Energy Supply Research Articles

Impacts of HVAC cleaning on energy consumption and supply airflow: A multi-climate evaluation

Energy-efficiency interventions are crucial for sustainable building operations to accommodate emerging indoor air quality (IAQ) criteria into their engineering life cycles. While several studies have addressed building energy consumption and IAQ considerations separately, few provide integrated analysis of these aspects in response to building hygiene practices. In response, this study evaluates the effectiveness of routine heating, ventilation, and air conditioning (HVAC) cleaning on energy consumption and supply airflow patterns in non-residential public buildings. This study juxtaposes HVAC energy consumption and ventilation performance before, during and after routine HVAC cleaning, across buildings situated in four different climate zones, while operating in cooling mode. Each site had nearly identical HVAC systems serving similar architectural features and occupational loads; these were segregated into an intervention (cleaned HVAC system) that could be compared to an otherwise identically operating HVAC (control system), which was not cleaned. Following prescriptive cleaning, HVAC systems exhibited significant energy consumption reductions and delivered higher airflows compared to their uncleaned counterparts. On average, intervention systems saved between 41 % and 60 % on conveyance (fan/blower) energy, with one exception, and supplied 10 % and 46 % more airflow compared to their uncleaned counterparts. This research demonstrates how a new generation of low-cost HVAC system monitors can compile Internet of Things (IoT) archives to show immediate energy consumption benefits associated with cleaning HVAC components and their associated ductwork serving relatively high occupancy commercial and educational spaces.

Molecular basis of E93-dependent tissue morphogenesis and histolysis during insect metamorphosis.

The evolution of insect metamorphosis has profoundly influenced their successful adaptation and diversification. Two key physiological processes during insect metamorphosis are notable: wing maturation and prothoracic gland (PG) histolysis. The ecdysone-induced protein 93 (E93) is a transcription factor indispensable for metamorphosis. While it has been established that both wing maturation and PG histolysis are dependent on E93, the molecular mechanisms through which E93 regulates these seemingly 'opposing' events remain poorly understood. In this study, time-course transcriptome profiles were generated for wing pads and PGs during metamorphosis in Blattella germanica, a hemimetabolous model insect. Comparative transcriptomic analyses demonstrated that E93 exerts predominant control over extensive gene transcription during wing morphogenesis and PG histolysis. During wing morphogenesis, E93 selectively enhances the expression of genes associated with cell proliferation, energy supply, signal transduction, actin cytoskeleton organization, and cell adhesion, etc. Additionally, E93 activates the transcription of the majority of genes within the wing gene network that are crucial for wing development in B. germanica. During PG histolysis, E93 preferentially promotes the expression of genes related to endocytosis, focal adhesion, the AMPK signaling pathway, adipocytokine signaling pathway, Toll and Imd signaling pathways, and autophagy, etc. The key genes involved in the aforementioned pathways were subsequently confirmed to contribute to the E93-dependent degeneration of the PG in B. germanica. In summary, our results reveal that E93 functions as a master transcriptional regulator orchestrating both tissue morphogenesis and histolysis during insect metamorphosis. These findings contribute to a deeper understanding of the genetic underpinnings of insect metamorphosis.

Molecular ecological insights into the synergistic response mechanism of nitrogen transformation, electron flow and antibiotic resistance genes in aerobic activated sludge systems driven by sulfamethoxazole and/or trimethoprim stresses

The prevalence of antibiotics poses a serious challenge to biological nitrogen removal in wastewater. In this study, the effects of sulfamethoxazole and/or trimethoprim (15 mg/L∼30 mg/L) on treatment performance, nitrogen transformation and antibiotic resistance genes (ARGs) were investigated in aerobic activated sludge systems to elucidate the metabolic mechanism under high antibiotic stress. 15 mg/L single antibiotic stress improved total nitrogen removal performance due to the persistence of nitrifiers and enrichment of denitrifiers, with an optimum removal efficiency of 96.5%. Up-regulation of all denitrifying genes, coupled with enhanced electron transfer of Complex II and III, contributed to the emergence of aerobic denitrification. The increased expression of antioxidant genes also alleviated intracellular pressure. Whereas combined antibiotic stress induced the significant down-regulation of denitrifying bacteria and genes (nirKS and nosZ), and suppressed the electron supply for denitrification by restraining genes related to Complex Ⅰ and energy supply by tricarboxylic acid cycle, driving the collapse of activated sludge system, with ammonia and total nitrogen removal efficiencies dropping to below 40% and 20%, respectively. The dominant genera in system changed from TM7a to Thiothrix and Sphaerotilus with increasing antibiotic concentration and type. Moreover, antibiotic stress promoted a slight enrichment of ARGs, especially those encoding efflux mechanisms. Cooperative relationships (> 93%) dominated among ARGs, and Klebsiella was identified as the crucial host. ARGs regulating antibiotic efflux were more likely to be co-expressed with functional genes. These results may provide a theoretical basis for establishing promising strategies to mitigate antibiotic-caused process deterioration.

