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Abstract Background Energy demand for reproduction leads to a wide diversity of foraging and life-history strategy among wild animals, linking to a common objective to maximize reproductive success. Semelparous squid species in particular can use up to 50% of the total energy intake for reproduction. However, the energy acquisition strategy for reproduction is still a controversial issue regarding whether the squid shift in diet ontogenetically. Here we used Argentinean shortfin squid (Illex argentinus) as a case study to investigate the strategy of energy acquisition for reproduction, by analyzing energy density of the squid’s reproductive tissues including ovary, nidamental glands and oviduct eggs, and stable isotopes and fatty acids of the squid’s ovary. Results The reproductive energy (the sum of the energy accumulated in ovary, nidamental glands and oviduct eggs) increased significantly with maturation. The ovary nitrogen stable isotopes (δ15N) showed a significant increase with maturation, but the increase by maturity stage was not equal to the typical enrichment of about 3‰ per trophic level. Isotopic niche width showed an increasing trend with maturation, and isotopic niche space exhibited greater overlap at advanced maturity stages. The relative amounts of 16:0, 20:5n3 and 20:4n6 in the ovary, tracing for carnivores and top predators, increased after the onset of maturation. The overall fatty acid profiles of the ovary showed significant differences among maturity stages, but obvious overlaps were found for mature squids. Mixed-effects model results revealed that reproductive energy was positively correlated with δ15N values. The reproductive energy was also positively related to the relative amounts of 18:0 and 20:4n6, respectively tracing for herbivores and top predators. Conclusions Our results validate that the squid shifts to feed on higher trophic prey for reproduction as energy demand increases once maturation commences. However, the squid does not shift feeding habits at a trophic level but instead broadens prey spectrum, coupled with increasing intake of higher trophic prey items, to meet the energy demand for reproduction. Such energy acquisition strategy may be selected by the squid to maximize reproductive success by balancing energy intake and expenditure from foraging, warranting future studies that aim to clarify such strategy for reproduction among semelparous species.

Due to the increasing energy consumption, energy has become a constant problem in the world. Rapidly increasing population, urbanization and economic activities increase the pressure of countries on energy. In a world where consumption is increasing, energy management has become a more important and challenging issue. For this reason, it is necessary to make proper estimations that will reduce the pressure of energy demand on this issue. In order to realize the estimation of energy demand, Turkey application is carried out in this study and arithmetic optimization algorithm (AOA) which is a stochastic metaheuristic algorithm has used to for solving energy demand problem. AOA is inspired from four substantial math functions such as subtraction, multiplication, addition and division for searching process of candidate solutions. The current position update rules of AOA are not powerful enough for solving the problem dealt with this study. Therefore, an improved version of AOA named as IAOA is proposed for solving energy demand problem. In the proposed algorithm, a new position update rule is incorporated to basic AOA in order to enhanced the exploration and exploitation capability of AOA. The linear regression model is used for the estimation of the energy demand and the population, domestic product, import and export data are used in estimation process. In the proposed model, Turkey’s real data samples for the years 1979–2011 have been used, and Turkey’s long-term energy demand has been estimated for the years 2012–2030. While performing the estimation process, Turkey’s energy data of the years 1979–2011 have processed, and then Turkey’s long-term energy demand estimations are realized for three different scenarios. Firstly, the experimental results of the proposed model are analyzed, then the results are compared with different studies proposed in the literature. As a result of the comparisons, it is seen that the IAOA method has achieved better or similar results than compared methods. For this reason, it can be said that the IAOA method is competitive and successful in realizing the energy demand forecast for Turkey’s future years.

The building sector is one of the biggest consumers of electricity in India. Climate change and global warming, as one of the most serious risks to human societies, are inextricably linked to energy usage and Greenhouse gas emissions (GHG). With a population of nearly 1.38 billion, India is the world’s second largest country. The majority of Indian urbanization is involuted rather than evolved. Between 1991 and 2011, the urban population expanded by 73 percent, from 217 million to 377 million, while energy demand more than doubled from 1990 to 2009. GRIHA — Green Rating for Integrated Habitat Assessment is an indigenous building rating system developed by Ministry of New and Renewable Energy, Govt. of India. GRIHA is a rating system that compares the building’s performance to nationally accepted benchmarks. It assesses a building’s environmental performance over the course of its full life cycle. With annual electricity consumption data for both residential and commercial sectors of India, with the linear regression methodology, the electricity demand till the year 2050 was plotted. Similarly, using the data from the GRIHA council and the Reference Building model, the annual electricity savings for GRIHA certified buildings has been plotted. Using non-linear regression, the savings for the years 2030 and 2050, has been plotted. The results show a positive indication that with stricter implementation of the energy conservation policies similar to GRIHA, by the govt of India, there can be significant savings in the energy Sector.

