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• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

Since modern times, the increase in shipborne equipment has brought tremendous pressure to the energy supply system. Establishing an accurate and reliable energy consumption model that reflects the dynamic characteristics of the system will provide an essential theoretical reference for energy efficiency optimization. This paper proposes a modeling method that considers both the dynamic characteristics and energy consumption characteristics of the system, based on the power bond-graph theory. Firstly, the transmission principle and energy transfer process of hydraulic and electric helicopter traction devices are analyzed. Then, the energy consumption is analyzed, and the state equation and energy equation of the system are established. Finally, the simulation tests are carried out. The results show that the proposed dynamic modeling method is reasonable and effective and can well reflect the dynamic characteristics and energy consumption characteristics of the system. [ABSTRACT FROM AUTHOR]

Saving resources and reducing the energy consumption of CNC machine tools has become one of the main research directions of modern green manufacturing. In this paper, the influence of curvature characteristics on energy consumption is studied based on surface milling. Firstly, the loss of energy consumption in the machining process of the NC machine tool is analyzed, and the milling energy consumption model per unit volume is established; secondly, based on the unit volume milling energy consumption model, a unit volume milling energy consumption model considering blade curvature characteristics is established; finally, based on the milling energy consumption model, the "specific energy" is introduced to establish the energy consumption field model of blade milling path. The distribution of the energy consumption field of four milling paths is analyzed in turn, and the distribution of the energy consumption field of 24 tool sites in the milling path is analyzed. The experimental results show that the energy consumption field model can show the energy consumption distribution of the NC machine tool machining process, and the accuracy is more than 90%, which provides a reliable basis for the subsequent energy consumption prediction of the impeller machining process. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [ABSTRACT FROM AUTHOR]

In the 21st century, urbanization has caused rapid growth in the global construction area, resulting in high building resource consumption and long-term environmental impacts. Analyzing the resource consumption of public buildings and identifying the key factors that affect the consumption of building resources is important for developing of targeted policies. In this paper, we analyzed each branch of the People's Bank of China based on energy, water, and land resource consumption as well as the structure of energy consumption. Based on resource-consumption indicators, we divided 31 provincial-level administrative regions into four resource consumption levels; 65% were in the low-to-medium resource consumption levels. Medium-sized buildings had the highest energy consumption level and a more balanced energy consumption structure. The energy consumption per capita, water consumption, and utilization rate of small-sized buildings were low. To reduce resource consumption by public buildings, the construction of large-sized buildings should be limited. • The comprehensive evaluation method is used to calculate the resource consumption of the People's Bank of China. • Regression equations were established for energy consumption and resource consumption. • Medium-sized institutions had the highest energy consumption level and a more balanced energy consumption structure. [ABSTRACT FROM AUTHOR]

The increasing effects of global warming and energy depletion have raised concerns about the pollution caused by traditional oil and fossil energy usage. Distributed energy resources (DERs) have emerged as promising techniques to address these issues. However, the growing proportion of power injection from DERs presents technical challenges such as power quality decline and power flow reversal, which may destabilize the grid. Non-intrusive load monitoring (NILM) is a promising and cost-effective approach that can provide residential power information to improve grid scheduling, dispatching, and optimize residential power consumption behavior. This paper proposes a novel NILM method that can decompose the power of both household appliances and DERs integrated with residential houses. The proposed approach employs a data segment method that utilizes adaptive window lengths to identify the behavior of DERs based on the operating periods and characteristics of different equipment. A non-intrusive load monitoring method is proposed based on the ResNet-seq2seq framework to address the problem of model degradation and gradient vanishing. Post-processing techniques are applied to the output results using a feature list of the target equipment. Experiments are conducted on a dataset organized based on the REDD and Pecan Street datasets to demonstrate the accuracy and effectiveness of the proposed method. The results show that compared to the start-of-the-art CNN-seq2seq model, the proposed method achieved 54.95%, 69.18%, 56.95%, and 84.98% reduction in MAE for PV, EV, refrigerators, and microwaves, respectively. • A novel ResNet-seq2seq-based NILM approach is proposed for load disaggregation of residential houses integrating DERs. • Wavelet denoising and adaptive window length data segments are used for data pre-processing and accurate disaggregation. • Residual connections are employed to solve model degradation and vanishing gradient issues as network depth increases. • A post-processing technique is applied to modify the output of the network and improve disaggregation accuracy. [ABSTRACT FROM AUTHOR]

