Energy Efficiency Considerations Research Articles

Modelling Building Stock Energy Consumption at the Urban Level from an Empirical Study

Quantifying the energy consumption of buildings is a complex and multi-scale task, with the entire process dependent on input data and urban surroundings. However, most urban energy models do not account for the urban environment. This paper employs a physical-based, bottom-up method to predict urban building operating energy consumption, using imported topography to consider shading effects on buildings. This method has proven to be feasible and aligned well with the benchmark. Research also suggests that commercial and transport buildings have the highest energy use intensity, significantly more than residential and office buildings. Specifically, cooling demands far outweigh heating demands for these building types. Therefore, buildings in the commercial and transportation sectors would receive greater consideration for energy efficiency and improvements to the cooling system would be a priority. Additionally, the method developed for predicting building energy demand at an urban scale can also be replicated in practice.

The Tradeoff Power Distribution Strategy Based on the Genetic -Pareto Algorithm for a Dual-Motor Coupling-Propulsion Electric Vehicle

Abstract The dual-motor coupling-propulsion electric vehicle (DMCP-EV) has the merit of favorable energy efficiency due to its multiple operation modes. In the driving process, the performance of the DMCP-EV depends greatly on the power distribution to meet various driving requests. A power distribution coefficient is optimized by using the genetic-Pareto algorithm (GA-Pareto) algorithm. In addition to this effort, a power distribution strategy based on tradeoff optimization with comprehensive consideration of energy efficiency and ride comfort is proposed. Then, the sensitive analysis of optimized performance is conducted to obtain the three appropriate GA-Pareto algorithm parameters. The benchmark strategies are conducted to validate the proposed tradeoff power distribution strategy and the comparisons of three preference strategies with the hardware-in-loop (HIL) validation are performed. Results of the simulation and HIL experiments demonstrate that the proposed control strategies can address the tradeoff between energy-efficient and ride comfort for DMCP-EV.

Valuing sustainability Part 2: Australian valuers' perception of sustainability in valuation practice

PurposeThe research investigates valuers' understanding of the value of sustainability in property and its' consideration in valuation practice in Australia. This paper explores valuers' perceptions of the relationships between sustainability and market values, sustainability and valuation variables, and the value influence of industry sustainability certification schemes. Further, this paper tracks prevalence of certified buildings in Australian commercial markets and the evolution of valuers' knowledge of sustainability certifications used in Australia.Design/methodology/approachThis paper reports on the next rendition of a longitudinal study examining valuers’ practice in Australia. This research explores the evolution of Australian valuers' perception and knowledge of sustainability in valuation practice. The survey data has been periodically collected from practising valuers from 2007 to 2021. The survey questions investigate valuers' knowledge development, understanding, reporting and consideration of the relationship between sustainability and market value.FindingsThe results have identified the evolution of the influence of normative research on valuers' perceptions of the relationship between sustainability and value; with a clearer understanding emerging over time of where the value relationships are identified in valuation variables. Greater alignment between empirical Australian studies and valuers' perceptions of the influence of sustainability ratings on value, demonstrate the value connection for higher rated buildings under NABERS (energy rating) and Green Star. Whilst only 41% of the study's participants are including sustainability in their valuation reports, they include a higher level of commentary on building descriptions and initiatives, building ratings, and reporting of owner and tenant objectives, than in previous studies. Knowledge development relating to sustainability certification tool, NABERS was identified. This is likely linked to the introduction of mandatory disclosure legislation. This has also led to increased awareness and valuers' knowledge of the differences between the two key rating tools used in Australia.Research limitations/implicationsThe research has several limitations: firstly, recruitment of valuers and the number of valuers' responses has varied over time; secondly, due to collection methods respondents have a greater likelihood of having an interest in and knowledge of sustainability creating potential for positive bias; thirdly, respondents may have responded to the survey in different years, but due to anonymity there has been no ability to track this. The results provide insights into the Australian valuation profession but may not be fully representative of the profession overall in Australia.Practical implicationsThe broader agenda of net zero, climate change, mitigation and carbon requirements, whether driven by market forces or government legislation, are generating changes in property markets as investors' reconsider their positions and model the implications of carbon emissions on their bottom lines. Introductions of policy and legislation over time in the Australian context have led to changes in valuation practice and increasing consideration of energy efficiency and ratings in the valuation of assets. However, further guidance and research still is required in Australia to assist in the knowledge development of valuers, and their ability to consider the emerging effects of sustainability, net zero and other market driven objectives including legislation, and how these may affect or influence their evaluation of market evidence and thus property values.Originality/valueThe research has tracked valuers' understanding, knowledge, and consideration of sustainability and energy efficiency in valuation practice since 2007. In that time the research has found that, as the market has evolved and more rated buildings are built (or retrofitted), so too has valuers' knowledge and consideration in valuation practices evolved. Valuers are more engaged with industry rating tools such as NABERS. This suggests that the Australian mandatory disclosure policies have contributed to changes in the market, which are then interpreted by valuers and reflected in their perceptions and consideration of energy ratings in valuation practice.