The new trend of the circular economy encourages the preservation of the planet’s resources. In this sense, plastic recycling is one of the critical strategies in line with the circular economy. Rice husk is one of the principal agro-industrial waste in Ecuador. Its harnessing also promotes this trend. The energy demand in single screw extrusion has been studied to optimize production cost in extrusion process manufacturing. This study examined rice husk’s effect on the extrusion process’s energy demand using a recycled high-density polyethylene (rHDPE) with a tapered compression screw with dispersive and distributive mixers. Rice husk was reduced to a maximum particle size of 45μm. Two commercial compatibilizers based on Maleic anhydride-grafted polyethylene-octene elastomer (POE-g-MA) and ethylene-glycidyl methacrylate copolymer (E-g-MA) were used to enhance compatibility between the rice husk and the polymer matrix. The energy consumption was measured on the total energy of the extruder machine. The energy consumption (EC) and specific energy consumption (SEC) were measured to contrast the effect of the rice husk (3 wt%) and the addition of the compatibilizers. The SEC of RH/E-g-MA and RH/ POE-g-MA were at about 41.11% and 55.32%, respectively, lower than rHDPE. This work suggests that rHDPE/RH bio-composite processing incurs minor costs when coupling agents, processing conditions, and screws are carefully selected.

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms. [ABSTRACT FROM AUTHOR]

South Africa's energy network is under severe pressure due to low supply and overwhelming demand. With an increase in renewable energy providers, specifically wind energy, knowing how the supply can satisfy the electricity demand may relieve apprehensions. This research aims to provide insight into the wind energy supply of South Africa and question how well this supply meets the demand of South Africa. The methodology used in this work highlights the importance of access to public datasets to dispel misconceptions in the energy industry. Additionally, the work supports network planning and the arguments for increasing wind energy penetration on the South African grid. Wind profiles and the typical energy production of South African wind farms are compared to electricity demand. The geographical spacing of the operational wind farms is considered. It is observed that wind energy supply assists in the peak electricity hourly demand as well as seasonal peaks. Furthermore, South Africa's coast is analysed to determine the offshore wind power potential, where shallow and deep waters are considered. It is observed that South Africa has a high potential for offshore wind, even after losses are applied. [ABSTRACT FROM AUTHOR]

China's crude steel output has grown rapidly since 1990, accounting for more than half of worldwide production in 2019. Iron and steel industry (ISI) in China's energy consumption and carbon emissions accounted for a higher proportion. In the context of China's "carbon peak, carbon neutrality", the ISI attaches great importance to energy conservation and emission reduction. The BF-BOF long process is far from meeting the China's policy needs in terms of energy-saving and emission-reducing targets. Therefore, the short process of EAF based on scrap steel's recycling and direct reduced iron (DRI)'s production has attracted great attention. The e-p approach and scenarios analysis method were used to research the impact of scrap steel's recycling and DRI's production on energy demand and carbon emissions of China's ISI. By 2050, scenario 4 (30% DRI based on coal gasification–gas plus 70% scrap steel for EAF) will have the lowest energy consumption (1.79 × 1011 kgce) and scenario 3 (30% DRI based on hydrogen plus 70% scrap steel for EAF) will have the lowest carbon emissions (3.42 × 1011 kg). The results show that the short process of EAF based on scrap steel recycling and DRI is an extremely important approach for the sustainable development of China's ISI in the future. [ABSTRACT FROM AUTHOR]

Upgrades in building energy efficiency codes led to differences between buildings designed according to outdated codes and those with most recent requirements. In this context, our study investigates the influence of external wall thermal transmittance, thermal inertia, and orientation on energy demand (heating, cooling) and occupant thermal comfort. Simulation models of an office building were designed, varying (i) the thermal transmittance values (0.20 and 0.60 W/(m2K)), (ii) the room orientation (four cardinal directions), and (iii) the wall thermal inertia (approximately 60 kJ/(m2K) for low and 340 kJ/(m2K) for high thermal inertia. The energy demand for heating and cooling seasons was calculated for Ljubljana using EnergyPlus 9.0.0 software. The reduction of the external wall thermal transmittance value from 0.6 W/(m2K) to 0.2 W/(m2K) contributes to significant energy savings (63% for heating and 37% for cooling). Thermal inertia showed considerable potential for energy savings, especially in the cooling season (20% and 13%, depending on the external wall insulation level). In addition, the orientation proved to have a notable impact on heating and cooling demand, however not as pronounced as thermal inertia (up to 7% total energy demand). Comparison of the thermal comfort results showed that when internal air temperatures are identically controlled in all the rooms (i.e., internal air temperature is not an influencing factor), the external wall thermal transmittance, thermal inertia, and room orientation show negligible influence on the average occupant thermal comfort. The simultaneous achievement of thermally comfortable conditions in the working environment and low energy use can only be achieved by simultaneously considering the U-value and thermal inertia. [ABSTRACT FROM AUTHOR]

With the introduction of energy-efficient buildings, the importance of embodied energy in new buildings has become increasingly relevant to minimising the impact of climate change. This study compares two existing four-storey residential buildings: one building has a reinforced concrete (RC) structure and the other has a timber structure. The study's aim is to find out which building components are responsible for the largest embodied impacts and whether there are differences between the two construction methods. The specificity of the wooden building is the combined use of solid and lightweight timber elements. The methodology consists of a general life cycle assessment (LCA) and a more detailed analysis of the product stage using the eco2soft software. The heating and cooling energy demand was calculated using the WUFI Plus software with recent regional climate data sets. The results show that for both types of construction in multi-storey buildings, it is not only the superstructure that needs to be considered, but also the floor structures, which have a major influence on the embodied impact. The timber building requires less energy to maintain the indoor climate within the set temperatures. As climate change has progressed rapidly in Austria in recent years, it is recommended that the standards for climate models be updated more quickly to allow realistic prediction of thermal comfort at the design stage. [ABSTRACT FROM AUTHOR]