The multi-energy conversion system (MCS) plays an important role in improving the utilization of energy resources and realizing the energy transition. With the application of the new generation of information technologies, the new MCS can realize real-time information interaction, multi-energy collaboration, and real-time demand response, in which energy suppliers can intelligently motivate consumers' energy use behavior. In this paper, an MCS coupled with a cloud platform is proposed to address information explosion and data security issues. Due to the development of Internet technology, the increasing energy data, and the serious energy coupling, it is difficult for traditional optimization methods to deal with the interaction between participants of the MCS. Therefore, the non-cooperative game is used to formulate the interactions between participants with the aim of maximizing the energy suppliers' profit and minimizing the customers' cost. It is proved that the game model is an ordinary game with one Nash equilibrium. The simulation was performed with a gradient projection algorithm and the results show that the proposed MCS improves energy utilization efficiency through energy conversion while ensuring consumer satisfaction, and benefits both the customers and suppliers by reducing the energy consumption cost and the peak load demand, which effectively improve the supply quality and enrich the energy consumption patterns. [ABSTRACT FROM AUTHOR]

Energy efficiency represents a key resource for economic and social development, providing substantial benefits to different stakeholders, ranging from the entities which develop energy efficient measures to everyone in society. In addition to cost savings, multiple benefits can be achieved by supporting a better alignment between energy issues and strategic business priorities: e.g., improved competitiveness, profitability, quality, etc. Thus, energy efficiency can be a strategic advantage, not just a marginal issue, for companies. However, most firms, especially small and medium enterprises (SMEs), face many problems and, in some cases, hostility when trying to effectively implement energy efficiency actions. The most dominant barriers are the access to capital and the lack of awareness (especially in terms of life cycle cost effects). The supply chain viewpoint represents one of the main opportunities for overcoming those barriers and improving energy performance even for weaker companies. Since the current literature on energy efficiency and practical approaches to ensure energy efficiency mainly focus on energy performance on a single-firm basis, this paper aims to provide a systematic review of papers on the integration of energy efficiency in supply chain design and management published in academic journal, thereby defining potential research streams to close the gaps in the literature. A number of literature reviews have been published focusing on specific aspects of sustainable or on green supply chain management; however, to the best of our knowledge, no review has focused on the energy efficiency issue. Firstly, the present paper shows how considering energy consumption in supply chain management can contribute to more energy-efficient processes from a systemic point of view. Then, the review methodology used is defined and the sampled papers are analyzed and categorized based on the different approaches they propose. From these analyses, potential future research streams are outlined. [ABSTRACT FROM AUTHOR]

In this paper, carbon-containing pellets were prepared by using crop-derived charcoal made from agricultural residuals and iron ore concentrates, and their pelletizing performance and properties were studied. Experimental results showed that the strengths of pellets were related to the particle size of concentrates and the content of moisture, bentonite, and crop-derived charcoal fines in the pelletizing mixture and the temperature of roasting and reduction. That the granularity of raw materials was fine and the bentonite content increased was beneficial to the improvement of pellet strengths. The suitable molar ratio of carbon to oxygen was 1.0 and the proper proportioning ratios of moisture and binder were 8.0% and 6.5%, respectively. The pellet strengths increased accordingly with increasing the reduction temperature, and when the temperature reached 1200°C, accompanied by the fast reduction of iron and the formation of crystal stock, the dropping strength of product pellets was 15 times and the compressive strength was 1650 N; this may be improved by grinding of the concentrate, leading to acceptable strength for the blast furnace. [ABSTRACT FROM AUTHOR]

This paper presents experimental comparison of two machine crushing technologies: one-stage and two-stage. The study was carried on a model double-toggle jaw crusher which allows crushing forces, energy and toggle displacement to be measured. The main aim of the work was to determine the energy consumption of crushing process assuming a given level of fragmentation. Studies were performed on three rocks: granite 'Strzegom', limestone 'Morawica' and sandstone 'Mucharz'. The material tested had a cubic shape and average dimension of 90 mm. One-stage crushing was carried out for outlet slot er = 11 mm, and two-stage crushing for er = 24 mm and 11 mm. In the tests special design of variable profile moving jaw was used and fixed jaw was flat. The analysis of the results shows that taking into account energy consumption, it is better to use two-stage crushing process. For given materials energy consumption in the two-stage crushing process was reduced by 30%. [ABSTRACT FROM AUTHOR]

