Energy Consumption Analysis and Characterization of Complex Systems

Topic Information

Dear Colleagues,

Understanding and characterizing energy consumption are fundamental challenges in the design and operation of modern complex systems, such as smart grids, intelligent transportation networks, data centers, and advanced manufacturing systems. These systems consist of numerous interconnected components with diverse energy consumption patterns and behaviors. Accurately analyzing and characterizing energy use in such systems are crucial for identifying inefficiencies, understanding system dynamics, and guiding the development of sustainable energy management practices.

This Topic aims to bring together high-quality research contributions that focus on the analysis and characterization of energy consumption in complex systems. We encourage submissions that present new methods, frameworks, and case studies that advance the understanding of energy usage patterns, reveal underlying factors influencing energy demand, and provide insights into improving energy efficiency and sustainability across various domains.

Topics of interest include the following:

• Methods and models for energy consumption analysis in complex systems;
• Characterization of energy usage patterns in networked and distributed environments;
• Data-driven approaches to understanding energy demand and consumption;
• Machine learning and AI methods for energy consumption prediction and analysis;
• Energy profiling and benchmarking in smart grids, transportation systems, and industrial settings;
• Empirical studies on energy behavior in multi-agent and distributed systems;
• Tools and frameworks for visualizing and interpreting energy consumption data;
• Socio-technical factors influencing energy usage in complex systems;
• Case studies on energy consumption patterns in smart cities, data centers, and autonomous systems;
• Energy optimization and evaluation of complex systems.

We invite researchers and professionals from academia, industry, and government to submit original research articles, review papers, and case studies that explore innovative approaches to the analysis and characterization of energy consumption in complex systems.

Dr. Md Rasheduzzaman
Prof. Dr. Yongming Han
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Buildings buildings	3.1	3.4	2011	15.3 Days	CHF 2600	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Processes processes	2.8	5.1	2013	14.9 Days	CHF 2400	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit
Smart Cities smartcities	7.0	11.2	2018	28.4 Days	CHF 2000	Submit

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Published Papers (3 papers)

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All Buildings Energies Processes Sustainability Smart Cities

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26 pages, 8244 KiB  

Open AccessArticle

Fuel Consumption Prediction for Full Flight Phases Toward Sustainable Aviation: A DMPSO-LSTM Model Using Quick Access Recorder (QAR) Data

by Jing Xiong, Chunling Zou, Yongbing Wan, Youchao Sun and Gang Yu

Sustainability 2025, 17(8), 3358; https://doi.org/10.3390/su17083358 - 9 Apr 2025

Viewed by 241

Abstract

Reducing emissions in the aviation industry remains a critical challenge for global low-carbon transition. Accurate fuel consumption prediction is essential to achieving emission reduction targets and advancing sustainable development in aviation. Aircraft fuel consumption is influenced by numerous complex factors during flight, resulting [...] Read more.

Reducing emissions in the aviation industry remains a critical challenge for global low-carbon transition. Accurate fuel consumption prediction is essential to achieving emission reduction targets and advancing sustainable development in aviation. Aircraft fuel consumption is influenced by numerous complex factors during flight, resulting in significant nonlinear relationships between segment-specific variables and fuel usage. Traditional statistical and econometric models struggle to capture these relationships effectively. This article first focuses on the different characteristics of QAR data and uses the Adaptive Noise Ensemble Empirical Mode Decomposition (CEEMDAN) method to obtain more significant potential features of QAR data, solving the problems of mode aliasing and uneven mode gaps that may occur in traditional decomposition methods when processing non-stationary signals. Secondly, a dynamic multidimensional particle swarm optimization algorithm (DMPSO) was constructed using an adaptive adjustment dynamic change method of inertia weight and learning factor, which solved the problem of local extremum and low search accuracy in the solution space that PSO algorithm is prone to during the optimization process. Then, a DMPSO-LSTM aircraft fuel consumption model was established to achieve fuel consumption prediction for three flight segments: climb, cruise, and descent. The final proposed model was validated on real-world datasets, and the results showed that it outperformed other baseline models such as BP, RNN, PSO-LSTM, etc. Among the results, the climbing segment MAE index decreased by more than 40%, the RMSE index decreased by more than 38%, and the R² index increased by more than 6%, respectively. The MAE index of the cruise segment decreased by more than 40%, the RMSE index decreased by more than 40%, and the R² index increased by more than 5%, respectively. The MAE index of the descending segment decreased by more than 20%, the RMSE index decreased by more than 30%, and the R² index increased by more than 5%, respectively. The improved prediction accuracy can be used to implement multi-criteria optimization in flight operations: (1) by quantifying weight–fuel relationships, it supports payload–fuel tradeoff decisions; (2) enhanced phase-specific predictions allow optimized climb/cruise profile selections, balancing time and fuel use; and (3) precise consumption estimates facilitate optimal fuel-loading decisions, minimizing safety margins. The high-precision fuel consumption prediction framework proposed in this study provides actionable insights for airlines to optimize flight operations and design low-carbon route strategies, thereby accelerating the aviation industry’s transition toward net-zero emissions. Full article

