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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]

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]

The increasing data demand in recent years has resulted in a considerable rise in heterogeneous cellular network energy usage. Advances in heterogeneous cellular networks with renewable energy supplied from base stations offer the cellular communications sector interesting options. Rising energy consumption, fuelled by huge growth in user count as well as usage of data, has emerged as the most pressing challenge for operators in fulfilling cost-cutting and environmental-impact objectives. The use of minimum power relay stations or base stations in conventional microcells is intended to lower the cellular network's total energy usage. This paper examines the reasons, difficulties, and techniques for addressing renewable heterogeneous networks' energy cost reduction issue. Because of the variety of renewable energy and mobile traffic, the issue of reducing energy costs necessitates both spatial and temporal resource allotment optimization. In this paper, we proposed a new technique for reducing the energy consumption cost using the optimal time constraint algorithmic approach. We demonstrate that the proposed method has time as well as space complexity. Experimental simulations on actual databases with synthetic costs are used to confirm the usefulness and efficacy of our method. [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]

Abstract: In the frame of an enhanced environmental discussion regarding the existence and convenience of energy decoupling or dematerialization, this paper studies the past trends of global primary energy resources in relation to monetary and demographic variables in a top-down framework. This paper aims at contributing to the literature on dematerialization and energy sustainability from a dynamic perspective, with the purpose of shedding light on some questions, such as the real existence of an intergenerational energy dematerialization. To this purpose we use the phase diagrams of energy intensity and the product generational dematerialization (PGD) indicator for the period 1970–2011 for the global economy, the OECD and Latin American and Caribbean regions, China and India. While from energy intensity perspective a decoupling trend can be observed, we found no evidence for global intergenerational sustainable energy path in the long term. In this context, some questions related to the real impact of past and future environmental policies on energy consumption arise, especially in relation to developing countries. [Copyright &y& Elsevier]

Abstract: Per capita energy consumption is high in urban locations of any country. In this context, this article explores the deployment of standalone photovoltaic (PV) water pumping units in every household of a sustainable city. The various photovoltaic water pumping schemes and the domestic pumping requirements of a city in India are given in this paper. The peak shaving of load and reduction in line losses due to PV pump deployment on a secondary distribution transformer in a residential locality of the same city is investigated to bring out the advantages of the above policy initiative. The need for a legislation to install PV water pumps is thus brought out in this paper. [ABSTRACT FROM AUTHOR]

Efforts to reduce carbon intensity of electricity involve increasing shares of variable renewable energy (VRE), including a trend towards decentralized generation. Ensuring their high utilization necessitates addressing generation variability at different spatial and temporal scales, likely involving a coordination of multiple systems (e.g. flexible loads, storage, dispatchable generation). This paper presents a methodology for estimating the energy flexibility required from such a synchronized system. The simple data-based approach uses local load and VRE production patterns, and provides a regional assessment considering (1) indicators to evaluate energy flexibility requirements based on VRE self-consumption (2) visualization methods to observe consumption and production patterns' impact on flexibility requirements across day/week/year and (3) simple algorithms to estimate the energy flexibility requirements at different timescales. The approach is demonstrated for three Swiss distribution system operators, for both current and increased shares of VRE generation. The largest benefits for optimal self-consumption are realized for the energy flexibility timescale of 6–12 hrs. Medium- and long-term (seasonal) storage appears beneficial at VRE penetration levels beyond 40%. The proposed framework can be readily utilized to assess energy flexibility requirements of different regions, and serve as a basis for identifying a suitable mix of strategies needed to address them. • Variable renewable energy raises flexibility needs at various spatiotemporal scales. • Novel metrics evaluate energy flexibility needs using aggregated time-series data. • They consider the effect of spatial and temporal mismatch on renewable energy use. • A simple algorithm estimates how they evolve with increased shares of renewables. • As proof-of-concept, the method is applied to multiple case studies in Switzerland. [ABSTRACT FROM AUTHOR]

