Energy Demand Analysis Research Articles

A methodological framework to model cumulative energy demand and production costs for additive and conventional manufacturing approaches

Additive Manufacturing (AM) processes have undergone a considerable evolution in terms of industrial applicability. From prototyping applications, AM has evolved to become a competitive technology to manufacture end products in comparison with conventional manufacturing. Despite that, the environmental impact advantage of AM is limited to some production scenarios and depends on several factors: high geometric complexity, weight reduction enabled by topology optimisation, light-weight product to be assembled for transportation. Considering production cost, AM process for metal-based components proved to be often more expensive than those produced by conventional approaches. To provide new piece of knowledge in the domain of AM applicability, in this paper a comparative analysis of costs and energy demand for additive, subtractive and mass conserving manufacturing processes is presented and applied to two different Ti-6Al-4V case studies (namely, an axisymmetric and a T-shape component). Turning/milling, hot forging and EBM were analysed and compared one another. The modelling and the results were analysed with varying the product complexity, the batch size, the method for accounting for the credit arising from recycling and the extent of light-weighting obtained by AM application. The result is a framework enabling informed process design and selection with varying production scenarios.

Weather-aware energy management for unmannedaerial vehicles: a machine learning application with global data integration

This study introduces a machine learning (ML) framework to predict unmanned aerial vehicle (UAV) energy requirements under diverse environmental conditions. The framework correlates UAV flight patterns with publicly accessible weather data, to yield an energy management tool applicable to a wide range of UAV configurations. The model employs the Cross-industry standard process for data mining and advanced feature engineering, offering an in-depth analysis of meteorological factors and UAV energy demands. The study assesses several multi-regression linear and ML models, whereby ensemble models gradient boosting (GB) and eXtreme gradient boosting demonstrate superior performance and accuracy. Specifically, the GB model achieved a test mean absolute error (MAE) of 0.0395 V (V) for voltage, 0.808 A (A) for current, and 9.758 mA-hours (mAh) for discharge, with prediction accuracy of over 99.9% for voltage and discharge, and 97% for current, derived from the coefficient of determination (R2). A novel integration of real-world UAV logs and weather data underpins the development of a weather-aware ML prediction model for UAV energy consumption. Our framework is capable of concurrently predicting three components of energy and power with almost uniform accuracy, a feature not found in contemporary models. Empirical test flights show a discrepancy of only 0.005 W-hour (Wh) between total predicted and actual energy consumption. This work enhances both efficiency and safety in UAV operations. The resulting energy-predictive flight planning tool sets a new benchmark for artificial intelligence (AI) applications in intelligent automation for UAVs.

Decomposition analysis of renewable energy demand and coupling effect between renewable energy and energy demand: Evidence from China

Driving up demand for renewable energy is key to China's response to the energy crisis and to controlling carbon emissions. Several studies have been conducted to explore the factors affecting renewable energy demand. However, the literature on the importance of technological innovation in fossil energy production in affecting renewable energy demand is scant. Therefore, it is essential to explore the impact of technological innovations in fossil energy production on renewable energy demand, as well as to reveal the dynamic penetration mechanism of renewable energy in the field of energy demand, to provide policy guidelines for China to promote its energy revolution. This study decomposes renewable energy demand into 10 drivers with the extended IDA-PDA (Index Decomposition Analysis and Production-theory Decomposition Analysis) model. Then the dynamic penetration mechanism of renewable energy on the energy demand side is revealed by combining the coupled model and the decomposition results. The results show that the investment intensity effects and energy restructuring contributed the most to renewable energy demand growth, contributing 60.73 % and 79.97 % respectively. Conversely, technological advances in fossil energy combustion made the largest negative cumulative contribution to renewable energy demand growth, with a cumulative negative contribution of −39.57 %. The drivers of renewable energy demand vary considerably across types, regions and resource endowment levels, which means that regions should develop differentiated development strategies based on their own characteristics. In addition, the coupling effect between renewable energy demand and total energy demand is weakening, with market and policy effects becoming the drivers for augmenting the response of renewable energy to total energy demand. The findings of this study provide clear pathways for expanding renewable energy demand, offering important insights for the government to balance between promoting capital-driven interests and policy-driven development.