In this paper, the analysis of some possibilities for increasing the induction motor efficiency using production techno logical process modifications is reported. This approach is known as the "no tooling cost" (NTC) strategy because it does not require a complete redesign of new laminations with a consistent cost in terms of investments. The paper shows the results obtained by a full experimental approach, using "ad hoc" prototypes. The NTC design modification and the technological processes analyzed in this paper have been done on totally enclosed fan-cooled standard induction motors. Obviously, the original motors have been com- pared from the energetic point of view with these prototypes. The energetic performance has been measured in accordance with the WEE Std. 112-96 Method B. In particular, the following modifications, for obtaining an increase in efficiency, have been taken into consideration: rotor with copper bar included in the slot before the aluminum die cast, increase of the core axial length, and annealing of the stator core. [ABSTRACT FROM AUTHOR]

• Detailed energy consumption models for different appliance categories are developed. • Modular models can be used as building blocks enhancing their flexibility. • MILP models of appliance operation can be incorporated in energy management systems. • Models allow for the integrated optimization of all energy resources. • Models enable the participation in demand response programs. Demand response programs are essential to enable accommodating larger shares of variable power generation based on renewable sources, the deployment of which is imperative for decarbonizing the economy and mitigating global warming. Consumers/prosumers are increasingly exposed to and may benefit from time-differentiated energy prices aimed to induce changes in regular consumption patterns. These changes are also beneficial for retailers and grid operators in face of the variability of wholesale market prices, renewable energy availability and grid conditions. The optimization models to be implemented in autonomous home energy management systems require a rigorous modeling of appliance operation to generate effective load scheduling solutions, respecting their physical operation principles and use patterns in everyday life. A balance should be sought between the detail level of optimization models and the computational requirements to generate usable solutions having in mind their implementation in low-cost processors. This paper presents a comprehensive and modular set of mixed-integer linear programming models aimed at enabling their seamless incorporation in home energy management systems, allowing for the integrated optimization of all energy resources (exchanges with the grid, load management, electric vehicle and stationary battery, local microgeneration). Detailed energy consumption optimization models for shiftable, interruptible and thermostatic loads are presented, also including the power cost component and ways of dealing with user's discomfort. The modular models are presented in a building block manner enhancing the flexibility of their utilization in overall models with different objective functions encompassing the economic and comfort dimensions. Computational results are presented for a case study using actual data, which considers a time-of-use tariff with six periods. In addition to comparing with a plain tariff scheme, different consumer profiles are simulated to assess the impact of comfort requirements on cost. These results show that whenever consumers have the flexibility to change their consumption patterns, they are able to lower the net electricity bill by having an energy management system endowed with the models herein proposed to make optimized decisions on their behalf. [ABSTRACT FROM AUTHOR]

Most of the energy used to operate a cellular network is consumed by a base station (BS), and reducing the transmission power of a BS can therefore afford a substantial reduction in the amount of energy used in a network. In this paper, we propose a distributed transmit power control (TPC) algorithm inspired by bird flocking behavior as a means of improving the energy efficiency of a cellular network. Just as each bird in a flock attempts to match its velocity with the average velocity of adjacent birds, in the proposed algorithm, each mobile station (MS) in a cell matches its rate with the average rate of the co-channel MSs in adjacent cells by controlling the transmit power of its serving BS. We verify that this bio-inspired TPC algorithm using a local rate-average process achieves an exponential convergence and maximizes the minimum rate of the MSs concerned. Simulation results show that the proposed TPC algorithm follows the same convergence properties as the flocking algorithm and also effectively reduces the power consumption at the BSs while maintaining a low outage probability as the inter-cell interference increases; in so doing, it significantly improves the energy efficiency of a cellular network. [ABSTRACT FROM AUTHOR]

