Changes In Energy Consumption Research Articles

Research on the Impact of Energy Price on Carbon Emission Intensity of China—An Empirical Study Based on LMDI Decomposition and Econometric Models

Taking the data of various sectors and three industries from 1980 to 2019 as the research object, the LMDI-I (Logarithmic Mean Divisia Index) multiplicative decomposition model, which is based on the principle of decomposing the change in energy consumption into the contribution of each factor, was used to decompose the carbon emission intensity into technological progress effect and economic structure changing effect. Meanwhile, quantitative econometric models of energy price, economic growth, energy consumption structure, and the two effects were also established. The empirical results showed that energy price, economic growth, and energy consumption structure significantly influenced the reduction in carbon emission intensity. A positive U-shaped relationship between energy prices and carbon emission intensity was overserved, and the rise of energy prices mainly drive the decline of carbon emission intensity through the effect of technological progress. However, the effect of economic structure driven by the rise of energy prices was limited; thus, further optimization of economic structure is needed. Additionally, the proportion of coal consumption was positively correlated with the technological progress effect and economic structure change effect, while the decrease in coal consumption proportion promoted the decline of carbon emission intensity. Finally, three recommendations based on the analysis were proposed.

How do heat demand and energy consumption change when households transition from gas boilers to heat pumps in the UK

Heating with air source heat pumps is an important step in the transition to Net Zero Carbon for many households. When predicting the resulting energy consumption it is frequently assumed that heating demand is unchanged from before the switch from gas heating. However, heating patterns usually change, with a net increase in heating demand to achieve the same level of thermal comfort. This paper discusses the change in pattern and uses modelling to determine the resulting increase in energy demand. It shows that the increase can be predicted with information about dwelling heat loss and thermal mass as well as weather and the pre and post switch heating pattern. The increase can be 20% or more for homes with high heat loss. It proposes that the metric for heating demand increase would be a useful measure of heat pump readiness, and that the parameters required to assess this should be provided on energy performance certificates.

Are all the U.S. biomass energy sources green?

Whether biomass energy should be regarded as a clean energy or not is still a contentious issue in the literature. Biomass energy comes from different sources, each having the possibility to affect the environment differently. Accounting for this heterogeneity to avoid any aggregation bias, this study examines the impact of biomass energy on environmental quality in the U.S. from the standpoints of consumption and production, different biomass sources, asymmetric changes, and threshold effects. We find that the adverse environmental impact of biomass energy production is higher than its consumption. Moreover, except in the long-run where the impact of negative changes in biomass energy consumption overshadows its positive changes, positive changes in biomass energy consumption and production degrade the environment more than negative changes in biomass energy consumption. The threshold analysis further reveals that the production and consumption of biomass energy beyond some levels reduces environmental quality, unlike when they are minimal. Finally, wood is the most environmentally unfriendly biomass energy source, whereas biodiesel is the most environmentally friendly. The policy implications of these findings are crucial for environmental sustainability, climate change mitigation, and efficient production and use of biomass.

Asymmetric Effects of Financial Development on CO2 Emissions in Bangladesh

Depending on how it functions and is organized, the financial system can have a negative, positive, or zero impact on the environment. For Bangladesh, the empirical relationship between financial development and the environment, measured in terms of carbon dioxide (CO2) emissions per capita, is analysed over the period 1980 to 2020. This is the first such analysis for this country. We perform this within a non-linear bound testing framework while controlling for changes in energy consumption, gross domestic product, and trade volume. There are two key findings. One, we find that the relationship between CO2 emissions per capita and financial development is cointegrating, with the direction of cointegration running from financial development to CO2 emissions. Two, we find that positive and negative changes in financial development have asymmetric impacts on CO2 emissions in the long and short run. The implications of these findings are discussed regarding their attendant environmental policy implications.

