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Digital Mockup and Visualization Application in Energy Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (1 March 2024) | Viewed by 8194

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Prof. Dr. Jun-Ho Huh

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Guest Editor

Department of Software, Catholic University of Pusan, 57 Oryundae-ro, Bugok 3(sam)-dong, Geumjeong-gu, Busan, Republic of Korea
Interests: smart grid; medical IT; network security; energy harvesting; optimization
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Special Issue Information

Dear Colleagues,

There have been recent attempts to visualize big data and apply a digital mock up (DMU) to energy systems. DMU is a technology that can largely reduce time and cost by applying 3D solid modeling based on the data provided to parts that are to be manufactured. These parts are then assembled and examined on a computer screen to identify any errors in the design stage or address possible manufacturing issues in advance to improve quality. The application of DMU allows each system designer to achieve optimal designs within a limited space by receiving necessary design data for a system that must be mounted during the re-designing of parts or design of additional parts.

Additionally, similar to the energy-gauge user interface (UI) of electric vehicles, a technology that virtualizes electric energy has been commercialized, in conjunction with a big data study, which aims not only to monitor electric power usage in real time but also to predict future applications.

The significance of such a study especially lies in designing an electric system that understands the peak or off-peak periods of electric demand from electric power-based devices/equipment, including electric vehicles, smart homes or solar power stations and virtualizing the prediction of future power demands.

Meanwhile, the papers for this Special Issue will include some original developments, such as monitoring the power usage of old buildings with digital meters through power line communication (PLC). In addition, manuscripts that propose energy system solutions in the context of new multimedia technology relevant to virtualization (AR/VR/Metaverse, etc.) are most welcome. Additionally, papers focusing on the topics of software or hardware aspects are welcome as long as they focus on virtualization technology.

For this purpose, this Special Issue is open to receiving a variety of high-quality manuscripts concerning the purpose of solving (using DMU, new multimedia technology; visualization) challenges regarding Industry 4.0 based on smart grid/micro grid/power plants/energy harvesting. Participants may choose to write about one of the subjects listed below, though authors are not necessarily limited to these topics:

Energy harvesting system service respecting human beings and their livelihood;
Energy harvesting solutions of artificial intelligence and big data;
Visualization/DMU in energy systems;
UI/UX of energy systems;
Visualization/DMU of modeling in energy systems;
Engineering mathematical theories of energy harvesting that deeply affect science and industry;
Energy harvesting media techniques and services for systems engineering;
Visualization/DMU of power systems control;
Optimization of operation of power systems;
Visualization/DMU of energy management systems;
Visualization/DMU of IoT and/or AI for power systems;
Control visualization/DMU method of power electronics;
Optimal operation of renewable energy;
A public energy harvesting integration system for future systems;
Energy storage system (ESS) for future systems;
Visualization/DMU of photovoltaic (PV) systems and nuclear power plants;
Visualization/DMU of thermal power plants;
Visualization/DMU of wind power plants;
Visualization/DMU of photovoltaic (PV) plants;
Visualization/DMU of blockchain-based REC for photovoltaic (PV) systems;
Security visualization/DMU of photovoltaic (PV) plants/thermal power plants/wind power plants/nuclear power plants;
AR/VR/Metaverse of photovoltaic (PV) plants/thermal power plants/wind power plants/ nuclear power plants;
Visualization/DMU of smart farm and photovoltaic (PV) systems.

Prof. Dr. Jun-Ho Huh
Guest Editor

Manuscript Submission Information

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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.

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Keywords

visualization
digital mock up
DMU
energy harvesting
UI/UX
micro grid
smart grid
new multimedia technology

Published Papers (3 papers)

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Research

16 pages, 2036 KiB

Open AccessArticle

Optimized Hierarchical Tree Deep Convolutional Neural Network of a Tree-Based Workload Prediction Scheme for Enhancing Power Efficiency in Cloud Computing

by Thirumalai Selvan Chenni Chetty, Vadim Bolshev, Siva Shankar Subramanian, Tulika Chakrabarti, Prasun Chakrabarti, Vladimir Panchenko, Igor Yudaev and Yuliia Daus