Despite global efforts to reduce greenhouse gas emissions, the energy sector remains a major contributor, with hydrocarbon-based resources fulfilling around 80% of energy needs. As such, there is a growing focus on identifying effective and economically feasible policy mechanisms to promote renewable energy adoption. This study focuses on the theoretical problems surrounding the adoption of renewable energy policies. The study aims to highlight the potential for sustainable growth using renewable energy in the UAE and identify the most viable policy mechanisms for enhancing grid-tied solar energy adoption using qualitative and quantitative data collection methods and the HOMER Grid software. Compared to previous research, this study contributes by identifying a unified renewable energy policy mechanism that could significantly enhance the adoption of grid-tied solar energy generation in the UAE. The study's main findings show that a unified renewable policy mechanism could enhance grid-tied solar energy adoption throughout the UAE's electricity authorities. Net metering emerges as the most efficient and economically viable policy for customers and electricity utilities. [ABSTRACT FROM AUTHOR]

The increase in environmental problems such as climate change and air pollution caused by global warming has risen the popularity of electric vehicles (EVs) used in the smart grid environment. The increasing number of EVs can affect the grid in terms of power loss and voltage bias by changing the existing demand profile. Effective predicting of EV's energy demand ensures reliability and robustness of grid use, as well as aiding investment planning and resource allocation for charging infrastructures. In this study, the electricity demand amounts in Boulder and Perth cities are modeled by Support Vector Regression, Random Forest, Gauss Process, and Multilayer Perceptron algorithms. In addition, the impact of the COVID-19 pandemic on energy demand in electric vehicles and the energy demand behavior of EV owners were analyzed. The findings reveal that electric vehicle owners usually start to charge their vehicles during the daytime, the COVID-19 pandemic causes a severe decrease in EVs energy demand, and the support vector regression (SVR) is more successful in energy demand forecasting. Furthermore, the results indicate that the decline in electricity demand during the COVID-19 pandemic caused reductions in the prediction accuracy of the SVR model (a decrease of 17.1% in training and 12.6% in test performance, P<0.001). [ABSTRACT FROM AUTHOR]

A solar heating system is designed to reduce energy consumption in a poultry farm. According to the physics and conditions of the indoor environment of the poultry building and the effect of the poultry weather conditions, the amount of 1.37 × 108 kJ/h during the year energy is required for heating. Then, by using double-glazed windows and insulation for the exterior walls of the building in the building architecture section, the amount of energy consumption is drastically reduced, and the required annual gas consumption is equal to 11,833 m3. The surface required for the collector is recommended to supply 50% of the energy from the sun with the rest from the hybrid system. The results showed that 26 m2 of a solar collector with an optimal slope of 45 degrees, and a tank volume of 440 L and a pump discharge of 1700 kg/h are required to provide 100% of energy. To receive the maximum amount of solar energy (maximum solar fraction (SF)), a collector surface equal to 30 m2 is required. However, when the economic point of view is considered, the collector surface equivalent to 26 m2 is recommended. To establish a balance, that is, 50% of the energy from the auxiliary system and the rest from the solar system, between the use of solar energy and the use of the auxiliary system, a collector area of 16 m2 is needed. Based on this, 60 photovoltaic modules, which are 10 cells in series in 6 parallel circuits, is the most optimal mode. [ABSTRACT FROM AUTHOR]

The growth of renewable energy actively takes part in decarbonizing the fossil-fuel-based energy system. It reduces carbon emissions, carbon footprint, and greenhouse gas emissions and increases clean energy. The usage of renewable resources reduces and solves several problems, such as increasing temperature, carbon footprint, greenhouse gas emissions, and energy waste. Every sector contributes to increasing the above-mentioned factors in the environment. One of the main reasons for this biodegradation and climate change is energy resources. Using renewable energy instead of fossil fuel can solve the problem. This paper aims to find open research problems about the application of renewable energy and to initiate new innovative ideas regarding renewable energy. A detailed state of the art includes trends for renewable energy resources, their theoretical evolution, and practical implementations. Methodologies used for decision analysis in renewable energy are discussed in detail. The time frame for this analysis of renewable energy is 2010 to >2022. An extensive literature review finds a huge research scope in applying renewable energy in other research, such as logistics, smart production management, and advanced inventory management. Then, major changes in the profit/cost of that system due to renewable energy can be analyzed. This research proposes some innovative new ideas related cost formulas for renewable energy for the corresponding open problems. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical programming approach for the strategic planning of hydrogen production from renewable energies and its use in electric power generation in conventional technologies. The proposed approach aims to determine the optimal selection of the different types of technologies, electrolyzers and storage units (energy and hydrogen). The approach considers the implementation of an optimization methodology to select a representative data set that characterizes the total annual demand. The economic objective aims to determine the minimum cost, which is composed of the capital costs in the acquisition of units, operating costs of such units, costs of production and transmission of energy, as well as the cost associated with the emissions generated, which is related to an environmental tax. A specific case study is presented in the Mexican peninsula and the results show that it is possible to produce hydrogen at a minimum sale price of 4200 $/tonH2, with a total cost of $5.1687 × 106 and 2.5243 × 105 tonCO2eq. In addition, the financial break-even point corresponds to a sale price of 6600 $/tonH2. The proposed model determines the trade-offs between the cost and the emissions generated. [ABSTRACT FROM AUTHOR]

