Energy Consumption Analysis and Characterization of Complex Systems

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Buildings buildings	3.1	4.4	2011	15.1 Days	CHF 2600
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600
Processes processes	2.8	5.5	2013	14.9 Days	CHF 2400
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000
Sustainability sustainability	3.3	7.7	2009	17.9 Days	CHF 2400

29 pages, 2114 KB

Open AccessArticle

Determinants of Energy Consumption in South Africa: Evidence from an ARDL Model (1980–2023)

by Palesa Milliscent Lefatsa and Sanele Gumede

Energies 2026, 19(10), 2329; https://doi.org/10.3390/en19102329 - 12 May 2026

Viewed by 280

This study examines the determinants of energy consumption in South Africa over the period 1980–2023 using a multivariate time-series framework. Unlike conventional studies that focus primarily on the energy–growth nexus, this analysis incorporates financial development, industrialization, and population growth to provide a more [...] Read more.

This study examines the determinants of energy consumption in South Africa over the period 1980–2023 using a multivariate time-series framework. Unlike conventional studies that focus primarily on the energy–growth nexus, this analysis incorporates financial development, industrialization, and population growth to provide a more comprehensive understanding of energy demand dynamics. The Autoregressive Distributed Lag (ARDL) approach is employed to estimate both short-run and long-run relationships. Unit root tests confirm that all variables are integrated of order one, justifying the application of the ARDL bounds testing approach. The results reveal the existence of a stable long-run relationship between energy consumption and its determinants. Industrialization and population growth emerge as the most significant drivers of energy demand in both the short and long run, reflecting South Africa’s energy-intensive economic structure and rising demographic pressures. Financial development is found to have a positive and statistically significant effect, suggesting that improved access to credit stimulates energy consumption through increased investment and economic activity. In contrast, economic growth exhibits a positive but statistically insignificant long-run effect, indicating partial decoupling between output growth and energy demand. The error correction term is negative and statistically significant, confirming convergence to long-run equilibrium. Causality analysis further indicates that energy consumption is primarily driven by macroeconomic factors rather than acting as a leading indicator. The findings underscore the importance of industrial energy efficiency, population-responsive energy planning, and targeted financial support for sustainable energy investment. This study contributes to the literature by providing a comprehensive, country-specific analysis and offers policy-relevant insights for enhancing energy security and supporting sustainable economic development in South Africa. Full article

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

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20 pages, 3200 KB

Open AccessArticle

Experimental Wind Tunnel Study of Energy Consumption, Level Flight Speed, and Endurance of a Micro-Class UAV as a Function of Operating Weight

by Bartłomiej Dziewoński, Krzysztof Kaliszuk, Artur Kierzkowski, Jakub Jarecki and Kacper Lisowiec

Energies 2026, 19(8), 1892; https://doi.org/10.3390/en19081892 - 14 Apr 2026

Viewed by 531

Abstract

This paper presents an experimental investigation of the level flight speed and endurance characteristics of a micro-class unmanned aerial vehicle as a function of operating weight. Wind tunnel experiments were conducted to determine the aerodynamic performance and power requirements of the UAV over [...] Read more.

This paper presents an experimental investigation of the level flight speed and endurance characteristics of a micro-class unmanned aerial vehicle as a function of operating weight. Wind tunnel experiments were conducted to determine the aerodynamic performance and power requirements of the UAV over a range of operating weight configurations. The tested vehicle, a fixed-wing micro UAV, was examined under steady, level flight conditions, with particular emphasis on identifying variations in the minimum power required to sustain level flight. Measured aerodynamic forces and moments were used to derive drag polars and the corresponding power curves for each mass configuration. Based on these results, endurance estimates were obtained by coupling the experimentally derived power requirements with the characteristics of the onboard electric propulsion system. The study demonstrates a clear shift in flight speeds with increasing operating weight, as well as a reduction in achievable endurance, highlighting the sensitivity of micro-class UAV performance to mass variations, and therefore energy consumption. Full article

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

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16 pages, 4633 KB

Open AccessArticle

Application of a Multi-Objective Optimisation (MOO) via Pareto Front to the Energy Performance of a Domestic Oven

by Simona Rustico, Beatrice Bonfanti Pulvirenti and Marco Reguzzoni

Processes 2026, 14(6), 979; https://doi.org/10.3390/pr14060979 - 19 Mar 2026

Viewed by 318

Abstract

The growing demand for environmentally sustainable technologies is driving the adoption of increasingly stringent energy regulations across Europe. The residential sector is particularly impacted, not only through requirements for highly insulated buildings but also through stricter standards for household appliances. Among these, domestic [...] Read more.

