Science and Technology for Energy Transition最新文献

{"title":"Novel Multi-Port Converter for Distributed MPPT Operation in Solar PV System","authors":"N. S D Prakash Korlepara, Elanchezhian E B, Pragaspathy Subramani, Subramanian S","doi":"10.2516/stet/2024027","DOIUrl":"https://doi.org/10.2516/stet/2024027","url":null,"abstract":"Solar photovoltaic (PV) systems continue to be the most prevalent renewable energy resource despite the presence of numerous limitations. A power discrepancy between PV modules on a large scale may result in power dissipation throughout the entire PV system. This particular paper proposes an efficient multi-port converter for distributed maximum power point tracking operation (D-MPPT) for a solar PV system. The operation details of the proposed multi-port converter along with analytical waveforms are presented in this paper. To implement the D-MPPT approach in the proposed multi-port converter, a detailed analysis of mathematical modeling of solar PV systems with a mismatch of PV power and voltage stabilization approach is done. In addition, the proposed approach eliminates the need for additional current sensors and semiconductor components to overcome the effect of mismatched power in the PV system. To validate this, the prototype has been built and integrated with the real environment of the solar PV system. To verify the operation, a detailed simulation study and experimental investigation have been carried out and presented in this paper. After a detailed investigation and discussion of measured results and analysis, it is concluded that the proposed multi-port DC-DC converter is the most suitable solution for solar PV applications.\u0000 \u0000","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method to design contact-separation triboelectric nanogenerators (CS-TENG) for efficient vibrationenergy harvesting","authors":"Meriam Khelifa, Audrey Iranzo","doi":"10.2516/stet/2024026","DOIUrl":"https://doi.org/10.2516/stet/2024026","url":null,"abstract":"The necessity of self-powered electronic devices for sensing and communication (IoT) has led to the development of methods for energy harvesting. Triboelectric generators (TENG) are promising for harvesting mechanical energy from environment, in particular energy from vibrations. The optimization of the efficiency of energy transfer from vibration energy into electrical energy is a crucial problem. This paper deals with a contact-separation triboelectric nanogenerator (CS-TENG) devoted to vibration harvesting. Optimization was carried out taking into account all the parameters of the TENG connected to a load resistance. The moving electrode of the CS-TENG was supposed to be sinusoidal. After transformation into dimensionless parameters and variables intervening in the problem, it appeared that the electrical dynamics of the TENG is intrinsically determined by only two dimensionless parameters. Consequently, the optimization of efficiency can be conducted in a 2-dimensional space. It is shown that the maximum efficiency of a TENG permanently connected to the load resistance cannot be greater than 25%. However, the efficiency can be increased to nearly 100%, provided that a switch is used in series with the load resistance which opens and closes in synchronization with the electrode motion. The optimization method presented could be extended to design TENG energy harvesters operating in other modes and for arbitrary vibrations.","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"121 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140725737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Economic Energy Scheduling in Smart Cities Using Distributed Energy Resources","authors":"Awais Manzoor, Waseem Akram, Malik Ali Judge, Naveed Khan, Hasan Ali Khattak","doi":"10.2516/stet/2024025","DOIUrl":"https://doi.org/10.2516/stet/2024025","url":null,"abstract":"Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of\u0000smart cities. Whether it is healthcare monitoring, building automation, energy management, or traffic\u0000management, use cases of capability enhancement using machine learning have been significant in\u0000recent years. This paper aims to propose a modeling approach for scheduling energy consumption\u0000within smart homes, based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy\u0000management plays a significant role in reducing energy consumption and carbon emissions as com-\u0000pared to centralized energy generation. Multiple energy consumers can schedule energy-consuming\u0000household tasks using home energy management systems in coordination to reduce economic\u0000costs and greenhouse gas emissions. In this work, such a home energy management system is used\u0000to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption\u0000tasks based on a multi-objective optimization method that considers environmental and economic sustainability. There are two conflicting objectives: minimizing daily energy costs and reducing carbon\u0000dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which\u0000electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and\u0000electricity consumption are scheduled. An implementation of the proposed model is undertaken in a\u0000model smart building consisting of thirty homes under three pricing schemes. A Pareto curve illustrates the trade-off between cost and CO2 emissions","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"44 