The incredible increase in carbon emissions is a major global concern. Thus, academicians and policymakers at COP26 are continuously urging to devise strategies to reduce carbon and other greenhouse gas emissions. The transportation sector is a major contributor to greenhouse gas emissions in developing countries. Therefore, this study projected an increase in fossil fuel demand for transportation and corresponding carbon dioxide emission in Pakistan from 2018 to 2030 by employing the Grey model and using annual data from 2010 to 2018. Furthermore, the determinant of fossil fuel demand is modeled using an environmental sustainability model such as stochastic regression IPAT that links environmental impact as a product of population, affluence, and technology on annual time series data spanning from 1990 to 2019. The projected values of oil demand and carbon emissions reveal an increasing trend, with average annual growth rates of 12.68% and 11.45%, respectively. The fully modified ordinary least squares (FM-OLS) findings confirmed the environmental Kuznets hypothesis. The increase in population growth emerged as the major driver for oil demand and carbon dioxide emissions, while technological advancement can reduce oil demand and corresponding carbon emissions. This study urges Pakistan to switch from oil to gas and other green energies by encouraging hybrid vehicles, as the number of vehicles on the road positively impacts the transport sector's oil demand. Moreover, increasing economic growth and controlling the population growth rate by discouraging more children can be a valid policy for reducing oil demand and corresponding carbon emissions. [ABSTRACT FROM AUTHOR]

This work aimed to study the flux behavior, rejection and concentration factors, and energy demand to establish the optimal conditions during ultrafiltration of sweet buttermilk to produce ice cream. The experiments were conducted with a UF25-PAN membrane at a transmembrane pressure of 0.2, 0.35 and 0.5 MPa, and a volume reduction ratio (VRR) of 2, 3, 4, and 5. Total protein, fat, ash, and dry matter contents, phospholipid composition, and acidity of retentates and permeate were determined. The increase in the VRR led to a decrease in the permeate flux and an increase in the energy demand, rejection, and concentration factors of the main components of sweet buttermilk. The highest values of rejection and concentration factors established for fat were 98.65% and 4.93, respectively. The permeate flux and energy demand increased with the rise in the transmembrane pressure. The total phospholipids increased 2.8 times at VRR 5 compared to the initial buttermilk. The use of VRR 3 gave the best ratio between the permeate flux, the energy demand, and relatively high values of concentration and rejection factors. The use of VRR 5 will enrich the ice cream to the greatest extent to obtain a product with the highest level of biologically active substances (proteins, phospholipids, minerals). [ABSTRACT FROM AUTHOR]

The world is currently facing two socio-technical transitions: shifting to a low-carbon society, and a digital revolution. Despite some claims to the contrary, evidence suggests that spread and adoption of information and communication technology (ICT) does not automatically lead to reduction in energy demand, if this stimulates new energy-using practices or wider economic growth. Despite this policy challenge, the two transitions are often considered separately. This study examines potential drivers of reductions or increases in energy demand due to digitalization identified in recent leading global and UK net-zero transitions scenarios. We analyse the scenarios in terms of effects of digitalization on energy demand by identifying specific direct effects of ICT; indirect and rebound effects in transport and home energy use; and wider effects via economic growth. This analysis implies that the future pathways adopted for digitalization will have a significant impact on future energy demand and hence on the feasibility and acceptability of achieving net-zero goals. We find there are different assumptions and development pathways between scenarios. We also identify a need for better inclusion of behavioural effects and other social science understanding in scenarios on the one hand, and recognition that policy can affect digitalization pathways on the other. Overall, our work suggests opportunities for further research and potential for improving policy interactions between these two transitions, and stimulating greater public debate on the different framings for an ICT-driven low-carbon transition. Key policy insights Modelling and scenario development of low-carbon pathways need to pay more attention to drivers of energy demand, including direct and indirect effects of digitalization. Social science understanding of the behavioural change effects of digitalization needs to be considered in assessing energy demand changes. Different pathways of digitalization result from individual and collective choices, including technology development, business models and data protection. Policy makers should seek to promote pathways that deliver social wellbeing and local environmental benefits, not just economic gains. [ABSTRACT FROM AUTHOR]