• Isolated hybrid diesel-PV grids investigated at varying levels of PV penetration. • Effect of PV penetration on specific fuel consumption and payback period examined. • IEEE 30-bus configuration used with generation/load data based on measured data. • Results demonstrate similar fuel consumption for different dispatch strategies. • PV penetration above 50% enables a calculated payback period of fewer than 2 years. Isolated large communities in the Amazon region of Brazil are power by Islanded grids powered by diesel generator systems. The costs of these systems are high due to the complex and unreliable fuel supply chain. Including large-scale photovoltaic installations in these isolated grids have the potential to increase the reliability and reduce the cost of electricity. This paper investigates the impact of PV generation insertion on the specific consumption of isolated thermoelectric power plants. A diesel thermoelectric power plant (DTPP) of 4 MW, supplying electric energy to an isolated grid, was used as a case study. Five Photovoltaic penetration levels are evaluated, considering the operational regulatory constraints, three diesel generators (A, B and C), two daily solar irradiance profiles and annual solar irradiance profiles, and two dispatch strategies. The power plant loading range is bounded by the Brazilian regulatory framework to comply with fuel consumption efficiency measures and to guarantee the reliable operation. In the evaluated scenarios, PV penetration was not subject to power curtailment measures controlled by the DTPP. However, the power plant loading range considers both variation of photovoltaic generation and load. The results presented in this paper indicate that maintaining a higher number of machines in operation can make the DTPP more reliable to absorb the PV generation variations, enabling the growth of the PV penetration on the electric system, reducing the DTPP's fuel consumption and return on investment (payback) of the PV system without change significantly the DTPP's specific consumption. The results also show that the quality of the diesel generator is more important to keep specific fuel consumption low than the dispatch strategy used for a given PV penetration. The replacement of Generator A (less efficient) for B (more efficient) together the PV penetration of 50%, for example, can achieve a yearly fuel consumption reduction superior to 1 million liters. For the same PV penetration, an average annual reduction of 0.78 million liters in DTPP's fuel consumption can be reached, with PayBacks that could come close to 1.4 years. [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]

By the advent of cloud computing and the numerous related web applications, data center networks (DCNs) are becoming complex to provide all-to-all communications between underlying devices; it is done by spending a huge amount of electricity consumption. Energy consumption management is the first class concern for cloud providers in this energy hungry devices and also for the green computing goals. In the large DCs, the power management of hundreds or even many thousands idle switches can be a promising approach toward overall cost reduction. The virtual machine placement (VMP) scheme which is aware of both VMs affinity and underlying network topology for co-hosting dependent VMs as physically near as possible and lowering down power state of idle devices can enhance sustainability objectives. On the other hand, resource wastage lowers system utilization which leads more physical server usage causing more power consumption as a consequence. To address the issue, this paper presents an energy-efficient topology-aware VM placement scheme in the cloud DCs which is formulated to a multi-objective optimization problem with power consumption and resource wastage minimization perspective. To deal with this combinatorial problem, an advanced multi-objective discrete version of JAYA (MOD-JAYA) algorithm is presented since the search space of VMP is discrete in nature. The proposed algorithm is validated by intensively variable circumstances and simulations upon conducted scenarios. The simulation results prove the superiority of the proposed advanced MOD-JAYA algorithm in solving VMP problem in comparison with other existing schemes in terms of prominent assessment metrics. • It presents server power consumption model by contributing processors as the chief electricity consumer. • It presents DCNs' power consumption model by contributing switches as variable electricity consumer between network elements. • It presents server dissipation model by considering processor and memory utilization. • It formulates VMP problem into a multi-objective optimization model with power consumption and resource dissipation perspective. • It presents an advanced multi-objective discrete JAYA optimization algorithm to solve the stated problem. [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]