Environmental sustainability, energy efficiency and uncertainty analysis of agricultural residue-based bioethanol production: A comprehensive life cycle assessment study

Bioethanol produced from agro-residue provides a sustainable alternative to fossil fuels, mitigating pollution, reducing waste, and promoting a circular economy. However, challenges arise in sustainable sourcing, resource conflicts, water quality concerns, and managing environmental footprints to maximize agro-residue-based bioethanol benefits. The environmental impacts, net energy ratio (NER), net energy value, water footprint, and uncertainties associated with bioethanol production from agro-residues are analyzed in this study. The analysis followed a cradle-to-gate approach using 1 L of bioethanol produced as a functional unit (FU), allowing for a direct comparison of the environmental performance of the different feedstocks. The result shows that rice straw-based bioethanol exhibited higher environmental impacts, particularly in terms of ecotoxicity, ionizing radiation, human toxicity, and ozone layer depletion, except for greenhouse gas emission, which is lowest for corn stover (0.37 kg CO2 eq. per FU) and highest for cassava straw (1.1 kg CO2 eq. per FU). Furthermore, the Cumulative Energy Demand analysis highlighted a significant reliance on fossil fuels from agricultural biomass throughout the bioethanol production process. The energy balance analysis shows that the agricultural residues are feasible for bioethanol production with NER of 1.20, 3.10, and 3.28 for rice straw, corn stover, and cassava straw, respectively. To enhance the accuracy and reliability of the LCA results, Monte Carlo simulation was employed to account for uncertainty and variability in the data. This methodology provides a better understanding of the impacts of bioethanol production, with a focus on sustainability and advancements in reducing our reliance on fossil fuels.

Spatial-temporal evolution characteristics and driving factors analysis of regional energy supply and demand in China

Under the requirements of the Sustainable Development Goals, the study of China's energy development is an important reference for other countries to plan their energy policies. China is facing the problem of slow energy transformation processes and a continuously widening gap between supply and demand. Using panel data for 2012–2021, this study analyzes the characteristics of energy in China's 30 provinces, and clarifies the problems faced by each province. GM (1,1) model is employed to forecast the level of energy development from 2023 to 2030. The LMDI (Logarithmic mean Divisia index) model is utilized to analyze factors impacting energy demand in each province, according to which the design of energy development direction in each region is completed. The study finds that: (1) Overall, energy supply and demand in China are marked by “high in the west, low in the east” and “low in the west, high in the east,” respectively. The energy structure of each region is continuously optimized, but coal still has a high share. (2) The pace of clean energy development is satisfactory. However, natural gas's increase in share is relatively slight on both supply and demand sides. The substitution effect is not in line with the expectations of the transition policy. (3) Economic and investment effect are major contributing factors to increase energy demand, while the technical effect plays a significant part in reducing energy demand. A few insights from this study are provided for the formulation of transition policies in China.

LONG-TERM ENERGY DEMAND THROUGH BOTTOM-UP ANALYSIS AND PROJECTION FOR KUWAIT

In this study, an energy demand projection and analysis were conducted for the State of Kuwait, which has an oil-based economy. A bottom-up approach was used to conduct energy demand projection and analysis by utilizing the Model for Energy Demand Analysis (MAED), which is a software package developed by the International Atomic Energy Agency (IAEA). The energy demand projection was conducted until 2050, with 2019 as the base year. The total demand is expected to grow from 210.9 TWh in 2019 to 373.07 TWh in 2050. The industrial sector dominates final energy demand, with a percentage of 45.1% in 2019 and is expected to reach 55.8% by 2050. Reliance on fossil fuels for energy demand will persist with an annual growth rate of 4% throughout the projection period. The electricity demand in the household and service sectors has the highest share of space cooling during the long summer months. Policy implications in response to energy demand consumption patterns are discussed to achieve national sustainable economic growth and environmental requirements. The implementation of rules and regulations to deploy and use equipment and machinery with high energy efficiency ratings will assist in energy savings in the industrial sector. Providing incentives to use building materials with high insulation can significantly reduce the energy demand for space cooling. Setting high-efficiency rating standards for engine specifications for vehicles imported to Kuwait will help reduce the consumption of motor fuels in the transportation sector.

Cumulative energy demand and environmental impact analysis in the manufacture of recycled fiber reinforced composites panels for use in truck bodies

ABSTRACT The reuse of fiber-reinforced composite materials has become increasingly important with the tremendous waste produced by aerospace, wind turbine blades, and automobile components. Recycling fiber-reinforced composites from parts at their end-of-life provides the benefit of reinforcing strength and integrity. The life cycle assessment (LCA) method has been widely used to examine the embodied energy (EE) and the environmental impact of the manufacturing process. Two different types of composite panels made via hand layup and compression molding are considered for a comparative LCA investigation using SimaPro software. Some panels are made with a mixture of 50% virgin glass fiber (GF) and 50% polyester resin. Others are produced with a mixture of 25% virgin GF, 25% shredded recyclates from wind turbine blade, and 50% of polyester resin. Panels from the recyclates termed Recycled Glass Fiber Reinforced Polyester (rGFRP) had 2.44% lower EE as compared to their counterparts vGFRP. Ozone depletion proved to be the greatest impact on the environment as regard to the manufacturing of rGFRP (4.38E–6 kg CFC-11eq). rGFRP panels manufacture generated 3.8% less greenhouse gas emissions than the vGFRP. LCA analysis suggests that the manufacture of panels for truck body/floor using recyclates from shredded wind turbine blades involves less energy use and has less environmental impact.