Energies 2023, 16(6), 2900; https://doi.org/10.3390/en16062900 - 21 Mar 2023

Cited by 10 | Viewed by 2095

Abstract

Workload prediction is essential in cloud data centers (CDCs) for establishing scalability and resource elasticity. However, the workload prediction accuracy in the cloud data center could be better due to noise, redundancy, and low performance for workload prediction. This paper designs a hierarchical tree-based deep convolutional neural network (T-CNN) model with sheep flock optimization (SFO) to enhance CDCs’ power efficiency and workload prediction. The kernel method is used to preprocess historical information from the CDCs. Additionally, T-CNN model weight parameters are optimized using SFO. The suggested TCNN-SFO technology has successfully reduced excessive power consumption while correctly forecasting the incoming demand. Further, the proposed model is assessed using two benchmark datasets: Saskatchewan HTTP traces and NASA. The developed model is executed in a Java tool. Therefore, associated with existing methods, the developed technique has achieved higher accuracy of 20.75%, 19.06%, 29.09%, 23.8%, and 20.5%, as well as lower energy consumption of 20.84%, 18.03%, 28.64%, 30.72%, and 33.74% when validating the Saskatchewan HTTP traces dataset. It has also achieved higher accuracy of 32.95%, 12.05%, 32.65%, and 26.54%. Full article

(This article belongs to the Special Issue Digital Mockup and Visualization Application in Energy Systems)

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17 pages, 5543 KiB

Open AccessArticle

Solar Power Forecasting Using CNN-LSTM Hybrid Model

by Su-Chang Lim, Jun-Ho Huh, Seok-Hoon Hong, Chul-Young Park and Jong-Chan Kim

Energies 2022, 15(21), 8233; https://doi.org/10.3390/en15218233 - 04 Nov 2022

Cited by 48 | Viewed by 4297

Abstract

Photovoltaic (PV) technology converts solar energy into electrical energy, and the PV industry is an essential renewable energy industry. However, the amount of power generated through PV systems is closely related to unpredictable and uncontrollable environmental factors such as solar radiation, temperature, humidity, cloud cover, and wind speed. Particularly, changes in temperature and solar radiation can substantially affect power generation, causing a sudden surplus or reduction in the power output. Nevertheless, accurately predicting the energy produced by PV power generation systems is crucial. This paper proposes a hybrid model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) for stable power generation forecasting. The CNN classifies weather conditions, while the LSTM learns power generation patterns based on the weather conditions. The proposed model was trained and tested using the PV power output data from a power plant in Busan, Korea. Quantitative and qualitative evaluations were performed to verify the performance of the model. The proposed model achieved a mean absolute percentage error of 4.58 on a sunny day and 7.06 on a cloudy day in the quantitative evaluation. The experimental results suggest that precise power generation forecasting is possible using the proposed model according to instantaneous changes in power generation patterns. Moreover, the proposed model can help optimize PV power plant operations. Full article

(This article belongs to the Special Issue Digital Mockup and Visualization Application in Energy Systems)

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20 pages, 3783 KiB

Open AccessArticle

Low Power Sensor Location Prediction Using Spatial Dimension Transformation and Pattern Recognition

by Wonchan Lee and Chang-Sung Jeong

Energies 2022, 15(12), 4243; https://doi.org/10.3390/en15124243 - 09 Jun 2022

Cited by 1 | Viewed by 1222

Abstract

A method of positioning a location on a specific object using a wireless sensor has been developed for a long time. However, due to the error of wavelengths and various interference factors occurring in three-dimensional space, accurate positioning is difficult, and predicting future locations is even more difficult. It uses IoT-based node pattern recognition technology to overcome positioning errors or inaccurate predictions in wireless sensor networks. It developed a method to improve the current positioning accuracy in a sensor network environment and a method to learn a pattern of position data directly from a wavelength receiver. The developed method consists of two steps: The first step is a method of changing location data in 3D space to location data in 2D space in order to reduce the possibility of positioning errors in 3D space. The second step is to reduce the range of the moving direction angle in which the data changed in two dimensions can be changed in the future and to predict future positions through pattern recognition of the position data. It is to calculate the expected position in the future. In conclusion, three-dimensional positioning accuracy was improved through this method, and future positioning accuracy was also improved. The core technology was able to reduce inevitable errors by changing the spatial dimension from 3D to 2D and to improve the accuracy of future location prediction by reducing the range of the movable direction angle of the location data changed to 2D. It was also possible to obtain the result that the prediction accuracy increases in proportion to the amount of data accumulated in the wavelength receiver and the learning time. In the era of the Fourth Industrial Revolution, this method is expected to be utilized in various places, such as smart cities, autonomous vehicles, and disaster prediction. Full article

(This article belongs to the Special Issue Digital Mockup and Visualization Application in Energy Systems)

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