Comparison of Most Popular Buildings Performance Simulation Tools

A critical procedure in sustainable building design is the building energy performance assessment, which has significant implications for global energy consumption and climate change. This study compares three simulation software programs for a photovoltaic system on a building’s roof. The low-rise residential buildings in three East Mediterranean cities (Amman, Mafraq, and Aqaba) represent moderate drywarm, semiarid, and humid subtropical climate zones were compared using three simulation software programs (IES-VE, DesignBuilder, REVIT) for a typical building with PV on the roof and the second scenario without a PV system installed on the roof. This investigation aims to evaluate the shading effect of the PV system on a building’s roof structure by calculating the total electrical load required to maintain thermal comfort inside the building. The final results showed significant discrepancies between the three software for the base building design and the PV system on the roof, with a range of around 50 %. This highlights the importance of evaluating and calibrating different simulation tools and using them with a great deal of caution.

Metabolic energy consumption and information transmission of a two-compartment neuron model and its cortical network

The physiological structure of the brain and the morphology of its neurons result from selective pressure, the nervous system must be energy efficient. In this paper, a two compartment model consisting of dendritic and axon compartments is proposed to simulate the effects of morphology and temperature on neuronal kinetics. Based on the equivalent circuit method, we calculated the metabolic energy consumption of neurons with morphological and temperature variations. It is shown that higher temperatures increase the separation of Na+ and K+ currents in the axon, resulting in higher energy efficiency, while neuron with moderate morphological parameter is more likely to fire, but accompanied by a higher energy cost. We integrate this two-compartment model into a feedforward network to simulate changes in network energy consumption affected by neuron morphology and temperature during information propagation. It is found that moderate temperature and morphology allow stable propagation of synchronization spikes in the feedforward network and maximize the energy utilization for information transmission. In addition, we present a simple statistical method to detect the robustness of network information transmission. This paper may provide some insights for further studies on energy consumption in the cerebral cortex.

Changes in Air Quality, Meteorology and Energy Consumption during the COVID-19 Lockdown and Unlock Periods in India

The increasing population and its associated amenities demand innovative devices, infrastructure, methods, plans and policies. Regional climate has a great role in deciding the air quality and energy demand, and therefore, weather and climate have an indisputable role in its consumption and storage. Here, we present the changes in trace gases and associated regional weather in India during lockdown and unlock periods of COVID-19. We observe a reduction of about 30% in sulphur dioxide (SO2) and 10–20% in aerosols in the Indo-Gangetic Plain (IGP), large cities, industrial sites, mining areas and thermal power plants during lockdown as compared to the same period in the previous year and with respect to its climatology. However, a considerable increase in aerosols is found, particularly over IGP during Unlock 1.0 (1–30 June 2020), because of the relaxation of lockdown restrictions. The analyses also show a decrease in temperature by 1–3 °C during lockdown compared to its climatology for the same period, mainly in IGP and Central India, possibly due to the significant reduction in absorbing aerosols such as black carbon and decrease in humidity during the period. The west coast, northwest and central India show reduced wind speed when compared to its previous year and climatological values, suggesting that there was a change in regional weather due to the lockdown. Energy demand in India decreased by about 25–30% during the first phase of lockdown and about 20% during the complete lockdown period. This study thus suggests that the reduction of pollution could also modify local weather, and these results would be useful for drafting policy decisions on air pollution reduction, urban development, the energy sector, agriculture and water resources.