Energy Efficiency and Reliability Considerations in Wireless Body Area Networks: A Survey.

In this paper, we have reviewed and presented a critical overview of “energy-efficient and reliable routing solutions” in the field of wireless body area networks (WBANs). In addition, we have theoretically analysed the importance of energy efficiency and reliability and how it affects the stability and lifetime of WBANs. WBAN is a type of wireless sensor network (WSN) that is unique, wherever energy-efficient operations are one of the prime challenges, because each sensor node operates on battery, and where an excessive amount of communication consumes more energy than perceiving. Moreover, timely and reliable data delivery is essential in all WBAN applications. Moreover, the most frequent types of energy-efficient routing protocols include crosslayer, thermal-aware, cluster-based, quality-of-service, and postural movement-based routing protocols. According to the literature review, clustering-based routing algorithms are the best choice for WBAhinwidth, and low memory WBAN, in terms of more computational overhead and complexity. Thus, the routing techniques used in WBAN should be capable of energy-efficient communication at desired reliability to ensure the improved stability period and network lifetime. Therefore, we have highlighted and critically analysed various performance issues of the existing “energy-efficient and reliable routing solutions” for WBANs. Furthermore, we identified and compiled a tabular representation of the reviewed solutions based on the most appropriate strategy and performance parameters for WBAN. Finally, concerning to reliability and energy efficiency in WBANs, we outlined a number of issues and challenges that needs further consideration while devising new solutions for clustered-based WBANs.

Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System

Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response. Cognitive Internet of things (CIoT) technologies including inherent characteristics of cognitive radio (CR) are potential candidates to develop a monitoring and early warning system (MEWS) that helps in efficiently utilizing the short response time to save lives during flash floods. However, most CIoT devices are battery-limited and thus, it reduces the lifetime of the MEWS. To tackle these problems, we propose a CIoT-based MEWS to slash the fatalities of flash floods. To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors, we formulate a resource assignment problem for maximizing energy efficiency. To solve the problem, at first, we devise a polynomial-time heuristic energy-efficient scheduler (EES-1). However, its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency. To enhance the energy efficiency of the proposed EES-1 scheme, we additionally formulate an optimization problem based on a maximum weight matching bipartite graph. Then, we additionally propose a Hungarian algorithm-based energy-efficient scheduler (EES-2), solvable in polynomial time. The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS, leading to the extended lifetime of the MEWS without loss of throughput performance.

Analysing the systemic implications of energy efficiency and circular economy strategies in the decarbonisation context

<abstract> <p>The Paris Agreement goals require a rapid and deep reduction in global greenhouse gas emissions. Recent studies have shown the large potential of circular economy to reduce global emissions by improving resource and material efficiency practices. However, most large-scale energy system and Integrated Assessment Models used for mitigation analysis typically ignore or do not adequately represent circular economy measures. This study aims to fill in this research gap by enhancing a leading global energy system model with a representation of energy efficiency and circular economy considerations. The scenario-based analysis offers an improved understanding of the potentials, costs and impacts of circular economy in the decarbonisation context. The study shows that enhanced energy efficiency and increased material circularity can reduce energy consumption in all sectors, but most importantly in the industrial sector. They can also reduce the required carbon price to achieve Paris goals and the dependence on expensive, immature, and risky technologies, like Carbon Capture and Storage. Circular economy measures should be properly integrated with broad climate policies to provide a holistic and self-consistent framework to deeply reduce carbon emissions.</p> </abstract>

6G and the UN SDGs: Where is the Connection?

