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Showing papers on "Efficient energy use published in 2021"


Journal ArticleDOI
TL;DR: An iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived and can reduce up to 59.5% energy consumption compared to the conventional FL method.
Abstract: In this paper, the problem of energy efficient transmission and computation resource allocation for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model to a base station (BS) which aggregates the local FL model and broadcasts it back to all of the users. Since FL involves an exchange of a learning model between users and the BS, both computation and communication latencies are determined by the learning accuracy level. Meanwhile, due to the limited energy budget of the wireless users, both local computation energy and transmission energy must be considered during the FL process. This joint learning and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the system under a latency constraint. To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived. Since the iterative algorithm requires an initial feasible solution, we construct the completion time minimization problem and a bisection-based algorithm is proposed to obtain the optimal solution, which is a feasible solution to the original energy minimization problem. Numerical results show that the proposed algorithms can reduce up to 59.5% energy consumption compared to the conventional FL method.

365 citations


Journal ArticleDOI
TL;DR: In this article, the impacts and challenges of COVID-19 pandemics on energy demand and consumption and highlights energy-related lessons and emerging opportunities are discussed. But, although the overall energy demand declines, the spatial and temporal variations are complicated.

283 citations


Journal ArticleDOI
TL;DR: In this article, the authors investigated the effects of technological innovation within certain countries on the energy efficiency performance of neighboring countries, using data from the OECD Triadic Patent Families database for 24 innovating countries between the years 1994 and 2013.

232 citations


Journal ArticleDOI
TL;DR: A review of management strategies for building energy management systems for improving energy efficiency is presented and different management strategies are investigated in non-residential and residential buildings.
Abstract: Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings' energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.

230 citations


Journal ArticleDOI
TL;DR: In this paper, a bi-level optimal dispatching model for a community integrated energy system (CIES) with an EVCS in multi-stakeholder scenarios is established, and an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range.
Abstract: A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users energy consumption and electric vehicles behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.

209 citations


Journal ArticleDOI
TL;DR: Stochastic optimization techniques are applied to transform the original stochastic problem into a deterministic optimization problem, and an energy efficient dynamic offloading algorithm called EEDOA is proposed, which can approximate the minimal transmission energy consumption while still bounding the queue length.
Abstract: With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA’s effectiveness.

200 citations


Journal ArticleDOI
TL;DR: In this paper, the empirical relationship between energy poverty and energy efficiency in developed and developing countries through various domains is addressed, and the analysis is conducted using energy poverty indicators, country-wise GDP, energy efficiency, and social welfare by using data envelopement analysis (DEA) and entropy method through mediating role of econometric estimation by using.

198 citations


Journal ArticleDOI
TL;DR: An in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted.

198 citations


Journal ArticleDOI
TL;DR: This paper reviews the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, which is to perform tasks T using Performance measure P and based on learning from Experience E.

197 citations


Journal ArticleDOI
TL;DR: In this paper, the impact of energy reforms on energy efficiency was analyzed using data envelopment analysis (DEA) and the difference-in-difference (DID) method.

186 citations


Journal ArticleDOI
TL;DR: How AI techniques outperform traditional models in controllability, big data handling, cyberattack prevention, smart grid, IoT, robotics, energy efficiency optimization, predictive maintenance control, and computational efficiency is explored.

Journal ArticleDOI
01 Jan 2021
TL;DR: The Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes and the outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods.
Abstract: Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony Optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.

Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors employed the super-efficiency data envelopment analysis and spatial econometric model to analyze energy utilization efficiency against the backdrop of environmental constraints and found that green credit has a positive impact on high-efficiency utilization of energy in China.

Journal ArticleDOI
TL;DR: The results confirm that fiscal decentralization and eco-innovation promote renewable energy consumption and lower non-renewable energy use and recommend that transferring the power to the local governments will further improve energy efficiency and switch these countries' energy mix towards more sustainable sources of energy.