Transition of household cooking energy in China since the 1980s

As urbanization accelerates and an increasing number of people move from rural to urban areas, changes in household energy consumption have become a major symptom of typical human-land relationship activities in different regions, i.e., labour migration is affecting household energy consumption, especially household cooking energy. Based on household data from the China Health and Nutrition Survey (1989–2015), this paper employs a panel multinomial logit model and mathematical statistics to reveal structural changes in urban and rural Chinese households cooking energy consumption and the driving factors of these changes over the past 30 years and provides suggestions for the structural upgrade of household cooking energy consumption. The research shows that household cooking energy consumption in China has undergone a substantial transition since the 1980s, displaying diverse trends in urban and rural households. The shift from coal to natural gas in urban areas and from solid fuels to electricity in rural areas was driven by a range of factors such as per capita disposable income, government policies, energy prices and availability. Among these, household income was found to be the most influential factor. Additionally, migration had a notable effect on household cooking energy consumption in rural areas. The reduction of solid fuel usage and the electrification of cooking are proven methods of lowering household energy emissions, as natural gas releases 60% less CO2 compared to coal when producing the same amount of heat. The government must implement a focused strategy for the transition towards cleaner energy sources for household cooking, prioritizing rural areas by strengthening energy infrastructure and fostering the development of solar and wind energy. In urban areas, efforts should be directed towards enhancing income, stabilizing energy costs, and popularizing the use of natural gas.

Influence of ambient temperature on the phenology of the greater mouse-eared bat (Myotis myotis).

In order to assess the consequences of climate change and evaluate its impacts on wildlife, it is essential to do so on a species-specific level. It is assumed that changes in the ambient temperature influence energy consumption as well as food availability and thus foraging behavior, reproduction, survival, and therefore population dynamics in bats. Based on this assumption, the present study aims to gain insights into the roosting and breeding behavior of the greater mouse-eared bat (Myotis myotis) in relation to changes of the ambient temperature. For this purpose, we investigated the effect of ambient temperature on the phenology of the greater mouse-eared bat by using activity data of the bats collected using light barriers at the maternity roosts. The light barrier used in this study is a system that detects the interruption of two light beams, for example, by a flying bat, and displays it as an electrical signal. The investigations have shown that the higher the winter temperatures, the earlier the greater mouse-eared bats returned to the roosts to form the maternity colony; however, this was only true for ambient temperatures below 0.5°C,birth season started earlier at higher spring temperatures,the dissolution of maternity roosts occurred earlier with earlier birth season and at higher ambient temperatures during lactation. The results revealed that ambient temperature has an influence on the phenology of the greater mouse-eared bat. Depending on the respective life history stage, an increase in ambient temperature can have a positive or negative effect on the fitness of the animals. In recent years, mild winters have been recorded more frequently, which can have an influence on the behavior of bats. Warm winters within certain limits seem to lead to an earlier formation of the maternity colony, which can be positive or negative for the bats depending on persistent weather conditions and thus insect availability. In the course of climate change, we can also expect earlier spring events and an increase in spring temperature, as well as hot spells in summer. These warm springs and summers seem to lead to an earlier beginning of births, a faster development of the juveniles and an earlier dissolution of the maternity roost. An advance of reproductive activities can be assumed to increase the chance to survive the following winter in both mothers and their young, as they have more time to build up sufficient energy reserves for hibernation before winter starts. Due to the climatic changes, phenological changes of the bats be expected. This study highlights that in order to understand the impact of climate change on biodiversity, it is necessary to investigate in detail effects on a species-specific level and also to consider direct and indirect effects of ambient temperature on different life history stages.

RECURRENT ANALYSIS OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE

The subject of the study is the study of models and methods for short-term forecasting of energy consumption in power systems based on recurrent analysis of time series. The aim of the work is a recurrent analysis of the time series of energy consumption of a metallurgical enterprise and the development of a program in the Matlab environment for automating calculations and experimental testing of data available for research in PJSC Electrometallurgical Plant "Dniprospetsstal" named after A. M. Kuzmin. The following tasks have been solved: the methodology for constructing recurrent diagrams and their quantitative analysis have been considered; a model of the time series and the phase trajectory of the time series was built to visualize the change in energy consumption during the day; software for constructing recurrent diagrams in the Matlab package was developed. Methods were used: analysis of time series based on recursive analysis to study the characteristics of the state of the system on the example of a metallurgical enterprise. The results were obtained: software was developed in the Matlab environment for short-term forecasting of energy consumption in power systems, and quantitative indicators were calculated that can be used to characterize the state of the system and analyze energy consumption in the summer and winter seasons. Conclusions: in the course of the study, software for constructing and quantitative analysis of recurrence diagrams in the Matlab package was developed, with the help of which patterns were discovered and information about the properties of the system under study was obtained. Based on the analysis of the average values of quantitative measures in the off-season for 2018–2021, it can be seen that the summer period is characterized by greater predictability, as well as a significantly higher latency indicator, which characterizes the average time when the system can spend in a more or less unchanged state. Confirmed on real data, the benefits of using the recursive analysis method for estimating electricity consumption, as well as more efficient modeling of this process, can lead to an increase in the accuracy of forecasting its future dynamics.