Envisioning Glucose Transporters (GLUTs and SGLTs) as Novel Intervention against Cancer: Drug Discovery Perspective and Targeting Approach.

Metabolic reprogramming and altered cellular energetics have been recently established as an important cancer hallmark. The modulation of glucose metabolism is one of the important characteristic features of metabolic reprogramming in cancer. It contributes to oncogenic progression by supporting the increased biosynthetic and bio-energetic demands of tumor cells. This oncogenic transformation consequently results in elevated expression of glucose transporters in these cells. Moreover, various cancers exhibit abnormal transporter expression patterns compared to normal tissues. Recent investigations have underlined the significance of glucose transporters in regulating cancer cell survival, proliferation, and metastasis. Abnormal regulation of these transporters, which exhibit varying affinities for hexoses, could enable cancer cells to efficiently manage their energy supply, offering a crucial edge for proliferation. Exploiting the upregulated expression of glucose transporters, GLUTs, and Sodium Linked Glucose Transporters (SGLTs), could serve as a novel therapeutic intervention for anti-cancer drug discovery as well as provide a unique targeting approach for drug delivery to specific tumor tissues. This review aims to discussthe previous and emerging research on the expression of various types of glucose transporters in tumor tissues, the role of glucose transport inhibitors as a cancer therapy intervention as well as emerging GLUT/SGLT-mediated drug delivery strategies that can be therapeutically employed to target various cancers.

The molecular code of kidney cancer: A path of discovery for gene mutation and precision therapy.

Renal cell carcinoma (RCC) is a malignant tumor with highly heterogeneous and complex molecular mechanisms. Through systematic analysis of TCGA, COSMIC and other databases, 24 mutated genes closely related to RCC were screened, including VHL, PBRM1, BAP1 and SETD2, which play key roles in signaling pathway transduction, chromatin remodeling and DNA repair. The PI3K/AKT/mTOR signaling pathway is particularly important in the pathogenesis of RCC. Mutations in genes such as PIK3CA, MTOR and PTEN are closely associated with metabolic abnormalities and tumor cell proliferation. Clinically, mTOR inhibitors and VEGF-targeted drugs have shown significant efficacy in personalized therapy. Abnormal regulation of metabolic reprogramming, especially glycolysis and glutamine metabolic pathways, provides tumor cells with continuous energy supply and survival advantages, and GLS1 inhibitors have shown promising results in preclinical studies. This paper also explores the potential of immune checkpoint inhibitors in combination with other targeted drugs, as well as the promising application of nanotechnology in drug delivery and targeted therapy. In addition, unique molecular mechanisms are revealed and individualized therapeutic strategies are explored for specific subtypes such as TFE3, TFEB rearrangement type and SDHB mutant type. The review summarizes the common gene mutations in RCC and their molecular mechanisms, emphasizes their important roles in tumor diagnosis, treatment and prognosis, and looks forward to the application prospects of multi-pathway targeted therapy, metabolic targeted therapy, immunotherapy and nanotechnology in RCC treatment, providing theoretical support and clinical guidance for individualized treatment and new drug development.