Journal ArticleDOI
TL;DR: In this article, a review of the fast-developing Covalent Organics (COF) field in terms of molecular design and subsequent synthetic strategies to prepare COFs with highly conjugated and modifiable structures and their applications in energy conversion and storage is presented.
Abstract: The excessive depletion of fossil fuels and consequent energy crisis combined with environmental issues call for inexhaustible, clean and renewable energy sources and environmentally friendly energy technologies, such as solar energy and novel electrochemical energy conversion and storage devices. Developing supporting platforms for energy conversion and storage ameliorating mass transfer and electron transfer has stepped into the center of the energy research arena. Covalent organic frameworks (COFs) are emerging crystalline porous materials linked via covalent bonding possessing flexible molecular design and synthetic strategies, high conjugated and modifiable structures, large surface area and porosity. Due to these merits, COFs have shown promising perspectives in energy applications including photocatalysis, electrocatalysis, supercapacitors, metal-ion/sulfur batteries, etc. This critical review imparts a comprehensive summary of the fast-developing COF field in terms of molecular design and subsequent synthetic strategies to prepare COFs with highly conjugated and modifiable structures and their applications in energy conversion and storage. Furthermore, challenges and perspectives according to previous contributions are also discussed for developing more efficient energy conversion and storage COF materials. It is anticipated that this review could boost further research enthusiasm for COF-based materials in energy applications.

Journal ArticleDOI
01 Feb 2021
TL;DR: The overall structure of EnergyPLAN and the essential algorithms and computational structure are described, which enables the analysis of the conversion of renewable electricity into other energy carriers, such as heat, hydrogen, green gases and electrofuels, as well as the implementation of energy efficiency improvements and energy conservation.
Abstract: EnergyPLAN is an energy system analysis tool created for the study and research in the design of future sustainable energy solutions with a special focus on energy systems with high shares of renewable energy sources. It has been under development since 1999 and has formed the basis for a substantial number of PhD theses and several hundreds of research papers. EnergyPLAN is designed to exploit the synergies enabled from including the whole energy system, as expressed in the smart energy system concept. Thus, with EnergyPLAN, the user can take a holistic approach focusing on the analysis of the cross-sectoral interaction. Traditionally disparate demand sectors, such as buildings, industry and transport, are linked with supply technologies through electricity, gas, district heating and cooling grids. In this way, EnergyPLAN enables the analysis of the conversion of renewable electricity into other energy carriers, such as heat, hydrogen, green gases and electrofuels, as well as the implementation of energy efficiency improvements and energy conservation. This article describes the overall structure of EnergyPLAN and the essential algorithms and computational structure.

Journal ArticleDOI
TL;DR: In this paper, a detailed investigation of the current developments on compressed air storage systems (CAES) is presented, which explores both the operational mode of the system, and the health and safety issues regarding the storage systems for energy.
Abstract: Energy storage systems are a fundamental part of any efficient energy scheme. Because of this, different storage techniques may be adopted, depending on both the type of source and the characteristics of the source. In this investigation, present contribution highlights current developments on compressed air storage systems (CAES). The investigation explores both the operational mode of the system, and the health & safety issues regarding the storage systems for energy. The investigation also includes a detailed conclusion, which summarises the vast significance of novel energy storage technology. The investigation thoroughly evaluates the various types of compressed air energy storage systems, along with the advantages and disadvantages of each type. Different expanders ideal for various different compressed air energy storage systems are also analysed. Design of salt caverns and other underground and above compressed air storage systems were also discussed in terms of advantages and disadvantages.

