Energy Use Research Articles

Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology

Air quality monitoring systems based on Internet of Things (IoT) technology are critical for addressing environmental and public health challenges, but their energy efficiency poses a significant challenge to their autonomous and scalable deployment. This study investigates strategies to enhance the energy efficiency of IoT-based air quality monitoring systems. A comprehensive analysis of sensor types, data transmission protocols, and system architectures was conducted, focusing on their energy consumption. An energy-efficient system was designed using the Smart Air sensor, Zigbee gateway, and Mini UPS, with its performance evaluated through daily energy consumption, backup operation time, and annual energy use. An integrated efficiency index (IEI) was introduced to compare sensor models based on functionality, energy efficiency, and cost. The proposed system achieves a daily energy consumption of 72 W·h, supports up to 10 h of autonomous operation during outages, and consumes 26.28 kW·h annually. The IEI analysis identified the Ajax LifeQuality as the most energy-efficient sensor, while Smart Air offers a cost-effective alternative with broader functionality. The proposed architecture and IEI provide a scalable and sustainable framework for IoT air quality monitoring, with potential applications in smart cities and residential settings. Future research should explore renewable energy integration and predictive energy management.

An Empirically Validated Framework for Automated and Personalized Residential Energy-Management Integrating Large Language Models and the Internet of Energy

The growing global demand for energy has resulted in a demand for innovative strategies for residential energy management. This study explores a novel framework—MELISSA (Modern Energy LLM-IoE Smart Solution for Automation)—that integrates Internet of Things (IoT) sensor networks with Large Language Models (LLMs) to optimize household energy consumption through intelligent automation and personalized interactions. The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. A 12-month randomized controlled trial was conducted with 100 households, which were stratified across four socioeconomic quintiles in metropolitan areas. The experimental design included the continuous collection of IoT data. Baseline energy consumption was measured and compared with post-intervention usage to assess system impact. Statistical analyses included k-means clustering, multiple linear regression, and paired t-tests. The system achieved its intended goal, with a statistically significant reduction of 5.66% in energy consumption (95% CI: 5.21–6.11%, p<0.001) relative to baseline, alongside high user satisfaction (mean = 7.81, SD = 1.24). Clustering analysis (k=4, silhouette = 0.68) revealed four distinct energy-consumption profiles. Multiple regression analysis (R2=0.68, p<0.001) identified household size, ambient temperature, and frequency of user engagement as the principal determinants of consumption. This research advances the theoretical understanding of human–AI interaction in energy management and provides robust empirical evidence of the effectiveness of LLM-mediated behavioral interventions. The findings underscore the potential of conversational AI applications in smart homes and have practical implications for optimization of residential energy use.

A state-of-the-art review on machine learning techniques for driving behavior analysis: clustering and classification approaches

Abstract Smart mobility has ushered in advanced sensing technologies. These, together with high‑level data analytics, are revolutionizing how we analyze driving behavior. Excellent performance in dealing with real-world, high-technology complexities for machine learning has made wide enthusiasm to utilize them to study driver behavior. This article gives a thorough overview of the important machine learning methods—especially clustering and classification techniques—that help analyze complex driving behaviors, predict fuel and energy use, and improve vehicle safety systems. The review specifically explains unsupervised methods like fuzzy c-means, k-means, and density-based spatial clustering of applications with noise, as well as supervised techniques such as artificial neural networks, k-nearest neighbors, and support vector machines. Also, this review discusses the integration of clustering and classification techniques with hybrid deep learning models, and examines their applications in eco-driving, energy forecasting, and intelligent transport systems while offering novel findings that contribute to more sustainable mobility. Emphasis is placed on how these methods transform vast, heterogeneous driving data into actionable insights that support real-time monitoring and personalized feedback for eco-driving and smart transportation applications. Finally, current benefits and barriers, and future research opportunities and challenges in integrating machine learning into intelligent transportation systems are reviewed. The potential to advance to safer, better, and more sustainable forms of mobility is emphasized.

