it enables a more accurate estimation of potential savings from thermal comfort control. Cooling energy savings ranging from 33 to 47% are estimated based upon real data for variable energy effectiveness and solar exposed residences. Full article
(This article belongs to the Special Issue AI in Clean Energy Systems)
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