Journal ArticleDOI
TL;DR: In this paper, a systematic review analysis of fully enforced stay at home orders and government lockdowns is presented to identify the impacts of stay home living patterns on energy consumption of residential buildings.
Abstract: In this paper, a systematic review analysis of fully enforced stay at home orders and government lockdowns is presented. The main goal of the analysis is to identify the impacts of stay home living patterns on energy consumption of residential buildings. Specifically, metered data collected from various reported sources are reviewed and analyzed to assess the changes in overall electricity demand for various countries and US states. Weather adjusted time series data of electricity demand before and after COVID-19 lockdowns are used to determine the magnitude of changes in electricity demand and residential energy use patterns. The analysis results indicate that while overall electricity demand is lower because of lockdowns that impact commercial buildings and manufacturing sectors, the energy consumption for the housing sector has increased by as much as 30% during the full 2020 lockdown period. Analysis of reported end-use data indicates that most of the increase in household energy demand is due to higher occupancy patterns during daytime hours, resulting in increased use of energy intensive systems such as heating, air conditioning, lighting, and appliances. Several energy efficiency and renewable energy solutions are presented to cost-effectively mitigate the increase in energy demands due to extended stayhome living patterns.

Journal ArticleDOI
29 Mar 2021-Energies
TL;DR: In this paper, the authors present the steps taken and innovative actions carried out by enterprises in the energy sector and analyze the relationships between innovative strategies, including, inter alia, digitization, and Industry 4.0 solutions, in the development of companies and the achieved results concerning sustainable development and environmental impact.
Abstract: In the 21st century, it is becoming increasingly clear that human activities and the activities of enterprises affect the environment. Therefore, it is important to learn about the methods in which companies minimize the negative effects of their activities. The article presents the steps taken and innovative actions carried out by enterprises in the energy sector. The article analyzes innovative activities undertaken and implemented by enterprises from the energy sector. The relationships between innovative strategies, including, inter alia, digitization, and Industry 4.0 solutions, in the development of companies and the achieved results concerning sustainable development and environmental impact. Digitization has far exceeded traditional productivity improvement ranges of 3–5% per year, with a clear cost improvement potential of well above 25%. Enterprises on a large scale make attempts to increase energy efficiency by implementing the state-of-the-art innovative technical and technological solutions, which increase reliability and durability (material and mechanical engineering). Digitization of energy companies allows them to reduce operating costs and increases efficiency. With digital advances, the useful life of an energy plant can be increased up to 30%. Advanced technologies, blockchain, and the use of intelligent networks enables the activation of prosumers in the electricity market. Reducing energy consumption in industry and at the same time increasing energy efficiency for which the European Union is fighting in the clean air package for all Europeans have a positive impact on environmental protection, sustainable development, and the implementation of the decarbonization program.

Journal ArticleDOI
TL;DR: The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm with Simulated Annealing with WOA, and is compared with several state‐of‐the‐art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA.
Abstract: © 2020 John Wiley & Sons, Ltd. Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state-of-the-art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches.

Journal ArticleDOI
25 Oct 2021-Energy
TL;DR: In this article, the authors tried to connect sustainable development goals with energy efficiency for 20 Asian and Pacific (AP) countries using Data Envelopment Analysis (DEA) from 2000 to 2018.

Journal ArticleDOI
TL;DR: In this article, the authors proposed a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), which is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation.
Abstract: Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff–Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

Journal ArticleDOI
TL;DR: In this paper, a review of the fundamental, technical, environmental, and economic aspects associated with the use of pure ammonia as a transportation fuel are broadly addressed, focusing on pure ammonia and ammonia fuel blends operation, NOx emissions control, current challenges related to the detailed and accurate understanding of the ammonia chemistry, and the lack of high-fidelity numerical models.

Journal ArticleDOI
Waseem Aftab1, Ali Usman1, Jinming Shi1, Kunjie Yuan1, Mulin Qin1, Ruqiang Zou1 
TL;DR: In this paper, the authors review the broad and critical role of latent heat TES in recent, state-of-the-art sustainable energy developments and discuss the exciting research opportunities available to further improve the overall energy efficiency of integrated TES systems.
Abstract: Thermal energy plays an indispensable role in the sustainable development of modern societies. Being a key component in various domestic and industrial processes as well as in power generation systems, the storage of thermal energy ensures system reliability, power dispatchability, and economic profitability. Among the numerous methods of thermal energy storage (TES), latent heat TES technology based on phase change materials has gained renewed attention in recent years owing to its high thermal storage capacity, operational simplicity, and transformative industrial potential. Here, we review the broad and critical role of latent heat TES in recent, state-of-the-art sustainable energy developments. The energy storage systems are categorized into the following categories: solar-thermal storage; electro-thermal storage; waste heat storage; and thermal regulation. The fundamental technology underpinning these systems and materials as well as system design towards efficient latent heat utilization are briefly described. Finally, the exciting research opportunities available to further improve the overall energy efficiency of integrated TES systems are discussed.