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140743604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of the Performance of Supervised Learning Algorithms for Photovoltaic System Fault Diagnosis","authors":"G. Eldeghady, Mohamed A. Moustafa Hassan, Hanan A. Kamal","doi":"10.2516/stet/2024024","DOIUrl":"https://doi.org/10.2516/stet/2024024","url":null,"abstract":"New trends were introduced in using photovoltaic (PV) energy which are mostly attributable to new laws internationally having a goal to decrease the usage of fossil fuels. The PV systems efficiency is impacted significantly by environmental factors and different faults occurrence. These faults if they weren’t rapidly identified and fixed may cause dangerous consequences. A lot of methods have been introduced in literature to detect faults that may occur in a PV system such as using I-V curves measurements, atmospheric models and statistical methods. In this paper, various machine learning techniques in particular supervised learning techniques are used for PV array failure diagnosis. The main target is the identification and categorization of several faults that may occur such as shadowing, degradation, open circuit and short circuit faults that has a great impact on PV systems performance. The results showed the techniques high ability of fault diagnosis capability. The KNN technique showed the best fault prediction performance. It achieves prediction accuracy 99.2% and 99.7% AUC-ROC score. This shows its superiority in faults prediction in PV systems over other used methods Decision Tree, Naive Bayes, and Logistic Regression.","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"93 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140752424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Fe, Ni, and Co on the hydrothermal liquefaction of Chinese herb residue for bio-oil production","authors":"Liangbei Liu, Haibin Guan, Bingtong Chen, Baofeng Zhao, Cunqing Zhong, Heming Zhang, Di Zhu, Qinghe Bao, Bari Wulan, Xiangyu Feng","doi":"10.2516/stet/2024018","DOIUrl":"https://doi.org/10.2516/stet/2024018","url":null,"abstract":"Chinese herb residue waste, characterized by its high moisture content and concentrated emissions, shares a similar chemical constitution with common lignocellulosic biomass. Therefore, it can be effectively converted into biofuel through hydrothermal liquefaction. In this study, the impact of temperature (300-360 °C), reaction time (0-60 min), and solid-liquid ratio (1:5/1:10/1:15) on the distribution of Chinese herb residue conversion products was investigated. The highest HHV achieved was 25.964 MJ/kg with a bio-oil yield of 24.57 wt.% under the experimental conditions of temperature at 330 °C for 15 min and a solid-liquid ratio of 1:10. A series of metal-loaded catalysts, Fe/MCM-41, Ni/MCM-41, and Co/MCM-41, were prepared by the impregnation method for the catalytic hydrothermal liquefaction of Chinese herb residue, and the bio-oil products were analyzed by EA, GC-MS and FT-IR. Higher bio-oil yields were obtained for the monometallic catalysts, Fe/MCM-41 (26.15 wt.%), Ni/MCM-41 (26.2 wt.%), and Co/MCM-41 (27.05 wt.%), respectively. The bimetallic catalysts showed the highest conversion of Fe-Ni/MCM-41 (84.7%) > Fe/MCM-41 (82.9%) > Fe-Co/MCM-41 (82.1%), respectively. The highest HHV of 32 MJ/kg was achieved using Ni/MCM-41 catalyst. GC-MS analysis revealed that the major constituents of bio-oil included phenols, ketones, acids, and esters. The bio-oil obtained using Fe/MCM-41 catalyst exhibited the highest relative contents of phenols and ketones at 44.13% and 24.5%. These findings demonstrate that temperature and catalyst selection play crucial roles in influencing the yield of bio-oil, as well as its chemical composition. Furthermore, the deoxidation and hydrogenation reactions were significantly enhanced by the transition metal-supported molecular sieve catalyst, leading to a notable increase in the HHV.","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"100 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal management of Li-ion batteries in electric vehicles by nanofluid-filled loop heat pipes","authors":"I. Gabsi, Imène Saad, S. Maalej, Mohamed Chaker Zaghdoudi","doi":"10.2516/stet/2024019","DOIUrl":"https://doi.org/10.2516/stet/2024019","url":null,"abstract":"An analytical model is developed to determine the thermal performance of a loop heat pipe filled with copper oxide-water and alumina-water nanofluids for battery thermal management in electric vehicles. The thermal performances of the LHP are predicted for different heat loads and nanoparticle concentrations. It is demonstrated that for fast charging operation corresponding to a heat load of 150 W, the LHP ensures evaporator temperatures of less than 60 °C for a heat sink temperature of 40 °C. The heat transport capacity of the LHP is enhanced and the evaporator temperature is deceased by augmenting the nanoparticle concentration. The water-CuO nanofluid-filled LHP performs better than the water-Al2O3 nanofluid-filled one. The addition of the nanoparticles increases the LHP total pressure drop and the driving capillary pressure. The capillary limit of the water-CuO nanofluid-filled LHP is hardly affected by CuO nanoparticle concentration until 6 % beyond which the capillary limit starts decreasing. For the water-Al2O3 nanofluid-filled LHP, the capillary limit decreases when Al2O3 nanoparticle concentration increases. Beyond 6 % Al2O3 nanoparticle concentration, the capillary limit