Design and optimization of two stage annular thermoelectric generator coupling with high temperature heat pipe application

Traditional energy sources have limited applications in deep space and deep-sea exploration as they cannot provide a long-term, reliable energy supply. Small nuclear reactors are ideal because of their high adaptability and long life, especially for powering deep space missions. This study focuses on integrating thermoelectric generators (TEG) with small nuclear reactors. Due to the limitation of conventional planar thermoelectric devices in conversion efficiency, a novel annular thermoelectric device (ATEG) is proposed and discussed. Compared with the traditional flat thermoelectric devices, annular thermoelectric devices have the advantages of larger contact area with heat source, smaller volume and stable heat flux. According to the application requirements of heat pipe reactor, the geometric parameters of thermoelectric conversion system are optimized in order to find the best geometric parameters and external load resistance. Through detailed investigation, we found that under certain conditions (such as 900 K hot end temperature and 10 mm device height), the single-stage SKD thermoelectric device can achieve the highest conversion efficiency of 8.99 %. Optimizing contact characteristics (e.g., surface roughness less than 2 µm, contact resistivity less than 10 microohms square centimeters) is critical to maintaining this high level of efficiency. Based on the thermoelectric characteristics of single-stage thermoelectric devices, two-stage thermoelectric devices are studied. The two-stage SKD-BT device exhibits better performance. When the height ratio of thermoelectric material is 0.7, the efficiency of the two-stage SKD-BT device is as high as 11.49 %, which is 27.8 % higher than that of the single-stage thermoelectric device. In this study, two innovative ways of integrating cascaded annular thermoelectric devices are proposed, which are simple in structure and easy to be modularized. Under the same temperature boundary condition, the thermoelectric conversion efficiency of the PN leg alternating connection structure can reach 10.95 % under the optimal load resistance, which is better than 6.45 % of the parallel connection structure.

Predicting photovoltaic greenhouse irradiance at low-latitudes of plateau based on ultra-short-term time series

Accurate and reliable ultra-short-term prediction of solar irradiance in photovoltaic (PV) greenhouses at low-latitude plateau is essential to precisely control electricity consumption of greenhouse equipment and ensure high quality crop yields. However, the irradiance in the low-latitude plateau has problems such as poor data quality, limited short-term prediction accuracy, and insufficient ability to capture nonlinear characteristics. Therefore, in order to achieve efficient utilization of photovoltaic resources, this study proposed a new hybrid integrated model TTAO-CNN-BiGRU-Attention framework to predict ultra-short-term photovoltaic greenhouse irradiance in the region. Monthly and seasonal characteristics of irradiance in low-latitude plateau areas were analyzed by statistical methods. The performance of the proposed model was verified using 9 different models for 5 different data volumes and 4 different seasons. Comprehensive analysis results show that Total radiant instantaneous(TRI) demonstrates a seasonal trend, generally low in spring, high in summer and autumn, relatively stable in autumn and winter. The monthly trend initially increases and then decreases, reaching the highest value of the year in September. The scheme proposed in this paper makes full use of the advantages of CNN, BiGRU, Attention and TTAO, greatly improving the comprehensive prediction ability of the model. In predicting different data amounts, 1 year prediction performance was the best, with RMSE, MAE, MAPE and R2 reaching 70.61 W/m2, 31 W/m2, 9.3% and 95.84%, respectively. With regard to different seasons, autumn prediction performance was the best, with RMSE, MAE, MAPE and R2 reaching 66.27 W/m2, 31.02 W/m2, 8.37% and 95.87%, respectively. The TRI prediction curve of the proposed model was closer to the actual value than other comparison models. The study found that the TTAO-CNN-BiGRU-Attention model is more accurate and stable than many traditional models in predicting ultra-short-term TRI in low-latitude plateau photovoltaic greenhouses, which can provide a reference for the comprehensive performance of PV greenhouse irradiance prediction models and precise regulation of energy supply in the future.

Glucose metabolism impairment in major depressive disorder.

Major depressive disorder (MDD) is a common mental disorder with chronic tendencies that seriously affect regular work, life, and study. However, its exact pathogenesis remains unclear. Patients with MDD experience systemic and localized impairments in glucose metabolism throughout the disease course, disrupting various processes such as glucose uptake, glycoprotein transport, glycolysis, the tricarboxylic acid cycle (TCA), and oxidative phosphorylation (OXPHOS). These impairments may result from mechanisms including insulin resistance, hyperglycemia-induced damage, oxidative stress, astrocyte abnormalities, and mitochondrial dysfunction, leading to insufficient energy supply, altered synaptic plasticity, neuronal cell death, and functional and structural damage to reward networks. These mechanical changes contribute to the pathogenesis of MDD and severely interfere with the prognosis. Herein, we summarized the impairment of glucose metabolism and its pathophysiological mechanisms in patients with MDD. In addition, we briefly discussed potential pharmacological interventions for glucose metabolism to alleviate MDD, including glucagon-like peptide-1 receptor agonists, metformin, topical insulin, liraglutide, and pioglitazone, to encourage the development of new therapeutics.