Energy demand and carbon emission analyses of a solar-driven domestic regional environment mobile robot as household auxiliary heating and cooling method

Under the trend of energy saving and carbon reduction, reducing household energy consumption, which is an important part of social energy consumption, has become a hot topic. In order to solve the problem of mismatch between supply and demand of household environment control systems as well as to better meet the individual needs of users, we innovatively designed a solar-driven domestic regional environment mobile robot as auxiliary heating and cooling method. In this paper, a simplified thermal network analysis of the thermal conditions of a dwelling is utilized to calculate the energy saving and carbon reduction potential on summer and winter reference days in Beijing and Guangzhou, China. The simulation results show that the system has good energy saving potential and carbon reduction effect. In particular, the single-day energy saving potential of the system on winter days in Beijing reaches to 583.6 kWh compared to the traditional method. Under the power supply mode, the single-day carbon reduction can reach 462 kg CO2 on winter days in Guangzhou, and it can reach 333.2 kg CO2 on summer days in Beijing. We also discuss the effect of regional space size and clothing thermal resistance. As the local space size and the clothing thermal resistance increases, the energy saving potential and carbon reduction benefits of the system decrease, but the overall impact is small. The proposed mobile robot following in the vicinity of home inhabitants to help maintain their thermal comfort, can improve the efficiency of dwellings heating and cooling systems and utilize the limited solar energy resources in family area.

Dynamic modeling and energy demand analysis of chain heavy load transfer system on marine mobile platform

ABSTRACT The demand for transferring the heavy load on marine mobile platform is steadily increasing, surpassing the capabilities of the current system. This paper introduces a chain heavy load transfer system and develops its dynamic model. This paper studies the sensitivity laws of the system’s energy demand to unknown parameters and proposes an adaptive mutation particle swarm optimization (AMPSO), whose identification accuracy is verified through comparative analysis. Experimental verification confirms the reliability of the system model while studying the influence of environmental loads on the system’s energy demand. The results demonstrate that the chain transfer system meets the requirements of marine mobile platforms, and the system model incorporates the primary influencing factors. Unknown parameters significantly influence the system’s energy demand, and the displacement error after parameter identification using AMPSO is 0.00056 rad, substantially enhancing the accuracy of model calculations. The model calculations of the system closely align with experimental measurements, with a maximum displacement error not exceeding 0.004 rad. Sway exerts a greater influence on the system’s energy demand compared to tilt. When tilt and sway are superimposed, the friction torque between the load and the track results in increased input torque and power for the vertical arrangement of the system, with a maximum torque of 328.6kN·m and maximum power of 400 kW.

Optimizing energy modeling in PBF-LB/M metal additive manufacturing: a detailed analysis of resource and energy demand based on standard tensile test specimen

Additive manufacturing is an essential tool in modern production processes. Competitive quality and the increasing importance of local manufacturing have allowed companies to maintain their production despite supply chains disrupted by the pandemic. However, the rising awareness of society towards environmental and climate protection, the increasing demand for resource-efficient products, and recent developments in energy costs are leading to a rethink in manufacturing processes. Additive manufacturing offers great potential for resource saving components. This study uses standard tensile test specimens to analyze AM processes regarding energy and material flows. The results show a high dependence of energy demand on process time and are transformed into a data-based energy model. Compared to previous energy models, the accuracy can be significantly improved using model data in combination with specific and system-oriented approaches. In addition to system-related saving potentials, design-related optimization potentials can be identified. Innovative and highly resource-saving components can be designed with a design methodology adapted to the extended degrees of freedom of AM. Further saving and recycling potentials can be identified along the material flows. A long-term goal is optimizing and predicting of resource demand in additive manufacturing with a view to the entire product lifecycle.