Quality Management and Sustainability in the Defense Industry: Synergies and Challenges for a Sensitive Sector

Objectives: This article analyzes how quality management practices can be integrated with sustainability in the defense industry, aiming to balance effectiveness, safety, and socio-environmental responsibility in a strategic and sensitive sector. Theoretical Framework: The study is based on international standards such as ISO 9001 (quality management) and ISO 14001 (environmental management), as well as regulations like the REACH Directive, the Montreal Protocol, and the Paris Agreement. It also discusses environmental impacts and strategies such as circular economy, renewable energy use, and Kaizen methodology. Method: A mixed-methods approach (qualitative and quantitative) with a descriptive and exploratory character was adopted. Data were collected through a structured questionnaire applied to 18 employees of a Brazilian defense company, including closed-ended (Likert scale) and one open-ended question. Results and Discussion: Findings indicate a positive perception of the integration between quality and sustainability: 67% perceive strong integration, 66% report improved product quality, and 72% highlight a positive impact on institutional image. Main challenges include lack of financial resources (44%), insufficient training (17%), and limited leadership support (11%). Success cases like IMBEL, Embraer, and Helibras show that sustainable practices can reduce costs, improve efficiency, and strengthen corporate image. Research Implications: The integration of quality and sustainability is feasible and strategic, promoting innovation, efficiency, and legal compliance. Results can guide public policies and business practices in the defense sector. Originality/Value: The study shows that quality and sustainability are complementary. Its originality lies in the integrated approach applied to a traditionally change-resistant sector, offering practical paths for sustainable modernization.

The Impact of Digital Economies on Carbon Emissions and Economic Growth in Türkiye: Evidence from the FA-ARDL

Currently, digital economies, or digitalisation, are progressively infiltrating all facets of economies, from production to consumption. Consequently, alongside the impact of digitisation on economic growth, its influence on carbon emissions is also of interest. Digitalisation positively impacts economic growth while concurrently decreasing carbon emissions. Nonetheless, some contend that digitisation will elevate carbon emissions due to increased energy use. The literature does not achieve consensus regarding the effects of digitalisation on carbon emissions and economic growth. This study examines the impact of digital economies on carbon emissions and economic growth within the Turkish economy from 1996 to 2021. The research employs the ratio of internet users to the total population as an indication of the digital economy. In addition to carbon emissions and economic growth indicators, we utilise data on energy structure, government expenditures, urbanisation, and openness to foreign trade as control variables. The ADF unit root test with structural break is employed to ascertain the presence of both unit root and structural break in the study's data. We opted to employ the FA-ARDL approach, incorporating Fourier terms, to ascertain the long-term relationship among the variables. The study's findings indicate that digitisation positively influences economic growth while adversely affecting carbon emissions.

Power Flow Simulation and Thermal Performance Analysis of Electric Vehicles Under Standard Driving Cycles

This paper presents a simulation framework for evaluating power flow, energy efficiency, thermal behavior, and energy consumption in electric vehicles (EVs) under standardized driving conditions. A detailed Simulink model is developed, integrating a lithium-ion battery, inverter, permanent magnet synchronous motor (PMSM), gearbox, and a field-oriented control strategy with PI-based speed and current regulation. The framework is applied to four standard driving cycles—UDDS, HWFET, WLTP, and NEDC—to assess system performance under varied load conditions. The UDDS cycle imposes the highest thermal loads, with temperature rises of 76.5 °C (motor) and 52.0 °C (inverter). The HWFET cycle yields the highest energy efficiency, with PMSM efficiency reaching 92% and minimal SOC depletion (15%) due to its steady-speed profile. The WLTP cycle shows wide power fluctuations (−30–19.3 kW), and a motor temperature rise of 73.6 °C. The NEDC results indicate a thermal increase of 75.1 °C. Model results show good agreement with published benchmarks, with deviations generally below 5%, validating the framework’s accuracy. These findings underscore the importance of cycle-sensitive analysis in optimizing energy use and thermal management in EV powertrain design.