Triboelectric nanogenerator based on well-dispersed and oxide-free liquid metal-doped conductive hydrogel as self-powered wearable sensor for respiratory and thyroid cartilage signal monitoring

The poor dispersibility and easy surface oxidation of liquid metal (LM) significantly reduce the conductivity, mechanical property and stability of the composite hydrogels, thereby affecting their sensing performance. Herein, a well-dispersed and oxide-free LM-doped conductive double network (DN) hydrogel (CMC/P(AA-co-DAC)/NaCl-LM) is successfully prepared by employing bio-macromolecules (carboxymethyl cellulose (CMC) and double-bond functionalized CMC (CMC-AGE crosslinker) encapsulated oxide-free LM droplet at acidic condition as the first network and poly(acrylic acid-co-acryloyloxyethyltrimethyl ammonium chloride) (P(AA-co-DAC)) as the second network under the presence of NaCl. In this hydrogel, the multiple interactions and the synergistic effect between two interpenetrating networks insures multifunctional features of the hydrogels. As a result, the composite hydrogel exhibits superior mechanical properties (stress of 389.81 kPa and strain of 2866.44%) and high conductivity (5.27S/m). Subsequently, a triboelectric nanogenerator (CPL-TENG) is constructed using the composite hydrogel as electrode material, which displayed an excellent electrical out performance (VOC of 223.32V, ISC of 9.78 μA, QSC of 82.33 nC, and power density of 4.30W/m2) and highly-sensitive (pressure sensitivity of 0.268 kPa-1). Eventually, the as-fabricated CPL-TENG as a self-powered sensor for respiratory signal monitoring and machine learning-assisted language recognition is described and its great potential in wearable electronics and energy supply devices is foreseen.

Effect of light intensity on nitrogen removal, enzymatic activity and metabolic pathway of algal-bacterial symbiosis in rotating biological contactor treating mariculture wastewater.

An algal-bacterial symbiosis (ABS) system was developed on a rotating biological contactor treating mariculture wastewater, and its nitrogen removal, enzymatic activity and metabolic pathways were investigated under different light intensities. The nitrogen removal efficiency increased when light intensities ranged from 20 to 80 μE/(m2·s) but declined under 100 μE/(m2·s). Higher enzymatic activities under 80 μE/(m2·s) facilitated nitrogen conversion, light utilization, ATP supply and photosynthesis. Reactive oxygen species accumulation activated antioxidant pathways under 20 and 100 μE/(m2·s). Functional bacteria including Sedimentitalea, Thauera and Dechloromonas as well as Chlorella sorokinian, Dunaliella, Pleurosira laevis and Microcystis were enriched under 80 μE/(m2·s). Abundant photosynthesis-related genes (petC, Lca3/4 and atpH/A) supported energy supply and electron transport. Conversely, lower proportions of IDH3, gltB, and acnA/B under 20 and 100 μE/(m2·s) hindered tricarboxylic acid cycle, reducing NADPH and energy production. These results enhance the understanding on the effect of light intensity on ABS system treating mariculture wastewater.

Thermal characteristics of phase change heat storage process and waste heat recovery of hydrogen fuel cell: a numerical study

The utilization of waste heat recovery in fuel cells through heat storage can result in favorable energy efficiency gains and environmental advantages, while simultaneously improving the reliability of energy provision in photovoltaic systems. However, the low thermal conductivity of phase change materials dramatically hinders access to a safe, stable, and efficient renewable energy supply. To improve thermal efficiency, this paper proposes a novel triangle branch fin with straight, arc-curved, and corrugated shapes. Combined with metal nanoparticles, the heat transfer of phase change material is enhanced. A series of simulations are performed to judge the novel design based on fin shape and distribution based on the energy charging characteristics. The propagation of the melting front as well as the development of temperature and velocity during the entire process of melting are compared and analyzed. Results reveal that the straight triangle branch fin outperforms the other five cases with basic straight fin, corrugated fin, curved fin, arc-shaped fin, and corrugated branch fin regarding the melting performance, resulting in a 39.79 % reduction in melting time. The findings guide the evaluation and operation of heat storage tanks with fin enhancements.