Journal ArticleDOI
Hakpyeong Kim1, Heeju Choi1, Hyuna Kang1, Jongbaek An1, Seungkeun Yeom1, Taehoon Hong1 
TL;DR: In this article, the authors investigated the research themes on smart homes and cities through a quantitative review and identified barriers to the progression of smart homes to sustainable smart cities through qualitative review, based on the results of the holistic framework of each domain (smart home and city) and the techno-functional barriers.
Abstract: In recent years, smart cities have emerged with energy conservation systems for managing energy in cities as well as buildings. Although many studies on energy conservation systems of smart homes have already been conducted, energy management at the city level is still a challenge due to the various building types and complex infrastructure. Therefore, this paper investigated the research themes on smart homes and cities through a quantitative review and identified barriers to the progression of smart homes to sustainable smart cities through a qualitative review. Based on the results of the holistic framework of each domain (smart home and city) and the techno-functional barriers, this study suggests that the following innovative solutions be suitably applied to advanced energy conservation systems in sustainable smart cities: (i) construction of infrastructure for advanced energy conservation systems, and (ii) adoption of a new strategy for energy trading in distributed energy systems. Especially, to reflect consumer behavior and energy in sustainable smart cities, the following responses to future research challenges according to the “bottom-up approach (smart home level to smart city level)” are proposed: (i) development of real-time energy monitoring, diagnostics and controlling technologies; (ii) application of intelligent energy management technologies; and (iii) implementation of integrated energy network technologies at the city level. This paper is expected to play a leading role as a knowledge-based systematic guide for future research on the implementation of energy conservation systems in sustainable smart cities.

Journal ArticleDOI
TL;DR: This work aims to maximize the energy efficiency for mmWave-enabled NOMA-UAV networks by optimizing the UAV placement, hybrid precoding and power allocation, and three schemes are proposed, where the cluster head selection algorithm is adopted while considering different equivalent channels of users.
Abstract: Owing to the recent advances of non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave), these two technologies are combined in unmanned aerial vehicle (UAV) networks in this paper.However, energy efficiency has become a significant metric for UAVs owning to their limited energy.Thus, we aim to maximize the energy efficiency for mmWave-enabled NOMA-UAV networks by optimizing the UAV placement, hybrid precoding and power allocation.However, the optimization problem is complicated and intractable, which is decomposed into several sub-problems.First, we solve the UAV placement problem by approximating it into a convex one.Then, the hybrid precoding with user clustering is performed to better reap the multi-antenna gain. Particularly, three schemes are proposed, where the cluster head selection algorithm is adopted while considering different equivalent channels of users.Finally, the power allocation is optimized to maximize the energy efficiency, which is converted to convex and solved via an iterative algorithm.Simulation results are provided to evaluate the performance of the proposedschemes.

Journal ArticleDOI
TL;DR: In this paper, the impact of energy trilemma and population growth on economic growth in the top ten countries in the World Energy Trilemma Index (WETI) 2020 was examined.
Abstract: Economic growth can increase the economy's energy intensity, impeding security through unmanaged demand and affecting sustainability. This study employed energy use, population growth, and financial development from 1990 to 2016 as moderator variables to examine the impact of energy trilemma on the economic growth of the top ten countries in the World Energy Trilemma Index (WETI) 2020. We applied advanced econometric methodologies for empirical analysis, such as second-generation panel unit root tests, and cross-section dependence. Besides, we used the random-effect and fixed effect panel generalized method of moments (GMM) for short-run estimates and the random effect and fixed effect generalized least squares (GLS) regressions and robust fully modified least squares (FMOLS) regression for the long-run estimates. The results indicate that the impact of energy trilemma and population growth on economic growth are significant only in the long-run, while energy use and financial development influence economic growth in both the short-run and the long-run. Our findings suggest that policymakers implement lesser potential for Pareto perfections in the energy system by levying energy security, affordability, and sustainability taxes on energy products, and that they highlight energy efficiency and support robust policies to enhance financial development. Study limitations and directions for future research in the area are discussed.