Sustainability has entered all aspects of life, calling for an active approach from the wireless and mobile communications community to help in solving fundamental challenges facing societies. Societal, economic, and environmental aspects of sustainability have become increasingly important design criteria in developing future technologies, along with the United Nations Sustainable Development Goals (UN SDGs) framework that sets specific goals and targets to be achieved by 2030. The role of mobile communications is important in supporting nations and organizations in meeting the UN SDGs in a timely manner, but the whole ICT sector itself, with its critical role as the backbone of society, can create a significant sustainability burden. Research on the next-generation mobile communication networks (6G) has started, aiming at first deployments in 2030, in a new era where sustainability defines its development. Therefore, sustainability, especially through the UN SDGs, and the future 6G wireless networks, cannot be treated in isolation, but a clear connection between them is urgently needed. This paper extends from traditional green communications and energy efficiency considerations in wireless communications to establishing a close connection between 6G and the triple bottom line of economic sustainability, societal sustainability, and environmental sustainability. The paper outlines open research challenges for sustainable 6G development and provides a set of research questions encouraging especially the researchers and engineers in the wireless and mobile communications community to address to realize a sustainable future.

Energy efficiency and emissions control: The response of the second-hand containerships sector

We explore factors that impact on price determination of second-hand containerships. Employing data-driven selection techniques to secure a sound model specification, we make use of a population of 5431 containerships and 1680 transactions registered in Clarksons database during 2005Q1-2020Q4. We find that energy efficiency, ship-specific characteristics, and attributes associated with buyers' and shipbuilders' nationality are significant determinants of ship price. The impact of environmental regulations as a driver of second-hand containership prices is also revealed. Compliance with emission controls impacts second-hand containership prices strongly and positively. Energy efficiency considerations are found to be increasingly important determinants of containership prices in the S&P market in the backwash of the 2008 crisis.

Occupant-density-detection based energy efficient ventilation system: Prevention of infection transmission

Ventilation plays an important role in prevention and control of COVID-19 in enclosed indoor environment and specially in high-occupant-density indoor environments (e.g., underground space buildings, conference room, etc.). Thus, higher ventilation rates are recommended to minimize the infection transmission probability, but this may result in higher energy consumption and cost. This paper proposes a smart low-cost ventilation control strategy based on occupant-density-detection algorithm with consideration of both infection prevention and energy efficiency. The ventilation rate can be automatically adjusted between the demand-controlled mode and anti-infection mode with a self-developed low-cost hardware prototype. YOLO (You Only Look Once) algorithm was applied for occupancy detection based on video frames from surveillance cameras. Case studies show that, compared with a traditional ventilation mode (with 15% fixed fresh air ratio), the proposed ventilation control strategy can achieve 11.7% energy saving while lowering the infection probability to 2%. The developed ventilation control strategy provides a feasible and promising solution to prevent transmission of infection diseases (e.g., COVID-19) in public and private buildings, and also help to achieve a healthy yet sustainable indoor environment.

Analysis of electrical energy efficiency using LED in the bank building (Study case in Bank X)

This study aims to analyse the efficiency of electrical energy by utilizing LED lights in the bank building in replacing fluorescent type lamps which can later be used as a basis for consideration of energy efficiency. This research was conducted using a qualitative description method by observing and measuring initial conditions, then conducting an analysis to calculate efficiency. To carry out energy efficiency analysis begins with an ideal plan for the need for lighting intensity according to the standard using LED lamp. Research activities began by collecting data covering the location and specifications of the building, detailed drawings in the form of ass built drawings related to electricity, lighting, and lighting equipment’s throughout the building. Then measure lighting intensity and illumination power. Measurement data were analysed against SNI 6197-2011 standard and carried out a plan to replace existing lamps using LED type lamps as energy efficiency. Then draw conclusions about energy efficiency by replacing LED lights. Based on data analysis of lighting intensity that there is 104 rooms or 55.32 % of 188 rooms measured that have not been accord to standard SNI 6197-2011. And analysis of illumination power that there is 55 rooms or 29.26 % of 188 rooms measured that have not been accord to standard SNI 6197-2011. If all of TL fluorescent lamps are replaced by LED lamps, and some rooms that less illumination power are improved the with LED lamps, so the total efficiency of electrical energy in the Bank Building is approximately 18 (eighteen) percent.