Decision optimization in the linked electricity market at multi-timescale: Integrating coupled tradable green certificate and green power trading.

Achieving the national climate target would depend on national actions. China has implemented important market mechanisms for a green and low-carbon energy transition, including the Renewable Portfolio Standard (RPS), the Tradable Green Certificate (TGC) market, the green power trading market, and so on. However, how to effectively integrate coupled TGC and green power trading to achieve a balance between maximizing economic benefits and environmental friendliness remains to be explored. Therefore, this study extends prior research by establishing a bi-level decision optimization model to explore market participants' decision making from the perspective of energy supply and economic value, and analyzes the impact of the RPS, TGC price, and the penetration of renewable energy (RE) in the electricity market, the green power market, and the trading strategy of market participants in a multi-market equilibrium state. The feasibility and effectiveness of the bi-level decision optimization model and algorithm are verified using the example of IEEE14 node and historical data of Elia Energy. The results show that: (1) Under the market equilibrium condition, the clearing price in the day-ahead RE market is equal to the clearing price in the day-ahead traditional energy market plus the TGC price. (2) When green power participates in the spot market, there is a complementary relationship between RE generation and traditional energy generation. (3) A shift from decreasing to increasing costs for electricity consumers when RE penetration is above a certain threshold (α>30%) and the TGC price is 100 CNY/MWh, and a decrease in the growth rate of RE generators' profits when RE penetration is above a certain threshold (α>35%). (4) Traditional energy generators with small installed capacities adopt riskier market behaviors to declare more electricity and try to obtain higher profit in market transactions. These insights can make up for the research gap of decision optimization in multi-timescale electricity market, achieve energy optimization allocation and environmental sustainability.

Optimizing economic and environmental sustainability in biomass-based energy supply chains: A multi-criteria decision-making approach

Optimizing economic and environmental sustainability in biomass-based energy supply chains: A multi-criteria decision-making approach

Enhancing energy resilience in enterprises: a multi-criteria approach

Unreliable energy supply disrupts productivity and escalates costs in manufacturing enterprises, highlighting the need for robust energy resilience strategies. Existing multi-criteria decision-making (MCDM) methods, such as the analytic hierarchy process (AHP), best–worst method (BWM), and unaugmented TOPSIS, often lack advanced risk prioritization and objective criteria weighting, limiting their effectiveness in addressing complex, multidimensional energy challenges. This study proposes an integrated framework that combines Failure Mode and Effects Analysis (FMEA) for risk prioritization, the CRITIC-method for objective criteria weighting based on the 4A’s energy resilience dimensions—availability, accessibility, affordability, and acceptability—and fuzzy-TOPSIS for strategy ranking. The framework bridges theoretical rigor with operational applicability, addressing the unique challenges faced by manufacturing enterprises. Validated through a real-world case study and sensitivity analysis, it identifies actionable strategies, such as flexible scheduling, demonstrating consistent rankings across comparative analyses with fuzzy VIKOR, WASPAS, and MARCOS. This sector-specific approach advances both academic understanding and industrial practice by providing a scalable, multidimensional tool for enhancing energy resilience.

Key Technologies for Horizontal Well Development in Deep Tight Sandstone Reservoirs

ABSTRACTDeep tight sandstone oil and gas reservoirs are exerting an increasingly crucial role in the augmentation of fossil energy reserves and the provision of energy. On account of the intricate geological conditions and the deficiency of a comprehensive set of exploration and development engineering technologies as well as supporting processes, the present development of deep tight sandstone oil and gas reservoirs remains in its nascent stage. Through the analysis and generalization of the horizontal well development technology for deep tight sandstone oil and gas reservoirs, a series of technologies have been established, encompassing reservoir geological evaluation and modeling, horizontal well development reservoir engineering validation, horizontal well geological design, and enhanced oil recovery processes. By taking the C 6 reservoir in Ordos basin, China as the research subject, in light of the research outcomes regarding the damage mechanisms and potential damage factors of tight sandstone oil and gas reservoirs, a geological evaluation approach based on the well log response characteristics was constructed, clarifying the porosity and permeability features of the C 6 reservoir, establishing the numerical model of the oil reservoir, and further elaborating the methods for dividing the development layers, selecting the development well pattern, and determining the development well density. The design parameters of horizontal well‐segmented hydraulic fracturing were meticulously optimized, resulting in a minimum cluster spacing of 7 m and a maximum cluster spacing of 20 m. Given the influence of horizontal stress differences, the optimum fracturing density was ascertained to be 16 perforations per meter, and the optimal fracturing fluid volume was identified through simulation to range from 12 to 25 m3/m. The crucial technologies for the development of tight sandstone oil and gas reservoirs in horizontal wells have been clearly identified, offering theoretical direction for the efficient exploitation of deep tight sandstone oil and gas reservoirs.