Human Behavior and Circulation Efficiency in High-Rise Residential Buildings

The vertical expansion of cities through high-rise residential buildings addresses urban land scarcity but presents complex challenges in circulation efficiency and human behavior. Adopting a Systematic Literature Review methodology and guided by PRISMA protocols, 23 peer-reviewed articles published between 2010 and 2024 were critically evaluated using defined inclusion and exclusion criteria. Thematic synthesis of the selected literature revealed key domains: human behavior, circulation design, user satisfaction, emergency egress, typology and density, smart technological interventions, sustainability, and research gaps. Findings show that human behavior during emergencies is often marked by delay, verification of alarms, and reliance on social cues, which can critically hinder evacuation. Psychological concepts such as “psychonomics” and stress-induced cognitive inertia were found to play major roles in movement decisions. Circulation efficiency was shown to be dependent on spatial configurations, with strategic placement of stairwells, corridor geometry, and stair rotation influencing both routine flow and evacuation speed. Moreover, user satisfaction is tied not just to safety and accessibility but also to social interaction zones and environmental comfort. Smart technologies were demonstrated to improve imovement efficiency and energy use. Gaps remain in behavioral modeling, post-occupancy evaluations, and region-specific data, particularly within African urban contexts like Nigeria. Contradictions were also noted in areas such as the use of refuge floors and elevators during emergencies, where traditional codes diverge from empirical performance data. The study concludes with policy-oriented recommendations for Nigerian building code revisions, advocating for behavior-informed design standards, mandatory POE processes, and the formalization of fire safety engineering as a discipline.

Application of Methodological Calculations of Multilayer Beams and Applications of Information Technology and Use of Solar Energy

This article discusses the use of information technology capabilities in the use of multi-layer beams in the construction of multi-story buildings using solar energy. The most important task of the construction industry is to reduce the cost of building structures and structures while ensuring the required load-bearing capacity. The use of information technology capabilities becomes especially important when using multi-layer beams in the construction of multi-story buildings. The results of the study show that there are great economic and environmental opportunities in the construction industry due to the use of solar energy in the construction of new buildings.

The use of acoustic emission technique in MWD for mine to mill approach as a smart tool for sustainable mining.

The Mine-to-Mill (MTM) approach is crucial in mining due to the high energy consumption and costs of comminution processes like crushing and grinding, which account for over 50% of total energy use. Optimizing these processes, starting from blasting, enhances efficiency and profitability. Accurate rock mass characterization is key to blasting optimization, and Monitoring While Drilling (MWD) provides real-time geotechnical data foron-the-spot adjustments. Acoustic emission monitoring, a leading MWD technique combined with intelligent models, offers promising results in rock characterization. This study employed a Support Vector Machine (SVM) model to predict rock mass properties from drilling acoustic signals. The model demonstrated high accuracy, achieving R² values of 0.976 (training) and 0.808 (testing). The Mean Absolute Percentage Error (MAPE) was 4.36% and 29.52%, while the Root Mean Squared Error (RMSE) reached 0.0486 and 0.141, the Mean Absolute Error (MAE) was 0.021 and 0.103, and the Mean Squared Error (MSE) was 0.0024 and 0.0199 for training and testing, respectively. These results confirm the model's reliability in estimating rock characteristics. Integrating acoustic emission monitoring with advanced modeling can enhance MTM strategies, reducing energy consumption, operational costs, and environmental impact in mining.

Effect of improved Establishment Technologies (ET) on growth, yield, economics and energy use in Italian millet

Minor millets are small, seeded grains with enriched minerals and nutrients over other grain crops. Besides its nutritional quality, low yielding potential and lack of available technologies restricted its cultivation area. During the last few decades, most of crop-producing activities turned to mechanization except minor millets due to less focus on minor millet research. In this experiment, various establishment technologies were evaluated through mechanization with the objective of increasing foxtail millet (Italian millet) productivity. The treatments were formulated with various land management [(Ridges & Furrow (RF); Compartmental Bunding (CB); Broad Bed Furrow (BBF); Flatbed (FB)], sowing [Line sowing (LS), Sowing by drone (SD) and Machine sowing (MS)] and irrigation methods (Modified surge irrigation (MSI), Rain gun (RG)). The Randomized Block Design (RBD) was used to lay the experiment with 3 replications during the Rabi seasons of 2023 and 2024. Among the Establishment Technologies (ET), higher plant height was measured in ET-7 at 40 DAS (74.87 cm), 60 DAS (109.33 cm) and harvest stage (114.67 cm). Higher dry matter production of foxtail millet was recorded in ET-8 at 40 DAS (3772.40 kg/ha) & CT at 60 DAS (6035.87 kg/ha) and harvest stage (7541.90 kg/ha). In yield and economics parameters, ET-6 produced higher productive tillers (4.44/plant), grain yield (2340 kg/ha) & straw yield (4516 kg/ha) and it reflected on economic indices as higher net return (Rs. 47959) and benefit-cost ratio (1.65). ET-2 recorded the lowest values across all parameters during both the studies.