A model-based multi-objective optimization of energy consumption and thermal comfort for active chilled beam systems

Active chilled beam (ACB) systems provide two highly-coupled cooling capacities due to its unique structure through combining primary air nozzle and cooling coil, whose energy consumption is difficult to be estimated and balanced against thermal comfort. This study considered the trade-off between energy consumption and thermal comfort as a multi-objective optimization problem and proposed a novel and practical solution by utilizing empirical energy models of the ACB system and an evolutional non-dominated sorting genetic algorithm II. The energy models are established for components of fans, pumps, and chillers based on fundamental equations which are validated by experimental data. The thermal comfort of each room is quantified by the predicted percentage dissatisfied (PPD) model. Chilled water flow rate, primary airflow rate, and room temperature in ACB systems are specifically chosen as control variables due to the control convenience. Besides, a parameterless selection strategy that considers both thermal comfort and energy consumption is proposed to select the most appropriate solution among Pareto optimal solutions. Three steady-state experiments with different heat load conditions are conducted. Compared to experienced operation, the proposed strategy demonstrates a maximum of 39.32% of energy saving and 12.21% of PPD reduction by increasing the water flow rate and room temperature, and reducing the primary airflow rate. Furthermore, this study investigates the distribution of capacities in ACB systems and suggests to assign more capacity to the cooling coil based on energy efficiency considerations. A good trade-off is achieved between energy consumption and thermal comfort through the proposed multi-objective optimization strategy.

Integration of LED-based lighting in academic buildings for energy efficiency considerations

This paper presents an assessment by integration of LED T8 tube lamps in academic buildings for energy efficiency considerations. The proposed project was implemented in conjunction with a national mission, national energy efficiency action plan (NEEAP) for 10-year implementation period of 2016-2025 in reducing allocation on subsidies for efficient expenditure towards energy-saving measures. A case of study in a real environment at one of the UiTM’s buildings as a target task area has been tested by replacing a LED lighting technology in consideration of current operating costs and power factor performances. From the results, LED T8 tubes have shown a better power factor of 0.89 compared to fluorescent lamps with about 0.61 and primarily driven by economic benefits in terms of cost savings of up to 50% with return-on-investment (ROI) 6 months after installation. By integrating this technology, nearly 50% of the total energy consumption in indoor lighting system could be significantly saved, thus reducing the total cost of electricity bills in the future within the target area.

PEPS: predictive energy-efficient parallel scheduler for multi-core processors

In multi-core processors, energy efficiency and performance consideration are essential issues. Usually, energy-saving techniques result in performance loss and vice versa. Therefore, energy delay product (EDP) is used broadly in many applications as a trade-off between energy saving and performance improvement. This paper presents a technique to perform work-stealing scheduling in the operating system kernel without needing any modification to the user-space program. The proposed scheduling uses predictive models to determine the optimal active number of cores and clock frequency of the processor as an optimum configuration at runtime for any running program to achieve the minimum EDP value. Since EDP is considered as a long-term metric, at runtime, in each specific time frame, PEPS uses the instruction per watt (IPW) to determine the best configuration. By using performance and power predicting models, PEPS finds the optimal configuration in terms of energy efficiency for the next time interval. Because different workloads at runtime have different behaviors and programs with different degrees of parallelization acted variously, the proposed method uses performance counters as a factor for workload characterization. Compared to the Linux scheduler, the proposed algorithm has up to 25% improvement in energy saving at the cost of 7% performance loss. Moreover, while reducing the temperature by 24%, it results in 19% improvement in EDP.