of the Al2O3-filled LHP becomes lower than the water-filled one.","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"120 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GEOTHERMAL AND WIND ENERGY: SUSTAINABLE SOLUTIONS FOR PAKISTAN’S ENERGY ECONOMICS","authors":"Muhammad Tayyab Naqash, Qazi Umar Farooq","doi":"10.2516/stet/2024016","DOIUrl":"https://doi.org/10.2516/stet/2024016","url":null,"abstract":"The need for green energy sources like wind farms and geothermal energy has increased along with the world’s energy requirements. These developments offer reliable and clean energy with significant economic advantages, particularly for trade deficit countries like Pakistan. Geothermal and wind energy development reduces dependency on imports of fossil fuels, generates employment, and helps mitigate climate change. Pakistan struggles to meet its energy needs and economic targets like many other developing countries. Therefore, National research priorities should integrate geothermal and wind energy storage and discover relevant resources. This article summarizes how geothermal and wind energy technologies might support economic development objectives and promote sustainable energy production. The technological and financial possibilities, challenges, and opportunities, as well as the present and future of these technologies, are considered in the case study. In Pakistan, laws and policies are being established to promote renewable energy development. For example, the Alternative Energy Development Board is the public authority that encourages and enables the growth of renewable energy. \u0000This study proposes regulations to stimulate the use of renewable energy sources, promote private investment, and foster a favorable atmosphere for their growth.\u0000","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of compression ratio on the performance and emission levels of a CI engine fueled with safflower oil methyl ester through an engine simulation approach","authors":"Battal Doğan, Soroush Gholami Ghanati, Murat Kadir Yeşilyurt, H. Yaman","doi":"10.2516/stet/2024015","DOIUrl":"https://doi.org/10.2516/stet/2024015","url":null,"abstract":"In recent years, the research community has shown significant interest in the potential of biodiesel as a renewable alternative to conventional fossil-based fuels. Nevertheless, the experimental investigation of the effects of diverse biodiesel formulations on internal combustion engines demands a significant investment of time and financial resources. Consequently, the numerical alternative methodologies are advocated as a viable substitute for practical experiments. Numerical simulations offer the opportunity for a meticulous examination of the characteristics of internal combustion engines under diverse operational conditions and various biodiesel blends, thereby optimizing efficiency and cost-effectiveness. This study focused on the simulation of performance and emission characteristics of a diesel engine running on safflower (Carthamus tinctorius L.) oil methyl ester (SOME) and traditional diesel fuel using AVL simulation software. Furthermore, the simulation results were compared with a laboratory study carried out under identical conditions. The simulated engine underwent testing across various compression ratios (ranging from 12:1 to 18:1) and engine loads (from 25% to full load) while sustaining a consistent speed of 1500 rpm. The simulation findings revealed that the engine exhibited its highest BSFC as 0.495 kg/kWh with SOME fuel, at a compression ratio of 12:1, modestly lower than the corresponding experimental observation of 0.520 kg/kWh. Concurrently, the lowest value of BSFC, recorded as 0.267 kg/kWh with diesel fuel and a compression ratio of 18:1, demonstrated a marginal deviation from the experimental result of 0.281 kg/kWh. Additionally, SOME fuel usage was correlated with diminished CO and HC emissions. The experimental findings indicated the lowest value of CO and HC emissions, as 0.14% and 21.7 ppm, respectively, with SOME fuel at a compression ratio of 18:1, marginally below the simulation-derived values of 0.13% and 20.8 ppm. Conversely, diesel fuel at a compression ratio of 12:1 exhibited maximal CO and HC emissions, registering 0.38% and 199.5 ppm, respectively, in the experimental study. In comparison, the simulation values were slightly lower at 0.36% and 194.1 ppm. Moreover, the experimental investigation identified SOME fuel as yielding the highest CO2 emission, reaching a peak of 11.9% under a compression ratio of 18:1, while the simulation showed a slightly lower value of 11.2%. In contrast, diesel fuel at a compression ratio of 12:1 resulted in the lowest CO2 emission as 3.85% in the experiment, with the simulation reporting a slightly reduced value of 3.77%. Regarding NOx emissions, the experiment recorded the peak at 1687 ppm with SOME fuel and a compression ratio of 18:1, slightly surpassing the simulation's value of 1643 ppm. Conversely, the experimental data indicated the lowest NOx emission as 103 ppm with diesel fuel and a compression ratio of 12:1, with the simulation suggesting a slightly lower value of 9","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"252 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of compression ratio on the performance and emission levels of a CI engine fueled with safflower oil methyl ester through an engine simulation approach","authors":"Battal Doğan, Soroush Gholami Ghanati, Murat Kadir