The growing demand for environmentally sustainable technologies is driving the adoption of increasingly stringent energy regulations across Europe. The residential sector is particularly impacted, not only through requirements for highly insulated buildings but also through stricter standards for household appliances. Among these, domestic ovens represent a critical target, requiring manufacturers to develop technologies that support laboratory testing while reducing energy consumption. This work proposes a tool to support manufacturers during laboratory testing by applying a multi-objective optimisation approach using the Pareto front method. The code was developed in MATLAB^® and aims to minimise final consumption by acting exclusively on the management of the heating element. The results obtained from the code are first tested in the Simulink^® digital model of the oven and then through experimental testing. The results demonstrate that the proposed tool, specifically tailored for these systems, provides outcomes consistent with real operating conditions, while enabling a substantial reduction in laboratory testing time. Full article

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

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29 pages, 4624 KB

Open AccessReview

Power Consumption Analysis of the Power System in a Gigafactory: A Review

by Manzar Ilyas, Paolo Guglielmi and Andrea Mazza

Energies 2026, 19(5), 1345; https://doi.org/10.3390/en19051345 - 6 Mar 2026

Viewed by 776

Abstract

Recent decades have seen substantial growth in the demand for lithium-ion batteries (LIBs); as a result, the number of gigafactories is increasing. The power systems of these gigafactories are the most important parts of these installations, as they are directly responsible for energy [...] Read more.

Recent decades have seen substantial growth in the demand for lithium-ion batteries (LIBs); as a result, the number of gigafactories is increasing. The power systems of these gigafactories are the most important parts of these installations, as they are directly responsible for energy efficiency, operational cost, and sustainable growth of the LIB industry. This necessitates the need for comprehensive studies of power consumption in a gigafactory during the LIB manufacturing process. This paper presents a detailed review of the state-of-the-art of different parts and components of power systems in gigafactories, and power consumption estimation during cell production. This research analyzes the existing components of a power system, including power sources, different power distribution mechanisms, various power equipment, thermal management strategies, failure analysis methods, and several technologies for regenerative functions. The analysis of the above-mentioned components, systems, and technologies will enable us to understand the cumulative power consumption profile of the gigafactory, including the power consumption at each step of the production process, for normal non-production operations, like powering and lighting the facility, and for the complex and highly sophisticated power distribution system. The outcomes of this research paper highlight the importance of an optimized power system for the gigafactory, with maximum possible efficiency and minimal power losses during transmission, distribution, operational stages, and the cell formation process. This paper also helps to understand the shortcomings in existing systems and technologies, suggests improvements, and provides targets for future research directions. Full article

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

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40 pages, 9179 KB

Open AccessArticle

Cloud-Enabled Hybrid, Accurate and Robust Short-Term Electric Load Forecasting Framework for Smart Residential Buildings: Evaluation of Aggregate vs. Appliance-Level Forecasting

by Kamran Hassanpouri Baesmat, Emma E. Regentova and Yahia Baghzouz

Smart Cities 2025, 8(6), 199; https://doi.org/10.3390/smartcities8060199 - 27 Nov 2025

Viewed by 1302

Abstract

Accurate short-term load forecasting is vital for smart-city energy management, enabling real-time grid stability and sustainable demand response. This study introduces a cloud-enabled hybrid forecasting framework that integrates Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX), Random Forest (RF), and Long Short-Term [...] Read more.

Accurate short-term load forecasting is vital for smart-city energy management, enabling real-time grid stability and sustainable demand response. This study introduces a cloud-enabled hybrid forecasting framework that integrates Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX), Random Forest (RF), and Long Short-Term Memory (LSTM) models, unified through a residual-correction mechanism to capture both linear seasonal and nonlinear temporal dynamics. The framework performs fine-grained 5 min forecasting at both appliance and aggregate levels, revealing that the aggregate forecast achieves higher stability and accuracy than the sum of appliance-level predictions. To ensure operational resilience, three independent hybrid models are deployed across distinct cloud platforms with a two-out-of-three voting scheme, that guarantees continuity if a single-cloud interruption occurs. Using a real residential dataset from a house in Summerlin, Las Vegas (2022), the proposed system achieved a Root Mean Squared Logarithmic Error (RMSLE) of 0.0431 for aggregated load prediction representing a 35% improvement over the next-best model (Random Forest) and maintained consistent prediction accuracy during simulated cloud outages. These results demonstrate that the proposed framework provides a scalable, fault-tolerant, and accurate energy forecasting. Full article

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

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18 pages, 1247 KB

Open AccessArticle

Multi-Objective Sustainable Operational Optimization of Fluid Catalytic Cracking

by Shibao Pang, Yang Lin, Hongxun Shi, Rui Yin, Ran Tao, Donghong Li and Chuankun Li

Sustainability 2025, 17(22), 10045; https://doi.org/10.3390/su172210045 - 10 Nov 2025

Cited by 2 | Viewed by 973

Abstract

Fluid Catalytic Cracking (FCC) constitutes a critical process in petroleum refining, facing increasing pressure to align with sustainable development goals by improving energy efficiency and reducing environmental impact. This study tackles a multi-objective optimization challenge in FCC operations, seeking to simultaneously maximize the [...] Read more.