Scenario-based Simulation for Energy Optimization in Learning Factory Environments

Caused by the constantly rising energy prices and the demand for green products, the manufacturing industry has to increasingly deal with the topic of energy optimization. Thus, the focus is shifting to the improvement of production facilities in order to minimize resource consumption. When planning a more energy efficient production, it is advisable to set up a continuous monitoring system on the existing equipment to get an insight into the prevailing energy consumption. Based on this, optimization potentials can be identified. Different possibilities for increasing energy efficiency already exist, including e.g. the use of more efficient equipment or the optimal use of the facility. However, realistic assessments of saving potentials are a big challenge. In this paper, a virtual model of a learning factory is created to assess a realistic energy consumption profile. Using currently measured energy data and possible investment activities, scenarios for energy optimization in the assembly line are generated. By evaluating the scenarios using the virtual model, realistic saving potentials can be determined and evaluated, enabling investment planning to be strategically improved through the consideration of energy efficiency.

PARMA: Parallelization-Aware Run-Time Management for Energy-Efficient Many-Core Systems

Performance and energy efficiency considerations have shifted computing paradigms from single-core to many-core architectures. At the same time, traditional speedup models such as Amdahl's Law face challenges in the run-time reasoning for system performance and energy efficiency, because these models typically assume limited variations of the parallel fraction. Moreover, the parallel fraction, which varies dynamically in workloads, is generally unknown at run-time without application-level instrumentation. This article describes novel performance/energy trade-off models based on realistic architectural considerations, which describe the parallel fraction and speedup as functions of performance counter values available in modern processors, removing the need for application-level instrumentation. These are then used to develop a Parallelization-Aware Run-time Management (PARMA) approach. PARMA aims at controlling core allocations and operating voltage/frequency points for energy efficiency, according to the varying workload parallel fractions. The efficacy of our models and the PARMA approach is extensively validated using a number of PARSEC benchmark applications, involving two performance/energy trade-off metrics: energy-delay-product (EDP), typically used in high-performance applications and energy per instruction (EPI), suitable for energy-aware applications. Up to 48 and 68 percent improvements in EDP and EPI have been observed using the PARMA approach compared with parallelization-agnostic methods.

Preparation and characterizations of a novel temperature-tuned phase change material based on sodium acetate trihydrate for improved performance of heat pump systems

Application of latent heat thermal energy storage unit is regarded as an effective method to improve the coefficient of performance of hot water heat pump systems (HWHPS). With consideration of energy efficiency and domestic hot water usage, phase change materials with a melting temperature range of 45–48 °C are suitable for practical applications. The most commonly used PCMs, paraffin-based materials, are costly and have low latent heat density, which restrains their applications. This study therefore aims to prepare a novel sodium acetate trihydrate based composite PCM with suitable phase change temperature and cost-effectiveness for HWHPs. A series of SAT-based composites with varying mass fraction of potassium chloride (KCl), urea, disodium phosphate dodecahydrate (DSP) and carboxyl methyl cellulose (CMC) were prepared. Their thermophysical properties and stability were investigated by freezing-melting cycling tests, differential scanning calorimetry (DSC) and X-ray powder diffraction (XRD). The experimental results indicated that the optimized SAT-based CPCM with 8 wt% KCl, 3 wt% urea, 6 wt% CMC and 1.5 wt% DSP displayed a favorable phase change temperature of 47.8 °C and a higher latent heat of 242.0 kJ/kg. Meanwhile, the results demonstrated that it has extraordinary thermal cycling performance, negligible vibration in phase change temperature and latent heat, and good stability in chemical properties.

Many-objective optimization for scheduling of crude oil operations based on NSGA-Ⅲ with consideration of energy efficiency