An Energy-aware Technique for Resource Allocation in Mobile Internet of Thing (MIoT) Using Selfish Node Ranking and an Optimization Algorithm

Internet of Things (IoT) is a new revolution of the Internet, which can be thought of as an uprising expansion of internet services. IoT is defined in many different ways. It covers many areas of life, including houses, cities, automobiles, and roads. It also includes devices that track people's behavior and utilize the information gathered for push services. Any object may access the web over a wired or wireless network with the IoT. In addition to authentication and security, extensive research and development have gone into energy awareness. Mobile Internet of Things (MIoT) is the following stage in this situation. Mobile data collecting, data analysis, energy management, security and privacy, and the provision of IoT services are some issues arising when using MIoT. So, this paper proposed an energy-aware technique for resource allocation in MIoT. Due to this problem's NP-hard nature, a new approach is suggested to reduce the network's energy consumption and end-to-end delay in MIoT using selfish node ranking and ant colony optimization algorithms. The proposed method is compared to Whale Optimization Algorithm (WOA) and Task Priority-based Resource Allocation (TPRA) algorithms. The findings show the network's substantial changes in energy consumption and end-to-end delay using the proposed method. The findings of the current work are significant for academics and offer insights into upcoming study areas in this subject.

Research on energy saving prediction of building clusters based on data mining algorithm

In order to solve the problem of excessive burden of electricity and energy consumption in urban landscape buildings clusters, the study combined data mining algorithms to establish a prediction model for energy-saving renovation of urban landscape building clusters. Firstly, the energy demand and energy consumption of the urban landscape buildings complex were analysed, a mathematical model was established to predict the energy consumption of the building complex. Then, the prediction model of energy-saving retrofitting of building clusters was constructed by combining data mining techniques. The experimental results show that the change trend of total energy consumption is different under different single influencing factors of energy consumption. Among them, the lighting power density factor has the greatest influence on energy consumption, and its annual energy consumption change rate can reach about 0.35. Applying the prediction model to the energy consumption prediction of 15 urban single buildings, it was found that the total energy consumption of the buildings before the retrofit was much higher than that after the retrofit, and the energy-saving rate of the whole observed sample building group was as high as 18.5%, meanwhile, the highest energy-saving rate of the single buildings reached 30.1%.

Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks

Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent dimensions of a forecast: three dimensions in space, time and probability. The forecasts are generated through different models based on artificial neural networks as a post-treatment of point forecasts based on shallow artificial neural networks, creating a dynamic ensemble. The singular value decomposition (SVD) technique is then used herein to generate temperature scenarios and project different futures for the probabilistic forecast. In additional to meteorological conditions, time and recency effects were considered as predictor variables. Buildings that are part of a university campus are used as a case study. Though this methodology was applied to energy demand forecasts in buildings alone, it can easily be extended to energy communities as well.