The operation of airports is considered as particularly energy intensive and with the use of conventional energy sources, significant amounts of greenhouse gases (GHGs) are emitted, fueling the existential crisis of global warming. Hence, this study investigates the energy management system (EnMS) of Midtjyllands Airport with respect to its energy consumption, energy sources, and energy-related GHG emissions. The intention is to develop a sustainable energy strategy to close the gaps in their energy and carbon management by applying the methods of ISO 50001 EnMS and Airport Carbon Accreditation (ACA) Program. The findings reveal a total energy consumption of about 1 GWh including electricity (53%), natural gas (47%), and others (0.1%) while emitted GHGs account for in total 203 tCO2e. With regard to the developed baseline trends, the designed objectives comprise (1) net zero GHG emissions without offsetting by 2030, (2) 40% reduction in energy consumption by 2025, and (3) 40% reduction of two energy performance indicators (EnPIs) by 2030. The achievement of the objectives is summarized in a nine-point action plan including the major actions of identifying significant energy users (SEUs), improving thermal state of total building envelope and heating system, as well as replacing the current electricity and natural gas contract with a renewable electricity and biogas contract, respectively. [ABSTRACT FROM AUTHOR]

The global energy crisis and ozone layer depletion as a result of carbon emissions have increased the awareness and acceptance of renewable energy sources as an alternative form of electric power, resulting in the sizing of renewable energy sources. However, in order to properly size an energy power system, the information being addressed, such as the load demand, is critical. The Load demand data of Nile University campus is obtained from one of its power stations (PS-1) for a period of eight month. The data was measured from the bus bar of the power station using smart meters on a weekly basis. To power the university campus, the diesel generators are synchronized using Genset controllers with suitable communications interfaces and a SMA hybrid controller, which continually checks the power output of the power sources as well as the working condition of all loads in the busbar. The diesel generators are synchronized using SMA hybrid controllers and combined with the other source of the energy at a common bus bar and used to power the university campus. Additionally, carbon emission data were obtained from the PV solar system reading.

Abstract Renewable power generation from wind and solar energy is strongly dependent on the weather. To plan future sustainable energy systems that are robust to weather variability, a better understanding of why and when periods of low wind and solar power output occur is valuable. We call such periods of low wind speed and insolation “Dunkelflauten”, the German word for “dark wind lulls”. In this article, we analyse the meteorological conditions during Dunkelflauten in Germany by applying the concept of weather regimes. Weather regimes are quasi‐stationary, recurrent and persistent large‐scale circulation patterns that explain multi‐day atmospheric variability (5–15 days). We use a regime definition that allows us to distinguish four different types of blocked regimes, characterized by high‐pressure situations in the North Atlantic‐European region. We find that Dunkelflauten in Germany occur mainly in winter when the solar power output is low due to the seasonal cycle of solar irradiance and wind power output drops for several consecutive days. A high‐pressure system over Germany, associated with the European Blocking regime, is responsible for most of the Dunkelflauten. Dunkelflauten during the Greenland Blocking regime are associated with colder temperatures than usual, causing higher electricity demand, and would present a particular challenge as space heating becomes electrified in the future. Furthermore, we show that Dunkelflauten occur predominantly when a weather regime is well established and persists longer than usual. Our study provides novel insight into the occurrence and meteorological characteristics of Dunkelflauten, which is essential for planning resilient energy systems and supporting grid operators to prepare for potential shortages in supply.

The transportation sector stands out on the world stage as one of the largest consumers of energy and the trend towards electrification of the light vehicle fleet is proving to be challenging, given the impacts generated by growing demand of energy and mainly in the generation of electricity. In Brazil, the electricity generation mix comprises around 83% of renewable sources, basically composed of hydraulic, wind and biomass sources, making it reasonable to consider the electrification of the transportation sector as one of the measures to be adopted in the promotion of sector sustainability. This article contributes to energy planning through the long-term projection of the Brazilian fleet of light vehicles, and the simulation of their impacts on energy demand, in three scenarios of insertion of plug-in electric vehicles. In this sense, statistical methodologies applied to historical data series and supervised Machine Learning algorithms for curve fitting were used, as well as the well-to-wheels approach in the fuel life cycle to estimate the energy demand. The projections included three scenarios for the insertion of electric vehicles with annual increases in demand for electricity generation which would correspond to 0.6%, 0.9% and 4.2% of additional electricity generation demand in the year 2050, with reference to the year 2020. The electrification of the fleet also proved to be advantageous in terms of reducing the use of fossil fuels and promising in terms of sustainability in the transport sector. The results of these studies will provide fundamental information for energy planning, as well as for public policy decision-making.

Rising affluence and a warming climate mean that the demand for air conditioning (AC) is rising rapidly, as society adapts to climate extremes. Here we present findings from a new methodological framework to flexibly couple and emulate these growing demands into a global integrated assessment model (IAM), subsequently representing the positive feedbacks between rising temperatures, growth in cooling demand, and carbon emissions. In assessing global and regional climate change impacts on cooling energy demand, the emulator incorporates climate model uncertainties and can explore behavioural and adaptation-related assumptions on setpoint temperature and access to cooling. It is also agnostic to the emissions and climate warming trajectory, enabling the IAM to run new policy-relevant scenarios (Current Policies, 2 °C and 1.5 °C) with climate impacts that do not follow Representative Concentration Pathways. We find that climate model uncertainty has a significant effect, more than doubling the increase in electricity demand, when comparing the 95th percentile cases to the median of the climate model ensemble. Residential AC cooling energy demands are expected to increase by 150% by 2050 whilst providing universal access to AC would result in the order of a 400% increase. Depending on the region, under current policies and limited mitigation, climate change could bring in the order of 10%–20% higher cooling-related electricity demands by 2050, and approximately 50% by 2100. Set point temperature has an important moderating role—increasing internal set-point from 23 °C to 26 °C, approximately halves the growth in electricity demand, for the majority of scenarios and regions. This effect is so strong that the change in set point temperature to both residential and commercial sectors outweighs the growth in demand that would occur by providing universal access to AC by 2050 to the 40% of the global population who would otherwise not afford it.