Climate Change Mitigation at the Individual Level: Harnessing the 5Rs to Curb Greenhouse Gas Emissions

In the face of escalating climate change, identifying effective individual-level actions to reduce greenhouse gas (GHG) emissions has become increasingly vital. This study investigates the impact of personal choices, particularly in consumption, energy use, transportation, and diet on GHG emissions, within the framework of the 5Rs: Refuse, Reduce, Reuse, Recycle, and Recreate. The research adopts a multi-method approach, including a comprehensive review of existing literature, analysis of secondary data on individual behavioural patterns, and evaluation of government recommendations and educational content in Indian school curricula. Emissions reduction estimates for various lifestyle changes are critically examined to identify the most impactful personal strategies. The study also explores behavioural antecedents influencing residential energy conservation and sustainable transport decisions, such as mode of commuting and grocery shopping practices. Findings highlight that informed consumer choices guided by environmental education and the 5Rs framework can substantially mitigate emissions. The paper advocates for integrating individual action into broader climate strategies, particularly in the Indian context, and underscores the transformative potential of grassroots engagement in achieving climate resilience.

Priority analysis framework for equitable seismic and energy-efficiency renovation of residential buildings applied to mainland Portugal

The European construction sector faces significant challenges due to seismic vulnerabilities and widespread energy inefficiency of its residential building stock. It becomes thus crucial to prioritize regions with higher needs for building performance upgrading using frameworks that encompass single and multi-sectoral indicators. Such integrated frameworks should combine seismic risk mitigation, energy efficiency, and socioeconomic factors, to create safer and more sustainable urban environments. In line with the European strategy, Portugal has committed to carbon neutrality by 2050 and has set ambitious targets for emissions reductions by 2030. This study performs a prioritization analysis on the mainland Portuguese residential building stock combining multiple indicators related to three macro sectors, namely seismic, energy, and socioeconomic vulnerability. Using a multidisciplinary approach and the most recently available building fragility models, data on energy use, and regional socioeconomic factors, this study highlights critical regions for resource allocation and targeted renovations. The results provide valuable insights for policymakers, aiming to strengthen community seismic resilience, reduce carbon emissions, and support equitable risk mitigation measures across different regions.

The Urgency of the Need for Energy Transition in Africa: A Case Study of Ghana

Africa faces a critical energy challenge characterized by a heavy reliance on fossil fuels, limited electricity access — especially in rural areas - and underinvestment in renewable energy infrastructure. This paper examines the urgency of the energy transition across the continent, using Ghana as a case study. While nations such as Kenya and Ethiopia have advanced in their use of renewable energy, Ghana still relies heavily on fossil fuels due to challenges in infrastructure, national programs, and financing. Around 80% of the population in Ghana has access to electricity, but the disparity in the access of electricity between urban and rural areas remains an outstanding issue. This study paper calls attention to how the reliance on fossil fuels not only impacts energy security but also leads to Ghana's economic instability and environmental harm. The research paper suggests an actionable swift shift towards sustainable energy through heightened investments in renewable sources, the establishment of more robust policy frameworks, and enhanced energy efficiency to properly address this issue. For Ghana, effective strategies include broadening renewable energy capabilities, updating infrastructure, promoting private sector participation, and implementing regulatory reforms that offer support. A successful transition would foster inclusive economic development across Africa in addition to ensuring cleaner, more reliable energy.

Decarbonizing urology: carbon footprint assessment of a minimally invasive vasectomy in a French private hospital.

Decarbonizing urology: carbon footprint assessment of a minimally invasive vasectomy in a French private hospital.