Comprehensive assessment of green hydrogen potential in Jordan: economic, environmental and social perspectives

PurposeThis paper aims to assess the economic, environmental, policy-related and social implications of establishing green hydrogen production in Jordan.Design/methodology/approachThe comprehensive analysis has been investigated, including economic assessments, environmental impact evaluations, policy examinations and social considerations. Furthermore, the research methodology encompasses energy demand, sector, security and supply analysis, as well as an assessment of the availability of renewable energy resources.FindingsThe results indicate substantial economic benefits associated with green hydrogen production, including job creation, increased tax revenue and a reduction in energy imports. Additionally, the study identifies positive environmental impacts, such as decreased greenhouse gas emissions and air pollution. Noteworthy, two methods could be used to produce hydrogen, namely: electrolysis and thermochemical water splitting. As a recommendation, the study proposes that Jordan, particularly Aqaba, take proactive measures to foster the development of a green hydrogen industry and collaborate with international partners to exchange best practices and establish the necessary infrastructure.Originality/valueTo the best of the authors’ knowledge, this paper is among the first to provide a comprehensive perspective on the potential of green hydrogen production as a driving force for Jordan’s economy, while also benefiting the environment and society. However, the research recognizes several challenges that must be addressed to materialize green hydrogen production in Jordan, encompassing high renewable energy costs, infrastructure development requirements and community concerns. Despite these obstacles, the study asserts that the potential advantages of green hydrogen production outweigh the associated risks.

Artificial Intelligence-Driven Multi-Energy Optimization: Promoting Green Transition of Rural Energy Planning and Sustainable Energy Economy

This research contributes to the overarching objectives of achieving carbon neutrality and enhancing environmental governance by examining the role of artificial intelligence-enhanced multi-energy optimization in rural energy planning within the broader context of a sustainable energy economy. By proposing an innovative planning framework that accounts for geographical and economic disparities across rural regions, this study specifically targets the optimization of energy systems in X County of Yantai City, Y County of Luoyang City, and Z County of Lanzhou City. Furthermore, it establishes a foundation for integrating these localized approaches into broader national carbon-neutral efforts and assessments of green total factor productivity. The comparative analysis of energy demand, conservation, efficiency, and economic metrics among these counties underscores the potential of tailored solutions to significantly advance low-carbon practices in agriculture, urban development, and industry. Additionally, the insights derived from this study offer a deeper understanding of the dynamics between government and enterprise in environmental governance, empirically supporting the Porter hypothesis, which postulates that stringent environmental policies can foster innovation and competitiveness. The rural coal-coupled biomass power generation model introduced in this work represents the convergence of green economy principles and financial systems, serving as a valuable guide for decision-making in decisions aimed at sustainable consumption and production. Moreover, this research underscores the importance of resilient and adaptable energy systems, proposing a pathway for evaluating emission trading markets and promoting sustainable economic recovery strategies that align with environmental sustainability goals.

Medium-Term Energy Demand Analysis Using Machine Learning: A Case Study on a Sub-District Area of a Divisional City in Bangladesh

Medium-Term Energy Demand Analysis Using Machine Learning: A Case Study on a Sub-District Area of a Divisional City in Bangladesh

Cumulative energy demand analysis in the current manufacturing and end-of-life strategies for a polymeric composite at different fibre-matrix combinations

Fibre Reinforced Polymers (FRPs) are finding more applications in different industrial sectors. From a sustainability point of view, a component made of FRPs reduces energy consumption and CO2 emissions during its use-phase due to the material's lightweight nature. However, the production of these materials impacts the global energy demand significantly. To mitigate this impact, circular economy strategies are essential. This study focuses on a Cumulative Energy Demand (CED) analysis for different End-of-Life (EoL) strategies of FRPs components. Three EoL routes were evaluated: i.e., combustion, recycling and reforming of continuous fibres reinforced thermoplastics. Different fibres and matrices and three Fibre Volume Fractions (FVF) were taken into account. Specifically, Glass Fibres, Carbon Fibres, Polypropylene, and Polyether ether ketone were examined while FVF of 11%, 23% and 45% were evaluated. A Life Cycle Inventory data was built combining literature review and CES Edupack database. The results provided some guidelines for optimising the product's EoL phase in terms of CED reduction underlining the advantages and high competitiveness of the reforming strategy especially if high-performance matrices and/or fibres are processed. Recycling results to be a valuable EoL alternative if FRPs made by high-performance fibres and high FVF are employed while combustion is the more advisable option if low-performance matrices and fibres are used.

Exploring interconnections: A comprehensive multi-country analysis of climate change, energy demand, long-term care, and health of older adults

Challenges faced by many countries are energy insecurity, climate change, and the health and long-term care of growing numbers of older people. These challenges are increasingly intersecting with rising energy prices, aging populations, and an increased frequency and intensity of extreme climate events. This paper gives a deeper understanding of the current and predicted interconnections among these challenges through narrative-driven content and thematic analysis from workshops with a diverse group of international stakeholders from the Global North and Global South. Narratives emerged highlighting a complex nexus of interconnections and presenting critical action areas. Targeted local and global policies and interventions are needed to alleviate stress on health systems, encourage the integrated uptake of clean energy sources, and uphold social justice across all economies. Professionals can use this work to inform the design and implementation of effective interventions and increase the resilience of older adults by better preparing for systemic risks.