Income and its distribution profile are important determinants of residential energy demand and carry direct implications for human well-being and climate. We explore the sensitivity of residential energy systems to income growth and distribution across shared socioeconomic pathway-representative concentration pathways scenarios using a global, integrated, multisector dynamics model, Global Change Analysis Model, which tracks national/regional household energy services and fuel choice by income decile. Nation/region energy use patterns across deciles tend to converge over time with aggregate income growth, as higher-income consumers approach satiation levels in floorspace and energy services. However, in some regions, existing within-region inequalities in energy consumption persist over time due to slow income growth in lower income groups. Due to continued differences in fuel types, lower income groups will have higher exposure to household air pollution, despite lower contributions to greenhouse gas emissions. We also find that the share of income dedicated to energy is higher for lower deciles, with strong regional differences.

We initiate the process of developing a comprehensive low energy demand (LED) innovation narrative by applying the framework ‘Functions of Innovation Systems’ (FIS) and identifying the key conditions under which technology interventions can be improved and scaled up over the next three decades to contribute to climate change mitigation. Several studies have argued that the potential for LED-focused mitigation is much larger than previously portrayed and have shown that adopting a wide variety of energy-reducing activities would achieve emissions reductions compatible with a 1.5 C temperature target. Yet, how realistic achieving such a scenario might be or what processes would need to be in place to create a pathway to a LED outcome in mid-century, remain overlooked. This study contributes to understanding LED’s mitigation potential by outlining narratives of LED innovation in three end-use sectors: industry, transport, and buildings. Our analysis relies on the FIS approach to assess three innovations in these sectors. A key insight is that the distinct characteristics of LED technology make enabling social innovations crucial for their widespread adoption. Finally, we identify a set of eight social enablers required for unlocking LED pathways.

This paper determines the best design parameters and uses conditions of lightweight aggregate concrete panels containing microencapsulated phase change materials (MPCM-LWAC panels). The main work of this paper includes the followings: (1) The fundamental properties (dry density, thermal conductivity, and specific heat capacity) of MPCM-LWAC were researched to reveal the effect of MPCM dosage on these properties. (2) A model test was carried out to quantify the effect of MPCM dosage on the thermal response of the MPCM-LWAC panel exposed to realistic climate conditions. (3) The numerical simulation was conducted to investigate the effect of MPCM dosage, panel thickness, and outdoor temperature conditions on the thermal response of the MPCM-LWAC panel, which helps to determine its optimum design parameters and use condition. The results showed that the incorporation of MPCM results in lower dry density and thermal conductivity of MPCM-LWAC but higher specific heat capacity. The more MPCM dosage in the MPCM-LWAC panel with a thickness of 35 mm, the lower the energy demand to keep a comfortable interior temperature. Most notably, when the panel thickness exceeds 105 mm, the MPCM-LWAC panel with 5% MPCM only delays the peak temperature. Moreover, the optimal use condition for MPCM-LWAC panels is an average outdoor temperature of 25 °C, which makes the energy demand attain a minimum.

Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye’s energy demand. This study utilizes population, GDP per capita, imports, and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation, and their performance was measured using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared, both with each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF, and Ridge as the base learners in the stacking ensemble model yielded the highest R-squared value, which was 0.9882, indicating its superior performance. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of the predictive model but also underscore the broader implications of this study within the framework of the United Nations’ Sustainable Development Goals (SDGs).

Uncertainties related to the energy produced and consumed in smart grids, especially in microgrids, are among the major issues for both their design and optimal management. In that context, it is essential to have representative probabilistic scenarios of these environmental uncertainties. The intensive development and massive installation of smart meters will help to better characterize local energy consumption and production in the following years. However, models representing these variables over large timescales are essential for microgrid design. In this paper, we explore a simple method based on Markov chains capable of generating a large number of probabilistic production or consumption profiles from available historical measurements. We show that the developed approach can capture the main characteristics and statistical variability of real data on both short-term and long-term scales. Moreover, the correlation between both production and demand is conserved in generated profiles with respect to historical measurements.