• Household indirect energy consumption is decomposed by input-output subsystem method. • Indirect consumption is 1.3 and 1.65 times of direct in 2012 and 2017 respectively. • Coal indirect consumption accounts for 50.2%, 54.6% in 2012 and 2017 respectively. • Spillover effect is the most one among the four components. • Ferrous metal smelting and rolling sector is most affected by household activities. In recent years, China's rapid economic development and steady growth of household consumption have contributed to the continuous growth of energy consumption. In addition to considering direct household energy consumption, the indirect effects of people's consumption activities on energy consumption in other productive sectors can't be ignored. According to the input-output analysis framework and consumer lifestyle analysis, this paper mainly studies the direct and indirect consumption of energy by Chinese household consumption ways and the relationship between energy structure and lifestyle. From the perspective of structural analysis, the input-output subsystem model (scale component, own internal component, feed-back component and spillover component) is used to analyze the energy supply and demand relations among 36 sectors of the national economy related to the lives of household in 2012 and 2017, revealing the change trend and transmission mechanism. This paper also focuses on the impact of household consumption ways on energy consumption of industrial sector and changing consumer lifestyles on energy saving, meanwhile it also forecasts China's future household energy consumption under four scenarios. The results show that in 2012 and 2017, total consumption of energy which is influenced by household consumption ways in China is 28461.82 PJ and 44759.96 PJ, respectively, accounting for 26.9% and 34.1% of total consumption of final energy. The indirect energy consumption is 1.3 times and 1.65 times of direct, respectively. The coal consumption accounts for the majority of indirect energy consumption. The smelting and pressing of ferrous metals is the industrial sector which is most affected by the household consumption ways. The scale component, own internal component, feed-back component and spillover component has accounted for 18.5%, 1.3%, 0.1% and 80.0%, from 17.5%, 2.0%, 0.2% and 80.3% of energy consumption in total, respectively. Under the four scenario development modes (NN, NA, LN and LA), it is predicted that the indirect energy consumption in household life in 2025 is 43740.56PJ, 34922.93PJ, 39149.37PJ and 31257.28PJ, respectively. The implementation of low-carbon consumption mode and the accelerated decline of energy intensity will help to reduce energy consumption. [ABSTRACT FROM AUTHOR]

The current paper presents the energy efficiency analysis of the Basic and Combined Thermic Treatment Processes. The calculation method of energy efficiency of Waste to Energy Technology is defined by the R 1 formula. This formula shall be applied according to the reference document on Best Available Techniques for waste incineration (2008/98/EC). It is also characterized by the energy conversion efficiency, the change of energy, and electricity intensity indicators. [ABSTRACT FROM AUTHOR]

The energy industry is undergoing a tremendous transformation from a centralized paradigm to a decentralized one with the employment of various distributed energy technologies and information technologies. Different from the traditional urban central heating system, the district heating system (DHS) with combined energy supply by multi-energy and multi-level energy stations is considered to be promising and can improve the efficiency and flexibility of the energy system. However, its complex system structure and multiple stakeholders bring new challenges to the operation optimization. Therefore, in the face of the DHS, two optimization models are applied in this study: 1) Independent Optimization Model (IOM): the operation strategies of each energy station are optimized independently, and each station then submits its own energy demand to the upper-level energy station; 2) Collaboration Optimization Model (COM): a model established for all energy stations for global optimization. A small number of existing papers on the operation optimization of such DHS have ignored the contradiction between social benefits and operators' profits, and do not study the gap between the two models. Therefore, this paper takes COM as the benchmark, quantitatively evaluating the potential of IOM for improving the overall energy efficiency under the interest game, and grasps the key reasons that restrict its energy saving potential, aiming to improve its comprehensive benefits. An actual DHS in China was used as a case study to explore the above problems. The results show that, although COM is more energy-saving, it loses 18.3% of the profits of energy integrators, and increases the profits of energy suppliers by 13.8%. Compared with COM, the primary energy consumption of IOM increases by 19.1%. For the results, this paper finally discusses whether it is possible to further improve the energy efficiency of IOM by adjusting the heat price without impairing the profits of the operators. The results indicate that the system's primary energy consumption reduces by 5.2% without impairing the profits of each operator. • The operation strategy of the multi-energy multi-level district heating system is optimized. • The contradiction between social benefits and operator profits is considered. • Independent optimization model and collaborative optimization model are established and quantitatively compared. • An actual case study is analyzed to explore why the energy saving potential of independent optimization is restricted. • The further improvement of independent optimization is discussed. [ABSTRACT FROM AUTHOR]

With the steady growth of energy demands and resource depletion in today's world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called Expect, to forecast energy consumption from time series effectively. Our results demonstrate our proposal's robustness and accuracy when compared to the baseline methods. [ABSTRACT FROM AUTHOR]

The carbon trading mechanism is considered an effective method for improving energy efficiency, and reducing pollution. In this regard, two methods of considering reward-penalty on the supply and demand sides as well as user satisfaction are the most effective ways to improve the carbon trading mechanism. Herein, this paper proposes a bilayer optimization model of the community-integrated energy service system. The population in the community is mainly divided into office workers and retirees, and their preferences are fully considered. The user considers electric energy reduction and substitution modes to optimize the energy consumption behavior in the reward stage. And the service provider considers the full consumption of wind and photovoltaic power and optimizes the output of each unit in the penalty stage. The effect of reward-penalty factors on the supply and demand sides is analyzed. The algorithm used to achieve bilayer optimization is the hybrid solution algorithm, which adopts the improved particle swarm optimization algorithm and CPLEX solver. The experimental results show that the costs of office workers and retirees are reduced by an average of 18.2% and 12.2%, respectively. And the service provider's profit is increased by 15.28%. The results also provide site selection strategies for energy supply. • A carbon trading mechanism that considers the reward-penalty is proposed. • An integrated energy service system that considers user satisfaction is proposed. • The total cost of users has dropped by an average of 12.72%. • The profit of the energy service provider has increased by an average of 15.28%. • The conclusions provide certain help of site location for energy supply. [ABSTRACT FROM AUTHOR]