Most of today's data centers are equipped with servers that rely on air cooling, which is well known to have low cooling efficiency due to undesired air recirculation. As a result, many data centers have started to adopt liquid cooling and free air cooling for improved cooling efficiency. In this paper, we make two key observations. First, since data centers normally replace only a portion of their servers at a time, an important problem is where in the data center to place those new liquid-cooled servers for the best return on their investment. Given the complex thermal dynamics in a data center, we find, in our process of deploying liquid-cooled servers, different placement strategies lead to significantly different cooling power consumption. Second, different cooling techniques, including traditional air cooling, liquid cooling, and the emerging free air cooling, must be intelligently coordinated with dynamic workload allocation in order to minimize the cooling and server power of a data center. Based on the two observations, we propose SmartPlace, an intelligent placement algorithm that deploys liquid-cooled servers to minimize the power consumption of the data center cooling system. SmartPlace also takes into account the coordination with free air cooling and dynamic workload distribution among servers for jointly minimized cooling and server power in the entire data center. We compare SmartPlace with a state-of-the-art cooling optimization solution for two data centers with 1,280 and 10,240 servers, respectively. The results show that SmartPlace achieves up to 26.7% (with an average of 15%) less total power consumption with dynamically guaranteed application response time. Our hardware testbed results also demonstrate the effectiveness of SmartPlace. Moreover, we analyze how soon a data center can gain a full return on the capital investment of liquid-cooled servers. [ABSTRACT FROM AUTHOR]

The game relationship between the Energy Server (ES) and the Energy Consumer (EC) in the Integrated Community Energy System (ICES) can change the energy supply and consumption behaviors of both participants, and affect the economy of the ICES planning scheme. This paper proposes a bi-level model to address a joint master-slave planning-operation problem of the ICES. Firstly, the revenue models of the ES and EC in the ICES is established. Secondly, a bi-level planning model based on Stackelberg Game for the ICES is proposed. In the upper level, a joint optimal planning model of energy conversion devices and energy prices is built and used by the ES (game leader) to maximize its profit. In the lower level, an optimal energy consuming strategy is proposed for the EC (game follower) to minimize its energy bill. The Karush-Kuhn-Tucker (KKT) conditions are utilized to convert the bi-level planning problem to a single-level mathematical program with equilibrium constraints (MPEC). A typical ICES is employed as a test system to illustrate the effectiveness of the proposed bi-level planning approach, and the results show that the proposed approach can reduce the investment cost of ES while ensuring the interests of EC. • A bi-level planning method for ICES based on Stackelberg game is proposed. • Revenue models of the ES and the EC are established. • A single level MPEC is derived by using the KKT conditions transformation. • An actual ICES shows the effectiveness of the proposed bi-level planning method. [ABSTRACT FROM AUTHOR]

• A P2P energy trading structure in a prosumer community with self-build PV and ESS. • An energy-trading model with utility maximum model and hierarchical pricing model. • ADMM with predictor–corrector mechanism is introduced to solve decentralized problems. With the continuous expansion of distributed energy resources (DERs) in the demand side, the distributed peer-to-peer (P2P) energy-trading market in prosumer community receives extensive research interests. A proper transaction structure and pricing model are inevitable. This paper proposes a bilateral energy-trading structure considering the energy trading agent (ETA) with self-built photovoltaic (PV) and energy storage system (ESS), to provide more sufficient energy transactions in community. A bilateral energy-trading model is also developed to make better use of local resources, including utility maximum model and hierarchical dynamic pricing model for the self-build energy-owed ETA and prosumers. Considering the personalized flexibilities of energy consumption and market contributions, the proposed pricing model focuses on the share of demand and supply market and demand response (DR) discount factor to make differentiated pricing decisions and promote market competitiveness. To solve the model, the distributed alternating direction method of multiplier (ADMM) with the predictor–corrector mechanism is realized. Finally, an example simulation verifies the effectiveness and advantages, that the proposed model can decrease the total cost by 5.11% and increase the ETA's income by more than 9.38% compared to other designed cases. [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]

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]

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]