The dynamic model of Iran’s electrical energy supply system based on water-food-energy-climate change nexus

PurposeEnsuring energy security and controlling the share of energy in climate change are the two main challenges of the energy sector in the path of a sustainable future. This study has designed the dynamic model of Iran’s electrical energy supply system based on the water-food-energy-climate change nexus in order to identify sustainable policies for the supply of electrical energy resources and adaptation to Iran’s climate change process.Design/methodology/approachFor this purpose, first, the system dynamics model was designed with the participation of policymakers and the supply and demand data of water-food-energy resources and the trend of climate change and economic growth in Iran. After validation, the model is simulated in the 30-year horizon (2020–2050), and according to the results of the Monte Carlo sensitivity analysis, Iran’s electric energy supply policies are based on four strategies, including (1) Electric energy supply based on electric energy supply management from non-renewable sources, (2) Development of electrical energy supply based on management of energy supply from renewable sources, (3) Electrical energy supply based on electrical energy demand management and (4) Electrical energy supply based on adaptation to climate changes. By identifying and applying the policies of each strategy separately, the model was tested and the results were compared.FindingsBased on the implementation of the combination of selected policies in the model, the following policies have been proposed: 16% development of nuclear power plants, 18% reduction in the ratio of production of gas power plants to the total production of non-renewable power plants, and an increase in the production of combined cycle power plants through conversion of gas to combined cycle, energy aggregation and development of heat recovery systems in industrial units by 32%, a decrease of 5% Energy transmission and distribution losses, per capita reduction of energy consumption from 0.926 to the global average of 0.182 (MW) per year, management of water demand in the food sector by increasing irrigation efficiency to about 85%, a 27% increase in the area of land under the irrigation network, and reducing losses Food amounting to the global average of 0.9 m tons per year.Originality/valueThe proposed model is an application of system dynamics in the field of policymaking to ensure the security of electrical energy resources, taking into account the water-food-energy-climate changes nexus. The model is a valuable tool for policymakers in planning the sustainable management of resources in the path of adapting to climate change.

APOE4 and sedentary lifestyle synergistically impair neurovascular function in the visual cortex of awake mice

Reduced cerebral blood flow occurs early in the development of Alzheimer’s disease (AD), but the factors producing this reduction are unknown. Here, we ask whether genetic and lifestyle risk factors for AD—the ε4 allele of the Apolipoprotein (APOE) gene, and physical activity—can together produce this reduction in cerebral blood flow which leads eventually to AD. Using in vivo two-photon microscopy and haemodynamic measures, we record neurovascular function from the visual cortex of physically active or sedentary mice expressing APOE3 and APOE4 in place of murine APOE. Energy supply and demand are mismatched in APOE4 mice, with smaller increases in cerebral blood flow, blood volume and blood oxygenation occurring during neuronal activation as blood vessels frequently fail to dilate. Exercise dose-dependently overall improves neurovascular function, with an increased impact of exercise apparent after longer exposure times. Several haemodynamic measures show a larger beneficial effect of exercise in APOE4 vs. APOE3 mice. Thus, APOE4 genotype in conjunction with sedentary behaviour produces the worst neurovascular function. Promotion of physical activity may therefore be particularly important to improve cerebrovascular function and reduce dementia risk in APOE4 carriers.