Synchronization of water-energy consumption in residential and non-residential buildings during COVID-19

ABSTRACT COVID-19 has made working from home (WFH) a widely prevalent mode of work, resulting in highly complex changes of energy and water consumption in buildings. To understand these changes, this study applies the concept of water-energy nexus (WEN) in the analysis of energy and water data in residential and non-residential buildings in Ontario, Canada, before and during the pandemic. The study found the overall energy and water consumption of buildings exhibited a decreasing trend, with the most significant change found in water consumption. Energy and water consumption increased in residential buildings but decreased in non-residential buildings; the changes in energy and water consumption were synchronized over the WFH period. This study also elucidated that changes were related to the demographic and job attributes. When dealing with the peak load of residential consumption with a high consumption benchmark, due consideration should be given to the stronger synchronization of the two types of resources to improve the resilience of residences to cope with the uncertainty of unexpected large-scale public health crisis. Applying WEN to building resource consumption during WFH for the first time, the findings shed light on the need to enhance integrated water and energy management.

A Systematic Review of the Role of Land Use, Transport, and Energy-Environment Integration in Shaping Sustainable Cities

Land use, transport, and energy-environment integration (LUTEI) is receiving considerable attention as an elaborate approach to improving urban resilience. Research evidence on this multidisciplinary topic tends to be fragmented, hindering constructive analysis of its role in shaping sustainable cities. This paper addresses this by undertaking a holistic systematic review to consolidate diverse perspectives. The analysis of 195 reviewed papers identified four main clusters of knowledge that include methodological approaches, policy instruments, urban design elements, and impacts of interventions. The analysis revealed that a growing body of literature is increasingly focused on improving accessibility planning, transit-oriented development, and policy integration to achieve sustainable and healthy transport as a vital element of resilience in cities. The review found, however, that the integration of environment and energy into land use and transport models is still at a nascent stage of development and has largely been overlooked in traditional LUTI models. This can lead to unreliable assessments of the impacts of low-carbon mobility solutions, emerging green transport technologies, and long-term changes in energy consumption affecting sustainable mobility futures. This paper concludes by connecting LUTEI dimensions to the UN’s sustainable development goals (SDG), outlining future directions to ignite meaningful research on the topic and providing a transparent path for decision-makers to adopt LUTEI-informed planning.

Airflow uniformity and energy consumption investigation on data centers enhanced by jet fans

Active adjustment of the airflow organization in data centers (DC) has been increasingly applied due to its superior adjustment performance. But for now, the main focus is on the airflow distribution optimization in the horizontal direction, while few research has been conducted to investigate the additional energy consumption of active method. This paper introduced the jet-induced ventilation into under-floor air distribution (UFAD), then the coupling air supply system (CASS) was established. Taking the CASS data center as an example, this paper focuses on the impact of the active adjustment method on both airflow organization and energy consumption of the DC, and analyzes the energy consumption of the system while meeting the security operations of the thermal environment. The results showed that the incorporation of jet-induced ventilation effectively improved the airflow distribution uniformity, while the active adjustment of the airflow organization in the DC has little effect on the overall energy consumption. On the premise of meeting the demand of thermal environment, the energy consumption of the CRAC (Computer room air conditioning) units in the CASS is reduced and the change of the overall energy consumption is small, while the total energy consumption increased with the increase of airflow uniformity.

Effects of possible changes in natural gas, nuclear, and coal energy consumption on CO2 emissions: Evidence from France under Russia’s gas supply cuts by dynamic ARDL simulations approach

The study explores the influences of potential changes in energy consumption on carbon dioxide (CO2) emissions, focusing on disaggregated energy consumption sources. In this manner, the study considers France as the leading nuclear energy-consuming country in Europe, includes yearly data between 1970 and 2021, and performs the dynamic autoregressive distributed lag (DYNARDL) model. In addition, the Kernel-based regularized least squares (KRLS) is used for robustness check. The results reveal that (i) cointegration exists between the disaggregated energy consumption indicators and CO2 emissions; (ii) nuclear, natural gas, oil, and coal energy have a statistically significant effect on CO2 emissions, while renewable energy is not statistically significant; (iii) nuclear power has a decreasing effect on CO2 emissions; (iv) positive (i.e., increasing) shocks to nuclear reduce CO2 emissions, even if they are 300 % in the case of counterfactual shocks; (v) any positive (i.e., increasing) shocks to coal have a drastically increasing effect on CO2 emissions, even if they are 25 % in the case of counterfactual shocks; (vi) the KRLS approach confirms the robustness of the results. Thus, this study suggests that France should continue to rely on nuclear power for electricity generation and that French policymakers should reduce electricity exports to European Union countries to provide an alternative against the Russian natural gas shock by preventing a reduction in energy supply.