With the rapid progress of socio-economic development and urbanization in China, a wide variety of literature has focused on the phenomenon of energy-consumption disparity, which in turn could be related to numerous fundamental energy dilemmas that China must deal with now or soon, including energy use inefficiency, regional energy shortage, insufficient use of green energy, etc. However, in most cases, only a tendency scenario is discussed, while identifying which factors are more likely to affect the improvement of energy supply/use has not yet been fully explored. Therefore, this paper attempts to explore differences in energy consumption in specific, household-level aspects. Based on the information provided by Chinese General Social Survey (CGSS2015), the Gini coefficient and the Lorentz asymmetry coefficient are used to measure the difference among various categories of energy type and end use within and between different geographical sub-groups. The findings show that: (1) household energy-consumption behaviors exhibit a complicated effect on the overall level of energy-consumption difference; (2) from the supply side, energy users show the most significant difference in biomass energy consumption, while from the demand side, the contribution of space heating to the difference in total energy consumption is the highest; (3) a great urban–rural difference in energy consumption is generally observed with its difference within rural areas being much greater than in urban areas; (4) the eastern, middle and western regions weight differently in explaining the overall difference of energy consumption. These findings provide meaningful materials and references for policymakers in China to understand the overall situation of individual energy consumption to a great extent, and to locate key points to reform the current energy policy framework. [ABSTRACT FROM AUTHOR]

This paper presents a day ahead optimal dispatch method for smart grids including two-axis tracking photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS) and electric vehicles (EV), which serve as additional storage systems in vehicle to grid (V2G) mode. The aim of the day ahead schedule is the minimization of fuel-based energy, imported from the main grid. The feasibility of the proposed method lies on the extensive communication network of the smart grids, including sensors and metering devices, that provide valuable information regarding the production of the distributed energy resources (DER), the energy consumption and the behavior of EV users. The day ahead optimal dispatch method is applied on a smart grid in order to showcase its effectiveness in terms of sustainability, full exploitation of DER production and ability of EVs to act as prosumers. [ABSTRACT FROM AUTHOR]

The direction of the causality between energy consumption and income is an important issue in the fields of energy economics, economic growth, and policies toward energy use. The seminal work on the relations between energy consumption and aggregate income is Kraft and Kraft (1978). An extensive literature has followed, but the array of findings provide anything but consensus on either the existence of relations or direction of causality between the variables. The work in this paper extends this research by analysing the cointegrating and causal relations between income and three energy consumption series based on panel data and the latest panel methodologies. These relations are analysed for the 30 OECD countries and 26 non-OECD countries. The results support a finding of causality flowing from income to energy consumption for developed and developing economies, alike. [ABSTRACT FROM AUTHOR]

This paper attempts to estimate a panel "frontier" whole economy aggregate energy demand function for 29 countries over the period 1978 to 2006 using parametric stochastic frontier analysis (SFA). Consequently, unlike standard energy demand econometric estimation, the energy efficiency of each country is also modeled and it is argued that this represents a measure of the underlying efficiency for each country over time, as well as the relative efficiency across the 29 OECD countries. This shows that energy intensity is not necessarily a good indicator of energy efficiency, whereas by controlling for a range of economic and other factors, the measure of energy efficiency obtained via this approach is. This is, as far as is known, the first attempt to econometrically model OECD energy demand and efficiency in this way and it is arguably particularly relevant in a world dominated by environmental concerns with the subsequent need to conserve energy and/or use it as efficiently as possible. Moreover, the results show that although for a number of countries the change in energy intensity over time might give a reasonable indication of efficiency improvements; this is not always the case. Therefore, unless this analysis is undertaken, it is not possible to know whether the energy intensity of a country is a good proxy for energy efficiency or not. Hence, it is argued that this analysis should be undertaken to avoid potentially misleading advice to policy makers. [ABSTRACT FROM AUTHOR]