Yeşilyurt, H. Yaman","doi":"10.2516/stet/2024015","DOIUrl":"https://doi.org/10.2516/stet/2024015","url":null,"abstract":"In recent years, the research community has shown significant interest in the potential of biodiesel as a renewable alternative to conventional fossil-based fuels. Nevertheless, the experimental investigation of the effects of diverse biodiesel formulations on internal combustion engines demands a significant investment of time and financial resources. Consequently, the numerical alternative methodologies are advocated as a viable substitute for practical experiments. Numerical simulations offer the opportunity for a meticulous examination of the characteristics of internal combustion engines under diverse operational conditions and various biodiesel blends, thereby optimizing efficiency and cost-effectiveness. This study focused on the simulation of performance and emission characteristics of a diesel engine running on safflower (Carthamus tinctorius L.) oil methyl ester (SOME) and traditional diesel fuel using AVL simulation software. Furthermore, the simulation results were compared with a laboratory study carried out under identical conditions. The simulated engine underwent testing across various compression ratios (ranging from 12:1 to 18:1) and engine loads (from 25% to full load) while sustaining a consistent speed of 1500 rpm. The simulation findings revealed that the engine exhibited its highest BSFC as 0.495 kg/kWh with SOME fuel, at a compression ratio of 12:1, modestly lower than the corresponding experimental observation of 0.520 kg/kWh. Concurrently, the lowest value of BSFC, recorded as 0.267 kg/kWh with diesel fuel and a compression ratio of 18:1, demonstrated a marginal deviation from the experimental result of 0.281 kg/kWh. Additionally, SOME fuel usage was correlated with diminished CO and HC emissions. The experimental findings indicated the lowest value of CO and HC emissions, as 0.14% and 21.7 ppm, respectively, with SOME fuel at a compression ratio of 18:1, marginally below the simulation-derived values of 0.13% and 20.8 ppm. Conversely, diesel fuel at a compression ratio of 12:1 exhibited maximal CO and HC emissions, registering 0.38% and 199.5 ppm, respectively, in the experimental study. In comparison, the simulation values were slightly lower at 0.36% and 194.1 ppm. Moreover, the experimental investigation identified SOME fuel as yielding the highest CO2 emission, reaching a peak of 11.9% under a compression ratio of 18:1, while the simulation showed a slightly lower value of 11.2%. In contrast, diesel fuel at a compression ratio of 12:1 resulted in the lowest CO2 emission as 3.85% in the experiment, with the simulation reporting a slightly reduced value of 3.77%. Regarding NOx emissions, the experiment recorded the peak at 1687 ppm with SOME fuel and a compression ratio of 18:1, slightly surpassing the simulation's value of 1643 ppm. Conversely, the experimental data indicated the lowest NOx emission as 103 ppm with diesel fuel and a compression ratio of 12:1, with the simulation suggesting a slightly lower value of 9","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"39 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector","authors":"Gokalp Cinarer, Murat Kadir YEŞİLYURT, Ü. Ağbulut, Zeki Yilbaşi, Kazım Kılıç","doi":"10.2516/stet/2024014","DOIUrl":"https://doi.org/10.2516/stet/2024014","url":null,"abstract":"This study applies three different artificial intelligence algorithms (Multi-layer Perceptron, XGBoost, and Support Vector Machine) to estimate CO2 emissions in Türkiye's transportation sector. The input parameters considered are energy consumption (ENERGY), vehicle kilometers (VK), population (POP), year (Y), and gross domestic product per capita (GDP). Strong correlations are observed, with ENERGY having the highest correlation followed by VK, POP, Y, and GDP. Four scenarios are designed based on the correlation effect: scenario 1 (ENERGY/VK/POP/Y/GDP), scenario 2 (ENERGY/VK/POP/Y), scenario 3 (ENERGY/VK/POP), and scenario 4 (ENERGY/VK). Experiments compare their effects on CO2 emissions using statistical indicators (R2, RMSE, MSE, and MAE). Across all scenarios and algorithms, R2 values range from 0.8969 to 0.9886, and RMSE values range from 0.0333 to 0.1007. The XGBoost algorithm performs best in scenario 4. Artificial intelligence algorithms prove successful for estimating CO2 emissions. This study has significant implications for policymakers and stakeholders. It highlights the need to review energy investments in transportation and implement regulations, restrictions, legislation, and obligations to reduce emissions. Artificial intelligence algorithms offer potential for developing effective strategies. Policymakers can use these insights to prioritize sustainable energy investments. In conclusion, this study provides insights into the relationship between input parameters and CO2 emissions in the transportation sector. It emphasizes the importance of proactive measures and policies to address the sector's environmental impact. It also contributes to the understanding of AI-assisted CO2 emissions forecasting in the transport sector, potentially informing future policy decisions aimed at emission reduction and sustainable transport development.","PeriodicalId":272133,"journal":{"name":"Science and Technology for Energy Transition","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139784718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}