Abstract Crude oil operations in refineries are characterized as a hybrid system since it contains both discrete-event and continuous processes, and it is extremely difficult to schedule such a system. For scheduling such a system, initially the discrete tasks to be performed during the scheduling horizon is unknown such that heuristics and meta-heuristics are not directly applicable, which further complicates its scheduling problem. Moreover, there are large number of objectives to be optimized, including the minimization of energy consumption due to that crude oil operations consume large amount of energy and therefore lead to large amount of emissions. Furthermore, the energy optimization problem is characterized as highly non-linearity. Hence, the scheduling problem of crude oil operations in refineries belongs to the many-objective optimization problems and it is extremely challenging. This paper addresses this challenging scheduling problem of crude oil operations. This scheduling problem is first converted to a discrete dynamic resource allocation problem such that meta-heuristics can be applicable. Then, with the results of large number of experiments, this work innovatively proposes an NSGA-Ⅲ-based optimization algorithm to efficiently solve the problem for Pareto-optimal solutions. By the proposed method, the genes in a chromosome are generated one by one and, when generating a gene, safeness check is done according to the derived safeness conditions such that each gene is feasible. In this way, the schedule feasibility can be ensured. An industrial case study is given to test its performance and comparison is made with the existing state-of-the-art algorithms for many-objective optimization problems. The results show its good performance in terms of convergence, solution diversity, time efficiency, and its applicability to real-life refinery scheduling problems.

A game theoretic approach for the duopoly pricing of energy-efficient appliances regarding innovation protection and social welfare

Recent developments in energy consumption ‘management’ have heightened the need for energy efficiency considerations in energy-intensive supply chains, according to the sustainable development framework. Therefore, policy-makers need to find out how they can improve these conditions, considering the resource limitations and energy requirements. According to these issues, we address a novel pricing model for new energy-efficient products, conventional models, and relative energy consumption under a tax-subsidy system. The products and energy services are made in a product supply chain consisting of an energy supplier and two manufacturers in a duopoly. Furthermore, the protection costs of innovation process such as copyright are investigated for the first time in this problem. Then, a multi-stage game model is developed considering two scenarios, based on different game structures. Finally, we solve the problem and provide a comprehensive analysis of the optimal values of variables and players’ profit. The results show that the first scenario has more advantages than the leader-follower competition. However, the second scenario results in less energy consumption than the first one. The findings also suggest that energy policies applied on the producer side are more effective than the consumer-side energy policies to improve energy-saving.

Energy-Aware Joint Clustering and Scheduling for Multicast Beamforming in Cloud-RAN Downlink

In cloud radio access network (Cloud-RAN), dense remote radio heads (RRHs) connect to baseband unit (BBU) pool through optical transport link, causing significant interference and power consumption in multicast downlinks. Coordinated beamforming with RRHs clustering is expected to be a key element towards improving network performance with more consideration of energy efficiency and interference management. This letter proposes an energy-aware joint clustering and scheduling (EA-JCS) scheme for multicast beamforming in Cloud-RAN downlink, which is formulated as a network utility maximization problem with fractional formulation and low-rank constraints. To efficiently solve this problem, we first develop a heuristic algorithm to provide sub-optimal performance by Dinkelbach’s transformation. To handle non-convex formulation, we propose an iterative approximation algorithm with linear approximation and constraints relaxation. The effectiveness of the proposed scheme is confirmed by simulations.

An artificial neural network based approach for energy efficient task scheduling in cloud data centers

Energy efficiency is considered as a crucial objective in cloud data centers as it reduces cost and meets the standard set in green computing. Task scheduling an important problem becomes more complex and critical under energy efficiency consideration. Key issues in recent research on energy efficient task scheduling are execution overhead and scalability. Machine learning has been widely employed for energy efficient task scheduling problem but mostly used to predict resource consumption only instead of deciding the schedule itself. However, we used the neural network to decide which resource should be assigned to given task independently. In this paper, we proposed an energy efficient independent task scheduler using supervised neural networks with the aim to reduce makespan, energy consumption, execution overhead and number of active racks. Proposed artificial neural network-based scheduler takes incoming task and current cloud environment state as input and predict the best computing resource for given task as output which compiles our aim. We used genetic algorithm to generate a huge dataset (∼18 million training instances) and trained our neural network on this dataset using back propagation algorithm with 99.9% accuracy. We simulated experiments on heavily loaded and lightly loaded cloud environment and compared with well-known approaches: Genetic algorithm, MinMIN-MINMin heuristic and Linear regression based energy efficient task schedulers. Results clearly indicate that proposed work outperforms considered algorithms. In heavily (lightly) loaded environment, it improves makespan by 59% (64%), energy consumption by 45% (71%), execution overhead by 88% (43%) respectively and number of active racks by 70%.