Abstract: Many high-performance DSP processors employ multi-bank on-chip memory to improve performance and energy consumption. This architectural feature supports higher memory bandwidth by allowing multiple data memory accesses to be executed in parallel. However, making effective use of multi-bank memory remains difficult, considering the combined effect of performance and energy requirement. This paper studies the scheduling and assignment problem about how to minimize the total energy consumption while satisfying the timing constraint with heterogeneous multi-bank memory for applications with loop. An algorithm, TASL (Type Assignment and Scheduling for Loops), is proposed. The algorithm uses bank type assignment with the consideration of variable partition to find the best configuration for both memory and ALU. The experimental results show that the average improvement on energy-saving is significant by using TASL. [Copyright &y& Elsevier]

The K-Nearest Neighbors (KNN) query has been of significant interest in many studies and has become one of the most important spatial queries in mobile sensor networks. Applications of KNN queries may include vehicle navigation, wildlife social discovery, and squad/platoon searching on the battlefields. Current approaches to KNN search in mobile sensor networks require a certain kind of indexing support. This index could be either a centralized spatial index or an in-network data structure that is distributed over the sensor nodes. Creation and maintenance of these index structures, to reflect the network dynamics due to sensor node mobility, may result in long query response time and low battery efficiency, thus limiting their practical use. In this paper, we propose a maintenance-free itinerary-based approach called Density-aware Itinerary KNN query processing (DIKNN). The DIKNN divides the search area into multiple cone-shape areas centered at the query point. It then performs a query dissemination and response collection itinerary in each of the cone-shape areas in parallel. The design of the DIKNN scheme takes into account several challenging issues such as the trade-off between degree of parallelism and network interference on query response time, and the dynamic adjustment of the search radius (in terms of number of hops) according to spatial irregularity or mobility of sensor nodes. To optimize the performance of DIKNN, a detailed analytical model is derived that automatically determines the most suitable degree of parallelism under various network conditions. This model is validated by extensive simulations. The simulation results show that DIKNN yields substantially better performance and scalability over previous work, both as k increases and as the sensor node mobility increases. It outperforms the second runner with up to a 50 percent saving in energy consumption and up to a 40 percent reduction in query response time, while rendering the same level of query result accuracy. [ABSTRACT FROM AUTHOR]

Abstract: The implementation of the European Directive on the Energy Performance of Buildings (EPBD) is a milestone towards the improvement of energy efficiency in the building sector. However, even in cases where impressive measures can be implemented in the densely built urban environment, the less glamorous measure of building''s envelope thermal insulation remains a prerequisite towards the improvement of the building''s energy efficiency. Despite the insulation requirements specified by national regulations, thermal bridges in the building''s envelope remain a weak spot in the constructions. Moreover, in many countries construction practices tend to implement only partially the insulation measures foreseen by regulations. As a result, thermal losses are in practice greater than those predicted during the design stage. This paper presents a study on representative wall thermal insulation configurations used in Greek buildings, in order to investigate the impact of the thermal bridges on the energy consumption. The double wall construction, used widely in Greece and not only there, is rather susceptible to the occurrence of thermal bridges, in contrast to a typical thermal insulating façade, like the one applied in Central Europe. The analysis of the thermal bridges’ impact will in that sense also highlight the potential for energy renovation measures in older buildings. [Copyright &y& Elsevier]

Power consumption is an important issue for cluster supercomputers as it directly affects running cost and cooling requirements. This paper investigates the memory energy efficiency of high-end data servers used for supercomputers. Emerging memory technologies allow memory devices to dynamically adjust their power states and enable free rides by overlapping multiple DMA transfers from different I/O buses to the same memory device. To achieve maximum energy saving, the memory management on data servers needs to judiciously utilize these energy-aware devices. As we explore different management schemes under five real-world parallel I/O workloads, we find that the memory energy behavior is determined by a complex interaction among four important factors: 1) cache hit rates that may directly translate performance gain into energy saving, 2) cache populating schemes that perform buffer allocation and affect access locality at the chip level, 3) request clustering that aims to temporally align memory transfers from different buses into the same memory chips, and 4) access patterns in workloads that affect the first three factors. [ABSTRACT FROM AUTHOR]

The requirement of agile adaptation to varying resource constraints in mobile systems motivates the use of a service-oriented architecture (SOA), which can support the composition of two or more services to form a complex service. In this paper, we propose SOA-based middleware to support quality-of-service (QoS) control of mobile applications and to configure an energy-efficient service composition graph. We categorize services into two layers: functionality-centric services, which are connected to create a complex service to meet the user's intentions, and resource-centric services, which undertake distributed functionality-centric services in a way that increases the success rate of service composition while reducing contention at specific service nodes. We also present a service routing algorithm to balance the resource consumption of service providers on a service-overlay network. Through simulation of power-aware service composition using a realistic model based on ns-2 and traced data, we demonstrate that our approach can help both the mobile devices and the servers in a service-overlay network to reduce energy consumption without an increase in response time. [ABSTRACT FROM AUTHOR]