Journal ArticleDOI
TL;DR: In this article, a review of thermal energy storage systems using PCM technology for buildings is presented, where the authors highlight the promising effectiveness of PCM in building heating applications, and highlight the significance of the PCM system hybridization.
Abstract: Researchers world-wide are investigating thermal energy storage, especially phase change materials, for their substantial benefits in improving energy efficiency, sustaining thermal comfort in buildings and contributing to the reduction of environmental pollution. Residential buildings and commercial constructions, being dependent on heating and cooling systems, are subjected to the utilization of PCM technology through several applications. The current study presents a state-of-the-art review that covers recent literature on thermal energy storage systems utilizing PCMs for buildings. The reviewed applications are heating and hybrid applications, that are categorized as passive and active systems. A summary of the PCMs used, applications, thermo-physical properties and incorporation methods are presented as well. The study emphasizes the promising effectiveness of PCM in building heating applications, and highlights the significance of PCM system hybridization. The study shows that experimental investigations on commercial constructions, and hybrid systems development and optimization, are still required. Finally, possible combinations of active and passive heating applications with their auspicious benefits in terms of energy efficiency augmentation, are recommended.

Journal ArticleDOI
TL;DR: In this article, the authors explore the evidence on the size of economy-wide rebound effects and explore whether and how such effects are taken into account within the models used to produce global energy scenarios.
Abstract: The majority of global energy scenarios anticipate a structural break in the relationship between energy consumption and gross domestic product (GDP), with several scenarios projecting absolute decoupling, where energy use falls while GDP continues to grow. However, there are few precedents for absolute decoupling, and current global trends are in the opposite direction. This paper explores one possible explanation for the historical close relationship between energy consumption and GDP, namely that the economy-wide rebound effects from improved energy efficiency are larger than is commonly assumed. We review the evidence on the size of economy-wide rebound effects and explore whether and how such effects are taken into account within the models used to produce global energy scenarios. We find the evidence base to be growing in size and quality, but remarkably diverse in terms of the methodologies employed, assumptions used, and rebound mechanisms included. Despite this diversity, the results are broadly consistent and suggest that economy-wide rebound effects may erode more than half of the expected energy savings from improved energy efficiency. We also find that many of the mechanisms driving rebound effects are overlooked by integrated assessment and global energy models. We therefore conclude that global energy scenarios may underestimate the future rate of growth of global energy demand.

Journal ArticleDOI
TL;DR: Numerical results validate the analysis and show that the proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in IoT networks compared to the existing baselines.
Abstract: Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables the IoT devices of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the devices. In this article, we apply non-orthogonal multiple access (NOMA) technique to enable massive connectivity and investigate how it can be exploited to achieve energy-efficient MEC in IoT networks. In order to maximize the energy efficiency for offloading, while simultaneously satisfying the maximum tolerable delay constraints of IoT devices, a joint radio and computation resource allocation problem is formulated, which takes both intra- and inter-cell interference into consideration. To tackle this intractable mixed integer non-convex problem, we first decouple it into separated radio and computation resource allocation problems. Then, the radio resource allocation problem is further decomposed into a subchannel allocation problem and a power allocation problem, which can be solved by matching and sequential convex programming algorithms, respectively. Based on the obtained radio resource allocation solution, the computation resource allocation problem can be solved by utilizing the Knapsack method. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in IoT networks compared to the existing baselines.