Visualization and semi-quantitative analysis of dissolution processes at artificial structures in carbonate rocks using optical, 3D micro-scanning and confocal laser scanning microscopy

The Northern Alpine Foreland Basin in southeast Germany hosts more deep geothermal plants than any other region in the country. Its primary aquifer, the Upper Jurassic, is composed of permeable carbonates containing water with temperatures exceeding 150∘C in the southern margin and low total dissolved solids (≤ 2 g/L) at depths of up to 4000 m. Its sustainable use of geothermal energy depends on an efficient exploitation strategy concerning the entire reservoir, which is influenced by the development of flow paths between production and reinjection wells. The Upper Jurassic’s waters show a carbonate signature with calcium and magnesium often replaced by sodium due to ion exchange along the infiltration pathways. These waters become undersaturated upon cooling, and dissolution around reinjection wells has been previously documented. Assessing short- to medium-term localized dissolution experimentally is challenging. While dissolution kinetics and overall volume changes have been studied in the field, microscopic changes to flow paths remain less under investigation. This study used a time-lapse experiment to evaluate microscopic changes during dissolution in limestone samples exposed to elevated CO2 partial pressure in an autoclave. For an effective observation, we used artificial structures to localize the dissolution effects. Post-treatment analysis included Raman microscopy, 3D micro-scanning, confocal laser scanning microscopy (CLSM), and optical microscopy with image stacking, with a strong focus on the latter three. Each imaging method had distinct strengths and limitations. CLSM provided high-resolution surface roughness assessments but could not capture areas beneath overhangs. Optical microscopy is affordable and user-friendly and was effective for visualizing preferential dissolution pathways but lacked precise roughness information. 3D micro-scanning, despite lower resolution, uniquely resolved overhangs. The dissolution processes led to significant surface roughening, forming micrometer-scale moldic pores and preferential pathways. Artificial structures widened and deformed, with 3D micro-scanning quantifying these changes effectively and CLSM revealing fine-scale roughness details. Increased fracture surface roughness and widening of flow paths enhance water transport and dissolution, potentially accelerating thermal breakthroughs at geothermal plants. Understanding these processes is essential for predicting reservoir behavior, improving geothermal energy extraction efficiency, and exploiting aquifers sustainably.

Analyzing the impact of export tax rebates and energy conservation on sustainable industrial growth in China

When China implemented the exports tax rebate policy through administrative framework in 1985, industrial energy consumption increased by more than five times. The purpose of this study is to examine the relationship between industrial energy demand (IED), exports tax rebate, exports, and the value of industrial output in the presence of other variables such as GDP, taking into account the effects of carbon dioxide (CO2) and foreign direct investment (FDI). The present study adopted the Autoregressive-Distributed Lag (ARDL) model and Granger Casuality analysis of the VECM to examine the short run and long relationship among different variables. The empirical evidence supported the long-term cointegration of these pasrameters and demonstrated a positive impact on industrial energy consumption. Foreign direct investment reduces the need for industrial power. The conservation hypothesis between export tax credits and industrial energy use was also verified by the Granger Causality analysis of the VECM. Furthermore, export tax credits have a secondary impact on energy use in manufacturing. This research might lead to more effective legal or adminsitarive regulations and policy measures to curb China’s rising energy use in the industrial sector.

Life cycle assessment and multicriteria decision making analysis of additive manufacturing processes towards optimal performance and sustainability

The pressing need for sustainable construction materials and processes has been driving research into the optimum environmental and economic efficiency of Additive Manufacturing (AM). Most models available for Life Cycle Assessment (LCA), however, do not capture the dynamism of real-time data and the existing levels of uncertainty, and decision-making frameworks are not adaptive to evolving sets of criteria. In this paper, these described limitations are addressed through the introduction of an integrated approach that couples predictive Life Cycle Assessment (LCA) with Gaussian Process Regression (GPR), dynamic decision criteria weighting via Stochastic Forest for Multi-Criteria Decision Analysis (MCDA), and multi-objective optimization using Particle Swarm Optimization (PSO). In this study, GPR-based predictive LCA is conducted using historical and real-time environmental data for modeling impact categories of CO2 and energy use. This methodology makes estimates of not only the mean impact but also allows quantification of the uncertainties through confidence intervals and dynamic LCA. Stochastic Forest algorithm will enhance the traditional MCDA by weighting decision criteria like cost, environmental impact, and durability, in a more dynamic manner aligning to real-time manufacturing performance for better decision-making. Further, PSO will optimize material and process parameters to balance the multiple objectives of material strength, energy efficiency, and cost-effectiveness. In this way, this integrative novel approach of machine learning with bioinspired optimization contributes to the sustainability of AM. Experimental results prove that predictive accuracy can be achieved up to 85–90% by GPR, which reduces material wastage by 12%. By using Stochastic Forest, an improvement in decision accuracy can be attained to the extent of 15–20%, together with a cut in costs of about 10%. For its part, PSO optimizes design and manufacturing parameters for the materials, raising their efficiency by 10–15%, while the energy consumption goes down by 8–12%. The next framework is an important step toward integrating the steps reviewed for the development of sustainable Additive Manufacturing practices. This framework overcomes the present limitations of the LCA model by introducing dynamic predictive modeling using Gaussian Process Regression, real-time adaptive decision-making through Stochastic Forest, and multi-objective optimization through Particle Swarm Optimization. This integration of the techniques in the framework would help address the real-time data and uncertainties that are inherent for adaptive and sustainable solutions in additive manufacturing processes.