Reduction in energy consumption in electrical railway traction systems has become a vital need. The effort spent in providing better quality services implies increasing speed and, therefore, an increase in energy consumption. Various techniques are usually adopted to improve train performances. They span from modifying the vehicle structure to improving the motor. In this paper, a different way to reduce power absorption is presented, which is based on an appropriate interpretation of the traction diagram, along with the absorption curves. Measurement data are available by means of an onboard monitoring system that measures all the electromechanical quantities of the train course and sends them through a network connection to a remote control station. The evaluation of measurement uncertainty, the procedure to obtain the optimized protocol for train operation, and the experimental data obtained when the optimized protocol is applied are also presented. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an energy consumption change forecasting system using fuzzy logic to reduce the uncertainty, inconvenience and inefficiency resulting from variations in the production factors. The proposed fuzzy logic approach helps the manufacturer forecast the energy consumption change in the plant when certain production input factors are varied. Predictions given by the proposed system adopts the fuzzy rule reasoning mechanism so that any changes in the overall energy consumption will neither violate the stable power supply and production schedules nor result in energy wastage. To demonstrate how the fuzzy logic approach is applied to a manufacturing system, a case study of the energy consumption forecast in a clothing manufacturing plant has been conducted in an emulated environment. The result of the case indicates a percentage change in the plant’s energy consumption after analyzing three input parameters. This finding is able to provide a solid foundation on which decision makers and systems analysts can base suitable strategies for ensuring the efficiency and stability of a manufacturing system. [Copyright &y& Elsevier]

Abstract: Many factors influence the energy consumption of buildings, particularly those related to their design and use. Other factors, such as orientation and the nature of the surroundings, also have an influence, but these are often ignored. In this paper, we consider the influence of trees planted as a shelterbelt, using computer simulations to model the varying energy consumption of a building with or without the shelterbelt trees. It is concluded that a reduction of up to 18% in heating energy consumption can be made by planting an optimum shelterbelt of trees in appropriate situations. [Copyright &y& Elsevier]

A detailed study on the removal of oxides of nitrogen (NOx) from the exhaust of a stationary diesel engine was carried out using nonthermal-plasma (pulsed electrical-discharge plasma)-promoted catalytic process. In this paper, the filtered exhaust from the diesel engine is made to pass through a combination of nonthermal plasma reactor and a catalytic reactor connected in series. This combination is referred to as cascade. Two types of cascaded systems were studied. In one type, the plasma treating filtered exhaust was cascaded with a reduction catalyst V2O5/TiO2 using ammonia as reducing agent, and in the other type, the plasma treating filtered exhaust was cascaded with activated-alumina catalyst without any additive. Improved NOx-removal performance of both the cascaded processes and the role of nonthermal plasma in promoting catalysis are explained. Along with the NOx, total hydrocarbon and aldehydes were also removed. Furthermore, experiments were conducted at different temperatures and engine-loading conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel energy-efficient coverage-time optimized dynamic clustering scheme for two- tiered wireless sensor networks (WSNs) used in an outdoor monitoring application of home networking systems. The coverage-time is defined as the time until one of cluster heads (CH5) runs out of energy in clustering-based WSNs, thereby resulting in an incomplete coverage. DC-CTO scheme regulates cluster radii for balanced energy consumption among CHs to maximize coverage-time. There are several advantages of using the DC-CTO scheme. The first advantage is balanced energy consumption among CHs under NLP-based computations. The second advantage is minimized energy consumption in each CH to extend coverage-time. The last one is the guarantee of perfect coverage. If there is no area which is uncontrolled by a CH in a networked sensing field, we call it a perfect coverage. If there is an incomplete coverage, the event in the area cannot be identified. Therefore perfect coverage is important to robust monitoring systems. The novelty of DC-CTO scheme is demonstrated by various simulation-based performance evaluations. [ABSTRACT FROM AUTHOR]