Numerical analysis, design and application of a decentralized ventilation system with a heat recovery unit adapting to Nordic climates: A case study in Norway

This research investigated the energy efficiency of decentralized ventilation (DV) systems in cold weather conditions in comparison to centralized ventilation (CV) systems, focusing on three locations in Norway. The study found that DV systems with higher heat recovery unit (HRU) efficiency consumed less thermal and fan energy due to shorter air distribution pathways, utilizing a hybrid natural ventilation strategy in mild climates, and simpler space zoning for ventilation. This study proposes to determine a suitable ventilation system for adapting to cold climate. These methodologies are presented in three major parts: 1) ventilation energy demands of DV and CV systems in Norway, 2) performance of the heat recovery unit in the DV system, and 3) numerical analysis of the total ventilation energy demand of the DV system in cold climates using occupant ratio and existing measured data.The results showed specific time periods when it is ideal to use a fan-assisted NV system in cold climates, and in local conditions, 4–20% per year could benefit from a fan-assisted NV system using DV technology. A DV system with 0.7 HRU efficiency can save up to 14.5% of total HVAC energy compared to a CV system. The HRU heating efficiency played a crucial role in thermal ventilation energy demands in cold weather, while the cooling efficiency had no significant effect on energy demands. However, if a hydronic heating and cooling coil is added to the DV system, it consumes more than 9.08% of energy than using a pump. The energy-saving performance of the DV system decreases significantly if its HRU efficiency is below 50%, and there is no real advantage of using the DV system over the CV system in such cases. Nonetheless, the DV system can be utilized in various working conditions to conserve energy, such as different occupancy ratios, individual room space zoning, and indoor air pollution rates.

Regression-based sensitivity analysis and multi-objective optimisation of energy performance and thermal comfort: Building envelope design in hot arid urban context

Previous research has established that energy demand of buildings in street canyons are affected by the surrounding urban physical characteristics. This suggests that it is possible to optimise the energy performance of buildings by acting on the physical urban parameters that potentially influence their overall energy consumption. Unfortunately, few studies have addressed this issue. Precisely, improving buildings thermal behaviour and energy demand pattern throughout sensitivity-based optimisation by focusing on the building’s envelope design without neglecting the effect of urban context, remains a relatively under-explored research topic. The present study attempts to fill this gap by implementing a combined method based on sensitivity analysis and multi-objective optimisation of energy demand and thermal comfort for an existing residential building subjected to hot arid climatic conditions. The proposed method utilises Latin hypercube sampling with multivariate multiple linear regression to identify the relative importance of design parameters on building performance criteria, namely, the annual energy demand and the integrated summer thermal discomfort. Non-dominated sorting genetic algorithm II (NSGA-II) is then applied to simultaneously optimise the investigated building performance criteria with regards to the identified relevant envelope parameters. The results of sensitivity analysis indicate that window characteristics, thermal insulation, and roof reflectance are the most influential factors. The optimisation results, in turn, show promising improvement potential with a wide range of design solutions that can ensure better energy performance as well as better thermal comfort. The annual energy demand could be significantly improved by 13.2% up to 49.5% while discomfort degrees could be decreased by over 26%.

Annual Energy Demand Analysis of a Lettuce Growing Plant Factory according to the Environmental Changes

Annual Energy Demand Analysis of a Lettuce Growing Plant Factory according to the Environmental Changes

Carbon footprint of agricultural groundwater pumping with energy demand and supply management analysis

Carbon footprint of agricultural groundwater pumping with energy demand and supply management analysis

Global Energy Demand and Forecasting: A Managerial Economics Perspective

A military war between Russia and Ukraine resulted from unusual military action by Russia against Ukraine. Comprehensive sanctions were imposed against Russia in the areas of energy, finance, the economy, and other areas by European and American nations. The Russian-Ukrainian wars, which have been the most significant security event in recent international politics, will surely induce changes and adjustments to European geopolitics, political order, the international rule system, as well as economic ties. This paper focuses on the study of Global energy demand and forecasting from the perspective of global management economics. This paper will show the analysis of energy demand in the form of charts and use the Reference method and Time series analysis respectively to forecast the future energy. However, human demand for energy will double in the future as population growth and other factors change.