(This article belongs to the Topic Energy Consumption Analysis and Characterization of Complex Systems)

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24 pages, 5807 KiB  

Open AccessArticle

Research on the Optimized Design of Medium and Deep Ground-Source Heat Pump Systems Considering End-Load Variation

by Jianlin Li, Xupeng Qi, Xiaoli Li, Huijie Huang and Jian Gao

Sustainability 2025, 17(7), 3234; https://doi.org/10.3390/su17073234 - 4 Apr 2025

Viewed by 334

Abstract

Ground-source heat pump (GSHP) systems with medium-depth and deeply buried pipes in cold regions are highly important for addressing global climate change and the energy crisis because of their efficient, clean, and sustainable energy characteristics. However, unique geological conditions in cold climates pose [...] Read more.

Ground-source heat pump (GSHP) systems with medium-depth and deeply buried pipes in cold regions are highly important for addressing global climate change and the energy crisis because of their efficient, clean, and sustainable energy characteristics. However, unique geological conditions in cold climates pose serious challenges to the heat transfer efficiency, long-term stability, and adaptability of systems. This study comprehensively analyses the effects of various factors, including well depth, inner-to-outer tube diameter ratios, cementing material, the thermal conductivity of the inner tube, the flow rate, and the start–stop ratio, on the performance of a medium-depth coaxial borehole heat exchanger. Field tests, numerical simulations, and sensitivity analyses are combined to determine the full-cycle thermal performance and heat-transfer properties of medium-depth geological formations and their relationships with system performance. The results show that the source water temperature increases by approximately 4 °C and that the heat transfer increases by 50 kW for every 500 m increase in well depth. The optimization of the inner and outer pipe diameter ratios effectively improves the heat-exchange efficiency, and a larger pipe diameter ratio design can significantly reduce the flow resistance and improve system stability. When the thermal conductivity of the cementing cement increases from 1 W/(m·K) to 2 W/(m·K), the outlet water temperature at the source side increases by approximately 1 °C, and the heat transfer increases by 13 kW. However, the improvement effect of further increasing the thermal conductivity on the heat-exchange efficiency gradually decreases. When the flow rate is 0.7 m/s, the heat transfer is stable at approximately 250 kW, and the system economy and heat-transfer efficiency reach a balance. These findings provide a robust scientific basis for promoting medium-deep geothermal energy heating systems in cold regions and offer valuable references for the green and low-carbon transition in building heating systems. Full article

(This article belongs to the Topic Energy Consumption Analysis and Characterization of Complex Systems)

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24 pages, 1572 KiB  

Open AccessArticle

Toward Optimal Design of a Factory Air Conditioning System Based on Energy Consumption Prediction

by Shuwei Zhu, Siying Lv, Wenping Wang and Meiji Cui

Processes 2024, 12(12), 2615; https://doi.org/10.3390/pr12122615 - 21 Nov 2024

Viewed by 933

Abstract

The Make-up Air Unit (MAU) is an air conditioning system which plays an important role in serving semiconductor cleanrooms. It provides constant temperature and humidity for fresh air through various sections, including fresh air filtration, preheating, precooling, humidification, recooling, reheating, air supply, and [...] Read more.

The Make-up Air Unit (MAU) is an air conditioning system which plays an important role in serving semiconductor cleanrooms. It provides constant temperature and humidity for fresh air through various sections, including fresh air filtration, preheating, precooling, humidification, recooling, reheating, air supply, and high-efficiency filtration. However, the commonly used PID control method of the MAU indicates a deficiency in energy consumption. Hence, this research introduces a proactive energy-saving optimization control method based on machine learning and intelligent optimization algorithms. Firstly, the machine learning methods are used to train historical data of the MAU, resulting in a data-driven prediction model of energy consumption for the system. Subsequently, the customized genetic algorithm (GA) is used to optimize energy in cold and hot water systems. It facilitates the dynamic adjustment of the regulating valve opening for the cold and hot water coil in the fresh air unit, responding to real-time variations in outdoor air conditions. Meanwhile, it ensures that the supply air temperature and humidification adhere to specified requirements, thereby reducing the energy consumption associated with cold and hot water usage in the MAU. The experimental results indicate that the proposed algorithm can provide significant energy conservation in the MAU. Full article

(This article belongs to the Topic Energy Consumption Analysis and Characterization of Complex Systems)

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