Enhancing environmental quality in Portugal: can CO2 intensity of GDP and renewable energy consumption be the solution?

Despite Portugal's effort to reduce carbon emissions, the country still emits about 1.6% of the European Union's CO2 emissions. Meanwhile, limited empirical studies have been undertaken in the context of Portugal. As a result, this study examines the asymmetric and long-term impact of CO2 intensity of GDP, energy consumption, renewable energy and economic growth on CO2 emissions in Portugal from 1990 to 2019. The nonlinear autoregressive distributed lag (NARDL) is employed to uncover the asymmetric connection. The findings show that there is nonlinear cointegration among the variables. The long-run estimation reveals a positive change in energy consumption positively affects CO2 emissions, while a negative shock in energy consumption has a neutral effect on CO2 emissions. Furthermore, positive shocks of economic growth and CO2 intensity of GDP enhance environmental deterioration by increasing CO2 emissions. Meanwhile, the negative shock of these regressors positively impacts CO2 emissions. In addition, positive shocks in renewable energy enhance environmental quality, while negative shocks in renewable energy increase environmental deterioration in Portugal. Policymakers should focus on reducing per-unit energy usage and CO2 efficiency gains, which would necessitate a significant reduction in CO2 intensity andenergy density of GDP.

Impact of COVID-19 Pandemic on Energy Consumption in Office Buildings: A Case Study of an Australian University Campus

Building energy management, in terms of both adopted technologies and occupant consumption behaviour, is becoming an essential element of sustainability and climate change mitigation programs. The global COVID-19 pandemic and the consequential lockdowns and remote working had a notable impact on office building operations and provided a unique opportunity for building energy consumption studies. This paper investigates the COVID-19 effects on energy consumption in office buildings, particularly in the education sector. We studied different buildings at the University of Technology Sydney (UTS) campus before and during the pandemic period. The results demonstrate that the changes in energy consumption due to COVID-19 in different UTS faculties are not as strongly correlated with occupant activity. The comparison shows that buildings with administrative offices or classrooms are easier to switch to a remote-working mode than those housing laboratories and special equipment. During weekends, public holidays, or conditions requiring working from home, the per capita energy consumption increases significantly translating into lower energy efficiency. Our findings highlight the essential need for some changes in office building energy management systems. We provide recommendations for office and commercial buildings in general to deal with similar crises and to reduce energy overconsumption in normal situations.

Joint Optimization of Multi-User Partial Offloading Strategy and Resource Allocation Strategy in D2D-Enabled MEC

With the emergence of more and more computing-intensive and latency-sensitive applications, insufficient computing power and energy of user devices has become a common phenomenon. Mobile edge computing (MEC) is an effective solution to this phenomenon. MEC improves task execution efficiency by offloading some tasks to edge servers for execution. In this paper, we consider a device-to-device technology (D2D)-enabled MEC network communication model, and study the subtask offloading strategy and the transmitting power allocation strategy of users. The objective function is to minimize the weighted sum of the average completion delay and average energy consumption of users, which is a mixed integer nonlinear problem. We first propose an enhanced particle swarm optimization algorithm (EPSO) to optimize the transmit power allocation strategy. Then, we utilize the Genetic Algorithm (GA) to optimize the subtask offloading strategy. Finally, we propose an alternate optimization algorithm (EPSO-GA) to jointly optimize the transmit power allocation strategy and the subtask offloading strategy. The simulation results show that the EPSO-GA outperforms other comparative algorithms in terms of the average completion delay, average energy consumption, and average cost. In addition, no matter how the weight coefficients of delay and energy consumption change, the average cost of the EPSO-GA is the least.