Teleworking offers various socio-economic benefits to the workforce, especially during major disasters. However, the holistic net energy and greenhouse gas (GHG) emissions impacts of telework remain poorly understood. This paper develops and tests a longitudinal mixed-methods approach to estimate energy and emissions in three domains: home office, transportation, and information and communications technology (ICT). A pilot study of 11 participants from Ottawa, Canada, is used to evaluate the method, while generating a rich dataset and new insights. The results show transportation, home heating and cooling account for > 94% of telework-related energy, while home office equipment, lighting and ICT account for the remaining 6% (and < 2% of GHG emissions). Not including employer offices, teleworking will likely yield a net reduction in energy and GHG emissions compared with conventional working arrangements, but this result is dependent on personal choices, routines, purchasing decisions and household structure. The paper concludes with a discussion and future recommendations for the developed method based on the lessons learned. Practice relevance A new mixed-methods approach was developed and piloted to study the holistic energy impacts of teleworking. This demonstrates measurement tools, data analysis measures and scenario modelling. It provides lessons learned and acknowledges limitations. It is a major step forward in setting the stage for larger scale studies. The specific results showed that compared with conventional working arrangements, nine of the 11 participants are likely to consume less energy and produce fewer GHG emissions when teleworking based on a scenario-based analytical approach. However, if workers use sustainable transportation, teleworking may not yield any energy savings as increases in the home domain are expected. Future studies should include the employer offices.

Space cooling is the fastest-growing energy end-use in buildings worldwide, and Ecuador is no exception. Nevertheless, the last update of the Minimum Energy Performance Standards (MEPS) for air conditioners was in 2013 (EER 3.2W/W); since then, no new standards have been proposed in Ecuador. This study is the first assessment of stricter MEPS and estimation of benefits for the consumers and society of the residential sector in Guayaquil, Ecuador. The life cycle cost, payback time, net present value, electricity savings, and CO2 mitigation are the outputs from the Policy Analysis Modeling System (PAMS) methodology followed. The analysis considers future economic scenarios until 2035. Also, a new engineering approach based on linear optimization defines ACs designs in compliance with the proposed MEPS at the lowest cost. Therefore we can avoid setting less ambitious energy-efficiency targets when efficiency options are limited in the market (this is the Ecuadorian case). The analyzed MEPS are those proposed by UNEP and by the renewal program of inefficient equipment of the Ecuadorian Government. Our estimates show that AC demand can reach 17.3% of the total residential electricity demand in the business-as-usual scenario and 21.4% in the high economic growth scenario until 2035.Furthermore, the results show a significant gap between the proposed MEPS which can be progressively bridged. The best standard from the consumer perspective is EER 4.3W/W, while from the societal perspective, it is EER 5.5W/W. Stricter MEPS can reduce AC electricity demand and energy-related emissions between 5.7% and 31%, depending on the selected scenario. Therefore, stricter MEPS for AC represents a cost-effective option to reduce energy needs and emissions from air conditioners and a concrete action to support the national energy efficiency policies and nationally determined contributions for the residential sector.

For load forecasting, numerous machine learning (ML) approaches have been published. Besides fully connected feedforward neural networks (FFNNs), also called multilayer perceptron, more advanced ML approaches like deep, recurrent or convolutional neural networks or ensemble methods have been applied. However, evaluating the added benefit by novel approaches is difficult. Statistical or rule-based methods constitute a too low benchmark. FFNNs need extensive tuning due to their manifold design choices. To address this issue, a structured, comprehensible five-step FFNN model creation methodology is presented, which constitutes of initial model creation, internal parameter selection, feature engineering, architecture tuning and final model creation. The methodology is then applied to forecast real world heat load data of a hospital in Germany. The forecast constitutes of 192 values (upcoming 48 h in 15 min resolution) and is composed of a multi-model univariate forecasting strategy, with three test models developed at first. As a result, the test models show great similarities which simplifies creation of the remaining models. A performance increase of up to 18% between initial and final models points out the importance of model tuning. As a conclusion, comprehensible model tuning is vital to use FFNN models as benchmark. The effort needed can be reduced by the experience gained through repeated application of the presented methodology. [ABSTRACT FROM AUTHOR]

A growing body of research shows that retrofitting practices can potentially reduce energy demand in hospital buildings and enhance indoor air quality. Yet, there is a lack of comprehensive reviews associated with green retrofitting practices in hospital buildings. This study aimed to undertake a systematic review of the past literature on retrofitting techniques used to improve indoor air quality and energy efficiency in hospital buildings, to identify barriers to its uptake, and to recommend solutions to identified challenges. For this reason, a systematic review was conducted for the published research from various academic databases. Findings showed a growing uptake of various retrofitting strategies for indoor air quality in hospital buildings. As a result, hospital management or building owners might consider addressing these challenges to facilitate the uptake and implementation of retrofitting practices in their facilities. [ABSTRACT FROM AUTHOR]

This paper investigates relative aggregate energy efficiency for a panel of 39 developing countries by econometrically estimating an energy-demand function (EDF) using the stochastic frontier analysis (SFA) approach to provide relative energy efficiency scores over the period 1989 to 2008. Energy efficiency is arguably difficult to define or even conceptualise with several interpretations in the literature but here it is based on an economists' perspective of efficiency. Hence, the estimates of 'true' energy efficiency found in the paper using this approach approximate the economically efficient use of energy capturing both technical and allocative efficiency and the results confirm that energy intensity should not be considered as a de facto standard indicator of energy efficiency. While, by controlling for a range of socio-economic factors, the measurements of energy efficiency obtained by the analysis are deemed more appropriate and hence it is argued that this analysis should be undertaken to avoid potentially misleading advice to policy makers. This study contributes to the literature since it is, as far as is known, the first attempt to apply the benchmarking parametric stochastic frontier technique to econometrically estimate energy efficiency for a large panel of only developing counties around the world. Moreover, the results from such analysis are arguably particularly relevant in a world dominated by environmental concerns, especially in the aftermath of energy price increase as a result of the unrest in Ukraine. [ABSTRACT FROM AUTHOR]