Abstract: This paper develops an energy efficient and robust collaborative signal and information processing (CSIP) algorithm and applies it to vehicle classification applications. The conventional algorithms collaboratively process all the time-series data from every node in the network. This signal–noise ratio (SNR)-based CSIP algorithm (SNRCSIP) collaboratively processes only the extracted features from part of the nodes. This algorithm efficiently reduces the energy consumption compared with the conventional algorithms by reducing the traffic. Apart from the energy efficiency, we demonstrate the robustness of the SNRCSIP algorithm by giving the high correct recognition ratio between the tracked vehicle and the wheeled vehicle with the acoustic features extracted by an improved form of mel filter bank (MFB), which is rarely applied in vehicle classification applications. Experimental results show that the SNRCSIP algorithm greatly reduces the energy consumption and achieves quite satisfied correct recognition ratio with the features extracted by the improved MFB. [Copyright &y& Elsevier]

Abstract: This article analyses energy efficiency development in Lithuania since 1990 and the main legal, fiscal and financial and institutional measures implemented in Lithuania aiming to promote energy efficiency improvements. Energy intensity currently in Lithuania is almost 1.2 times higher than in EU-15 average but the trends are positive and energy intensity has declined by 2.25 times during the investigated period. The paper presents final and primary energy intensity trends in Lithuania and concludes that measures implemented in Lithuania had positive impact on energy efficiency improvements. The main driving forces for energy efficiency improvements in Lithuania were energy prices increase with regained independence form Russia and EU approximation. [Copyright &y& Elsevier]

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: This paper aims to verify the advantages of district heating and cooling (DHC) systems in terms of energy efficiency. From the measurement data, the parameters that characterize the energy efficiency of a heating/cooling plant are identified for DHC and an individual building. A simulation model that considers the difference in these parameters is developed. This model examines both the advantages and disadvantages of DHC systems and the effect of each parameter. The results show that the energy efficiency for cooling in DHC systems is superior to that in the case of individual cooling systems because of the “concentration effect” and “grade of operation”. [Copyright &y& Elsevier]

Abstract: Technology development may play a role in fuel reductions in transport, especially regarding automobiles. But these developments may be off-set by diverting technology gains into non-fuel saving vehicle features – “service attributes”. This paper estimates to what extent technological development, in the Swedish new car fleet, has resulted in lower fuel consumption or has improved service attributes. Changes between 1975 and 2002 are mapped through statistical analysis and modeling of a combination of sales statistics and vehicle attributes. About 35% of the effects of enhanced technology and design resulted in a net reduction in fuel consumption. The remaining 65% served to meet consumer demands for such things as increased passenger space and improved acceleration. [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]

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]

Abstract: This paper gives a brief introduction to the research methods used in this monograph, describes the geometric features and thermal characteristics of universal model buildings, makes detailed descriptions of the simulation assumptions in indoor setting conditions, operation mode and control strategies, introduces the concepts of annual, monthly and daily heating (cooling) energy consumption or hourly space load and the concepts of the relative variation rates of annual, monthly or daily heating (cooling) energy consumption in detail and summaries the location and weather features of the background cities involved in this monograph. [Copyright &y& Elsevier]

This paper reviews current heating system control practice in the UK through surveys, computer simulation and experimental studies. It is evident from the survey results that both boiler and heat emitter controls in the UK are generally poor. Computer simulation of a heating system demonstrated that the use of poorly commissioned boiler controls and the use of thermostatic valves with limited flow control leads to buildings not maintaining the desired internal temperatures. This results in occupant discomfort when under-heating occurs and excess fuel consumption when over-heating occurs. Finally, an experimental study demonstrated that a newly developed boiler control algorithm can offer significant energy savings by controlling the boiler more appropriately and reducing the effect of poor thermostatic radiator valve use. [Copyright &y& Elsevier]

Atmospheric corrosion of building materials is an enormously complex and interdisciplinary process. It affects not only the ability of building to perform over the prolonged period of time but also the overall energy consumption. The presented paper is a part of a larger study on application of acrylic plasters in passive solar systems. Absorption characteristics as well as microstructural features of acrylic plasters subjected to alternating climatic conditions have been analysed. In order to simulate the real conditions, samples were exposed to the variations of temperature and humidity, near-daylight radiation (xenon lamp) and subjected to acid and alkaline environments. [Copyright &y& Elsevier]