Fluid Catalytic Cracking (FCC) constitutes a critical process in petroleum refining, facing increasing pressure to align with sustainable development goals by improving energy efficiency and reducing environmental impact. This study tackles a multi-objective optimization challenge in FCC operations, seeking to simultaneously maximize the gasoline production and minimize the coke yield—the latter being directly linked to

C O_{2}

emissions in FCC. A data-driven optimization model leveraging a dual Long Short-Term Memory architecture is developed to capture complex relationships between operating variables and product yields. To efficiently solve the model, an Improved Multi-Objective Whale Optimization Algorithm (IMOWOA) is proposed, integrating problem-specific adaptive multi-neighborhood search and dynamic restart mechanisms. Extensive experimental evaluations demonstrate that IMOWOA achieves superior convergence characteristics and comprehensive performance compared to established multi-objective algorithms. Relative to the yields before optimization, the proposed methodology increases the gasoline yield by 0.32% on average, coupled with an average reduction of 0.11% in the coke yield. For the studied FCC unit with an annual processing capacity of 2.6 million tons, the coke reduction corresponds to an annual

C O_{2}

emission reduction of approximately 10,277 tons, delivering benefits to sustainable FCC operations. Full article

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

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19 pages, 2600 KB

Open AccessArticle

Superstructure Optimization Based on Hierarchical Accelerated Branch and Bound Algorithm and Its Application in Feedstock Scheduling

by Jian Cao, Haitao Yang and Yi Ji

Processes 2025, 13(9), 2936; https://doi.org/10.3390/pr13092936 - 15 Sep 2025

Viewed by 1044

Abstract

As modern petrochemical systems tend towards larger scales, process structures become more complex and highly integrated. The interaction and overall complexity between the operations of each unit are constantly increasing, and the solution process is becoming increasingly complex. The superstructure generated by P-graph [...] Read more.

As modern petrochemical systems tend towards larger scales, process structures become more complex and highly integrated. The interaction and overall complexity between the operations of each unit are constantly increasing, and the solution process is becoming increasingly complex. The superstructure generated by P-graph theory can reduce the generation of redundant structures, and the accelerated branch and bound algorithm can efficiently explore a vast solution space. However, this algorithm can only solve single-objective optimization problems. This article mainly focuses on the multi-objective optimization problem of the chemical process flow, with reduced production costs and the mitigation of environmental pollution being the main considerations. A novel algorithm framework based on an improved accelerated branch and bound method and incorporating the core idea of Analytic Hierarchy Process (AHP) was proposed, aiming to improve the high computational cost of traditional methods and solve the problem of high modeling costs in multi-objective optimization. The feedstock scheduling of ethylene is used to verify the effectiveness and superiority of the proposed method, with low-cost and carbon emission solutions being easily selected. This article provides a reference for the study of superstructures in chemical production processes. The experimental results show that with a small increase in energy consumption costs, the optimal solution obtained by our algorithm can reduce carbon emissions by 1.27% compared to the original solution, and reduce 2/3 of the modeling workload. Full article

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

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37 pages, 9314 KB

Open AccessArticle

A Data Imputation Approach for Missing Power Consumption Measurements in Water-Cooled Centrifugal Chillers

by Sung Won Kim and Young Il Kim

Energies 2025, 18(11), 2779; https://doi.org/10.3390/en18112779 - 27 May 2025

Cited by 2 | Viewed by 1310

Abstract

In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement system. When a substantial amount of missing data [...] Read more.

In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement system. When a substantial amount of missing data is present, the reliability of data analysis decreases, leading to potential distortions in the results. To address this issue, it is necessary to either minimize missing occurrences by utilizing high-precision measurement equipment or apply reliable imputation techniques to compensate for missing values. This study focuses on two water-cooled turbo chillers installed in Tower A, Seoul, collecting a total of 118,464 data points over 3 years and 4 months. The dataset includes chilled water inlet and outlet temperatures (

T_{1}

and

T_{2}

) and flow rate (

{\dot{V}}_{1}

) and cooling water inlet and outlet temperatures (

T_{3}

and

T_{4}

) and flow rate (

{\dot{V}}_{3}

), as well as chiller power consumption (

{\dot{W}}_{c}

). To evaluate the performance of various imputation techniques, we introduced missing values at a rate of 10–30% under the assumption of a missing-at-random (MAR) mechanism. Seven different imputation methods—mean, median, linear interpolation, multiple imputation, simple random imputation, k-nearest neighbors (KNN), and the dynamically clustered KNN (DC-KNN)—were applied, and their imputation performance was validated using MAPE and CVRMSE metrics. The DC-KNN method, developed in this study, improves upon conventional KNN imputation by integrating clustering and dynamic weighting mechanisms. The results indicate that DC-KNN achieved the highest predictive performance, with MAPE ranging from 9.74% to 10.30% and CVRMSE ranging from 12.19% to 13.43%. Finally, for the missing data recorded in July 2023, we applied the most effective DC-KNN method to generate imputed values that reflect the characteristics of the studied site, which employs an ice thermal energy storage system. Full article

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

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26 pages, 8244 KB

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

Cited by 6 | Viewed by 2635

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 KB

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

Cited by 3 | Viewed by 2090

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 KB

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

Cited by 1 | Viewed by 1811

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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