Bibliometric analysis of research trends and networks in carbon tax studies: Insights into environmental and economic policy implications

Carbon taxes are increasingly recognized as a critical tool in mitigating climate change by reducing carbon emissions. This study analyzes research trends in carbon tax studies using bibliometric techniques and social network analysis to explore their environmental and economic impacts and policy implications. By analyzing 922 documents from 308 sources published between 1968 and 2024, the study identifies key topics, emerging trends, and collaboration patterns among researchers. The results show a significant increase in carbon tax publications and citations, particularly from 2000 to 2019, peaking around 2017–2019, followed by a decline after 2021. The most frequently studied topics include carbon tax effects on emissions reduction, economic balance, and environmental sustainability. Social network analysis reveals influential researchers and institutions driving the discourse on carbon taxes, highlighting the importance of interdisciplinary collaboration in shaping effective policies. These findings provide insights into the evolution of carbon tax policies and underscore the need for continued research on specialized themes such as the role of carbon taxes in supply chain management, social equity, and energy policy. AcknowledgmentsThe authors would like to express their sincere gratitude to the Rector and the Dean of the Faculty of Economics and Business, Hasanuddin University for their unwavering support and invaluable assistance in this analysis. Their commitment to advancing academic research and community service has been instrumental in completing this study.

Modeling and Evaluation of Forecasting Models for Energy Production in Wind and Photovoltaic Systems

The comprehensive change from known, classical energy production methods to the increased use of renewable energy requires new methods in the field of efficient application and use of renewable energy. The urban energy supply presents complex challenges in improving efficiency; therefore, the prediction of the dynamical availability of energy is required. Several approaches have been explored, including statistical models and machine learning using historical data and numerical weather prediction models using mathematical models of the atmosphere and weather conditions. Accurately forecasting renewable energy production involves analyzing factors such as related weather conditions, conversion systems, and their locations, which influence both energy availability and yield. This study focuses on the short-term forecasting of wind and photovoltaic (PV) energy using historical data and machine learning approaches, aiming for accurate 8 h predictions. The goal is to develop models capable of producing accurate short-term forecasts of energy production from both resources (solar and wind), suitable for later use in a model predictive control scheme where generation and demand, as well as storage, must be considered together. Methods include regression trees, support vector regression, and regression neural networks. The main idea in this work is to use past and future information in the model. Inputs for the PV model are past PV generation and future solar irradiance, while the wind model uses past wind generation and future wind speed data. The performance of the model is evaluated over the entire year. Two scenarios are tested: one with perfect future predictions of wind speed and solar irradiance, and another considered realistic situation where perfect future prediction is not possible, and uncertain prediction is accounted for by incorporating noise models. The results of the second scenario were further improved using the output filtering method. This study shows the advantages and disadvantages of different methods, as well as the accuracy that can be expected in principle. The results show that the regression neural network has the best performance in predicting PV and wind generation compared to other methods, with an RMSE of 0.1809 for PV and 5.3154 for wind, and a Pearson coefficient of 0.9455 for PV and 0.9632 for wind.

Energy efficiency and indoor climatic conditions in buildings: dynamic modeling for systemic improvement

Improving the comfort of living and/or working conditions in buildings usually requires an increase in the consumption of energy and building materials, e.g., for thermal modernization, and thus additional economic costs. It is also important to note that this does not always lead to a reduction in overall emissions of pollutants into the environment, taking into account emissions at all stages of the production and use of energy and materials for energy efficiency and comfort in buildings. The paper is devoted to the problems of modelling energy supply systems for buildings, including methods and means of simulating thermal regimes of buildings and their engineering systems, as well as methods and means of modelling the operating modes of heat pumps that can be used in such systems for a coordinated improvement of energy efficiency and indoor climatic conditions in buildings. Based on the developed mathematical models, the dynamic modes of building energy supply systems are studied and recommendations for improving their efficiency are given. The dynamic models of the energy supply system is implemented in the MATLAB/Simscape software environment and the possibilities (schemes) of detailing the components of different types of sources are shown. The obtained results were verified by means of energy models created in the widely used software product EnergyPlus. It was found that, according to the quasi-steady-state method, the deviation of the energy consumption does not exceed 13% and is about 5% in dynamic modes. The possibilities of using a gas boiler and a heat pump as the most common sources of individual energy supply systems for buildings are investigated. It was found that the heat pump has a more flexible load level control. Its use can reduce the instantaneous power value by up to 40%, but the frequency of switching on the pump is much higher (about 4 times) compared to the gas boiler. If the energy sources produce the same amount of energy, the heat pump keeps the temperature within the set range more efficiently, avoiding overheating. In general, the presented energy dynamic building model and its software implementation allow a wide range of researchers to simulate different energy supply systems to ensure proper energy efficiency and indoor climate in a building.