Abstract: Energy-aware compilers are becoming increasingly important for embedded systems due to the need to meet a variety of design constraints on time, code size and power consumption. This paper introduces for the first time a trace-based, link-time compiler framework on binaries for embedded systems and evaluates its potential benefits in supporting energy optimisations, especially those that exploit the interaction between compilers and architecture. We present two algorithms for reducing leakage energy in functional units and data caches, respectively. Both algorithms work uniformly at the granularity of optimisation regions that are formed by the hot traces of a program. Our experimental results using Mediabench benchmarks show that good leakage energy savings can be achieved at the cost of some small performance and code size penalties. Furthermore, by varying the granularity of optimisation regions, which is a tunable parameter, embedded application programmers can make the tradeoffs between energy savings and these associated costs. [Copyright &y& Elsevier]

Hybrid electric vehicles (HEVs) have attracted a lot of attention due to environmental and efficiency reasons. Typically, an HEV combines two power trains, a conventional power source such as a gasoline engine, a diesel engine, or a fuel cell stack, and an electric drive system (involving a motor and a generator) to produce driving power with a potential of higher fuel economy than conventional vehicles. Furthermore, such vehicles do not re- quire external charging and thus work within the existing fueling infrastructures. The power-split power train configuration of an HEY has the individual advantages of the series and parallel types of HEV power train configurations. A sophisticated control system, however, is required to manage the power-split HEV power trains. Designing such a control system requires a reasonably accurate HEV system plant model. Much research has been done for developing dynamic plant models for the series and parallel types, but a complete and validated dynamic model for the power-split HEV power train is still in its infancy. This paper presents a power-split power train HEY dynamic model capable of realistically replicating all the major steady-state and transient phenomena appearing under different driving conditions. A mathematical derivation and modeling representation of this plant model and its components is shown first. Next, the analysis, verification, and validation through computer simulation and comparison with the data actually measured in the test vehicle at the Ford Motor Company's test track is performed. The excellent agreements between the model and the experimental results demonstrate the fidelity and validity of the derived plant model. Since this plant model was built by integrating the subsystem models using a system-oriented approach with a hierarchical methodology, it is easy to change subsystem functionalities. The developed plant model is useful for analyzing and understanding the dominant dynamics of the power train system, the interaction between subsystems and components, and system transients due to the change of operational state and the influence of disturbances. This plant model can also be employed for the development of vehicle system controllers, evaluation of energy management strategies, issue resolution, and verification of coded algorithms, among many other purposes. [ABSTRACT FROM AUTHOR]

The paper proposes an adaptive reception (AR) scheme for S-DMB systems. Layered streams are generated by adopting Scalable Video Coding (SVC) technique to transmit video data hierarchically over broadcasting channel(s). Depending on the terminal's type, the minimum amount of data frames are adaptively received by employing a frame selection control at the receiver. We quantitatively investigate the benefits in terms of power consumption and available viewing time with a case of S-DMB phones. [ABSTRACT FROM AUTHOR]

A cost optimization scheme for a microgrid is presented. Prior to the optimization of the microgrid itself, several schemes for sharing power between two generators are compared. The minimization of fuel use in a microgrid with a variety of power sources is then discussed. The optimization of a small power system has important differences from the case of a large system and its traditional economic dispatch problem. Among the most important differences is the presence of a local heat demand which adds another dimension to the optimization problem. The microgrid considered in this paper consists of two reciprocating gas engines, a combined heat and power plant, a photovoltaic array and a wind generator. The optimization is aimed at reducing the fuel consumption rate of the system while constraining it to fulfil the local energy demand (both electrical and thermal) and provide a certain minimum reserve power. A penalty is applied for any heat produced in excess of demand. The solution of the optimization problem strongly supports the idea of having a communication infrastructure operating between the power sources. [ABSTRACT FROM AUTHOR]

This paper analyses the interrelation between the transport system and measures of resource consumption such as material and energy consumption over a 60-year period (1937-1997) in the UK. Non-motorized transport and time consumption for mobility are estimated in addition to conventional measures of transport. During the period analyzed, the UK population grew by 20% while transport of goods and persons increased more than threefold and material and energy consumption almost doubled. The transport intensity of domestic material input (DMI) doubled to 300 ton-kilometers (tkm) per ton of DMI while the transport intensity of domestic energy consumption (DEC) doubled to 20 tkm per gigajoule (GJ) of DEC. Thus, while the material and energy intensity of GDP declined significantly, a well-established trend in many advanced countries, the transport intensity of materials and energy consumption rose. These findings suggest a close link between transport, economic development, and long-term structural transformations. In the case of personal transport, a “rebound effect” was also observed: whereas the average speed of transport has greatly increased, the average number of hours per day devoted to personal transport has not declined. [ABSTRACT FROM AUTHOR]