This study examines the design of food rations for survival at sea required by the International Maritime Organization through the Life Saving Appliances (LSA) Code, as implemented by Transport Canada. Energy demand and body fat reserves were estimated for the Canadian population based on demographic and anthropometric data. It was determined that caloric content of food rations could be drastically decreased without potential harm to survivors of a marine abandonment. Coupled with ration reformulation, such decrease could be an opportunity to extend safe survival conditions to up to 5 days versus 2 days as currently provisioned by the LSA Code. [ABSTRACT FROM AUTHOR]

Study of development prospects of the country's economy and energy requires long-term forecasting of energy demand. Such forecasting is particularly complex due to, among other things, uncertainty of economic and political conditions and technological development, as well as increasing ambiguity and variability of the relevant influencing factors and trends. The article provides an analysis of the current state, long-term trends, and new directions of energy use in large sectors of the Russian economy (industry, household, transport), including comparisons with other countries. The applied methodological approach to forecasting energy demand can be adjusted for promising structural and technological changes in industries, the spread of new technologies, and improved energy efficiency. Possible trajectories of changes in electricity and energy demand and in the energy intensity of Russia's GDP in the period until 2050 are calculated for conservative and baseline economic development scenarios. It is shown that the dynamics of per capita electricity consumption in Russia correspond to global trends. Additionally, estimates of changes in energy consumption levels associated with the use of digital technologies in the household sector and with large-scale development of electric mobility are also given. [ABSTRACT FROM AUTHOR]

The dehydration of agricultural products is a topic of research covering the preservation and conservation of the quality of agricultural and food items. The actual tasks of environmental protection include creating novel solutions to reduce the energy needs and exothermic consequences of the bulk of industrial processes. Due to the fact that air temperature and velocity in the seed layer are not uniform, the currently employed seed dryers have significant energy requirements and generate a lot of heat. A device that addresses this problem was developed and is now the subject of a patent application. The Coandă effect-based tronconic plates used in the dryer enable warm air to be dispersed uniformly throughout the product mass. Mathematical modeling was used for the design, operation, and optimization of the baffled drying unit. The investigations, which were conducted by modifying and observing the operational parameters (the velocity and temperature of the warm air) used four distinct types of seed with three different beginning moisture concentrations. Following the completion of the CFD simulation, the unit's design was created in SolidWorks. The seed and air humidity were measured using sensors, and the air speed was measured using an anemometer. The current lines and fields were used to express the speed and temperature results. The unit with deflectors reduced the amount of heat released by up to 15.38% and reduced the amount of energy used by up to 14%. [ABSTRACT FROM AUTHOR]

This study develops a model based on a general equilibrium framework to assess the excess burden of carbon taxes imposed on energy commodities (electricity and natural gas) among residential sector. The model takes into account labor market distortions from the tax and cross-price effects among energy commodities. Using data from the U.S. Residential Energy Consumption Survey, the own-price and cross-price elasticities of energy commodities are estimated. A substitution effect is found between electricity and natural gas, and omitting this effect would overestimate the excess burden of the carbon tax. The results show that the carbon tax behaves differently in affecting the excess burden for low-, middle-, and high-income households. The excess burden is lower for high-income households than for low-income households at lower pre-determined labor tax rates, but the effect is reversed at higher pre-determined labor tax rates. In addition, the empirical results show that the excess burden is different across the nine U.S. regions, while minor gas price changes have no significant effect on the excess burden. [ABSTRACT FROM AUTHOR]

Several rural regions located in Africa are experiencing recurrent and even permanent problems in terms of energy production, supply, and distribution to citizens. This study was conducted to investigate the relevance of the use of a new solar technology that is gradually responding in Europe and in industrialized countries. It is about the use of hybrid photovoltaic thermal (PV/T) solar panels that co-produce electricity and hot water for local use. Furthermore, in Africa, local use of solar energy can provide a share in the energy mix. This work is motivated by the lack of studies on these hybrid solar panels in tropical climates. Hence, the paper examines the potential for integration of these systems in small households. A complete PV/T system consisting of solar panels, pump, storage tank, batteries, and controllers was tested and calibrated by using the TRNSYS simulation tool. A comparative study could thus be carried out for the performance of PV/T in a tropical climate (case of the city of Koua in the Comoros) to its performance in Mediterranean and continental climates (Marseille in the south and Longwy in the northeast of France). The results quantify the performance of the PV/T in the three climates and show that the performance in the town of Koua is 44% to 54% higher than in European climates. It can be concluded from this study that the Comorian market and more generally the sub-Saharan market for PVT systems has a good potential for development. [ABSTRACT FROM AUTHOR]