Pulsed Thulium:YAG Laser for Lithotripsy.

Pulsed Thulium:YAG Laser for Lithotripsy.

Effects of Thermostat Control on Energy Use and Thermal Comfort in Office Rooms Under Different Glazing Ratio

Thermal comfort of occupants is characterized by operative temperature (Top), while thermal environment is usually controlled by air temperature (Ta). For perimeter areas in buildings, the use of Ta in the control may lead to uncomfortable conditions. In this paper, thermostat controls based on air (TC-Ta) and Top (TC-Top) were compared in an office module based on different glazing ratio (GR) and indoor units. The results showed that, for a fan–coil system, with TC-Top, thermal comfort can be better, while for a ceiling panel system thermal comfort was similar with both controls. For fan coils, with TC-Top, Ta in offices became higher in the winter and lower in the summer, which improved thermal comfort along with increased energy use. For both GR conditions, the radiant panel could compensate for the presence of cold/warm surfaces, and it decreased the differences between the two controls, especially during cooling, which made the radiant system more suitable in large GR condition. With TC-Top, for the ceiling panel system, the increment of energy use was quite small. According to the results, under large GR, TC-Top was better for the fan–coil system to assure thermal comfort, and both control methods could be used in ceiling panel system. This study presents a comprehensive comparison of the two control strategies for both convective and radiant systems, highlighting their performance under varying GR conditions. The results also provide guidance for the optimal control of different indoor units under different GR conditions.

Study on the interactive evolution mechanism of residents’ energy use behavior in shared living spaces

Guiding residents of shared living spaces to implement energy-saving behaviors can help improve building energy efficiency and promote the realization of the Carbon Peaking and Carbon Neutrality Goals. Since there are different subjects with different energy-use behaviors in shared living spaces, exploring how these heterogeneous subjects interact with each other is the key to guiding the energy-saving behaviors of residents in shared living spaces. Therefore, based on the evolutionary game theory, this study quantifies the utility of heterogeneous subjects under different combinations of strategies from five dimensions: social, environmental, economic and comfort needs, and effort, and constructs a mechanism model of the interaction and evolution of energy-use behaviors of heterogeneous subjects in shared living spaces. Subsequently, the student dormitory is taken as an example, data are obtained through questionnaires and matlab is used to analyze the interactive evolution process of heterogeneous subjects’ energy-use behaviors, findings demonstrate that enhancing the likelihood of heterogeneous individuals adopting energy-saving strategies can be achieved through various means. When the time spent with roommates is 0–1 year, effective approaches encompass enhancing interpersonal relationships, diminishing comfort levels, heightening attention to energy-related concerns, escalating the cost associated with energy consumption, mitigating the effort required for habituating energy-use changes, bolstering perceived behavioral control, and augmenting perceived behavioral control. These efforts serve to render energy-use behaviors of heterogeneous subjects more efficacious. Furthermore, when the time spent with roommates is 1–2 years, adjustments such as judiciously reducing comfort levels or elevating the costs associated with energy consumption can be implemented. Finally, when the time spent with roommates is 2–3 years, interventions involve fostering collective consensus on energy conservation, diminishing comfort levels, or reinforcing individual norms.