Academic literature on the topic 'Energy efficiency not elsewhere classified'
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Journal articles on the topic "Energy efficiency not elsewhere classified"
Bisio, G. "Exergy Analysis of Thermal Energy Storage With Specific Remarks on the Variation of the Environmental Temperature." Journal of Solar Energy Engineering 118, no. 2 (May 1, 1996): 81–88. http://dx.doi.org/10.1115/1.2848020.
Full textYang, Li, Zedao Shi, and Wen Qin. "Research on Building Insulation and Energy Efficiency." E3S Web of Conferences 118 (2019): 01012. http://dx.doi.org/10.1051/e3sconf/201911801012.
Full textSiwei, Han, Wang Linyu, Guo Lei, Liu Shuai, Song Guojun, and Song Tianyi. "Evaluation on the Energy Efficiency for Chinese cities." E3S Web of Conferences 143 (2020): 02016. http://dx.doi.org/10.1051/e3sconf/202014302016.
Full textShin, C. S., K. U. Kim, and Patrick Kwon. "Economic Analysis of Agricultural Tractors in South Korea: Classified Based on Energy Efficiency Grades." Applied Engineering in Agriculture 33, no. 5 (2017): 667–77. http://dx.doi.org/10.13031/aea.12026.
Full textMarín, M. F., H. Naya, E. A. Navajas, T. Devincenzi, A. C. Espasandin, and M. Carriquiry. "O45 Energy efficiency of Hereford heifers classified by paternal Residual Feed Intake." Animal - science proceedings 13, no. 3 (August 2022): 298–99. http://dx.doi.org/10.1016/j.anscip.2022.07.055.
Full textYang, Dingcheng, Chuanqi Zhu, Lin Xiao, Xiaomei Shen, and Tiankui Zhang. "An Energy-Efficient Scheme for Multirelay Cooperative Networks with Energy Harvesting." Mobile Information Systems 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5618935.
Full textPatel, Martin K., Jean-Sébastien Broc, Haein Cho, Daniel Cabrera, Armin Eberle, Alessandro Federici, Alisa Freyre, et al. "Why We Continue to Need Energy Efficiency Programmes—A Critical Review Based on Experiences in Switzerland and Elsewhere." Energies 14, no. 6 (March 21, 2021): 1742. http://dx.doi.org/10.3390/en14061742.
Full textHuang, Guo Qin, and Xi Peng Xu. "Analysis of Energy Consumption Efficiency in Diamond Circular Sawing." Solid State Phenomena 175 (June 2011): 67–71. http://dx.doi.org/10.4028/www.scientific.net/ssp.175.67.
Full textBortolini, Rafaela, Raul Rodrigues, Hamidreza Alavi, Luisa Felix Dalla Vecchia, and Núria Forcada. "Digital Twins’ Applications for Building Energy Efficiency: A Review." Energies 15, no. 19 (September 23, 2022): 7002. http://dx.doi.org/10.3390/en15197002.
Full textBánszki, Lívia, Tamás Rátonyi, and Endre Harsányi. "Evalution of energy for bioethanol production." Acta Agraria Debreceniensis, no. 51 (February 10, 2013): 77–80. http://dx.doi.org/10.34101/actaagrar/51/2066.
Full textDissertations / Theses on the topic "Energy efficiency not elsewhere classified"
Thakore, Renuka. "A strategic engagement model for delivering energy efficiency initiatives in the English housing sector." Thesis, University of Central Lancashire, 2016. http://clok.uclan.ac.uk/18647/.
Full textAzabany, Azad. "Economic analysis and environmental impact of energy usage in microbusinesses in UK and Kurdistan, Iraq." Thesis, University of Central Lancashire, 2014. http://clok.uclan.ac.uk/20475/.
Full textÖhman, Ben Sebastian. "Energy efficiency investments in the commercial real estate business : A study of decision drivers on the Swedish market." Thesis, Uppsala universitet, Industriell teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355254.
Full text(6623699), Juan Carlos Orozco. "Analysis of Energy Efficiency in Truck-Drone “Last Mile” Delivery Systems." Thesis, 2019.
Find full textTruck-drone delivery systems have the potential to improve how the logistics industry approaches the “last mile problem”. For the purposes of this study, the “last mile” refers to the portion of the journey between the last transportation hub and the individual customer that will consume the product. Drones can deliver packages directly, without the need for an underlying transportation network but are limited by their range and payload capacity. Studies have developed multiple truck-drone configurations, each with different approaches to leverage the benefits and mitigate the limitations of drones. Existing research has also established the drone’s reduction to package delivery time over the traditional truck only model. Two key model factors that have not been considered in previous research are the distribution of package demand, and the distribution of package weight. This study analyzes the drone’s impact to the energy efficiency of a package delivery system, which has taken a backseat to minimizing delivery time. Demand distribution dictates the travel distances required for package delivery, as well as the proportion of delivery locations that are in range for drone delivery. Package weight determines the energy consumption of a delivery and further restricts the proportion of drone eligible packages. The major contributions of this study are the development of a truck-drone tandem mathematical model which minimizes energy consumption, the construction of a population-based package demand distribution, a realistic package weight distribution, and a genetic algorithm used to solve the mathematical model developed for problems that are too computationally expensive to be solved optimally using an exact method. Results show that drones can only have a significant impact to energy efficiency in package delivery systems if implemented under the right conditions. Using truck-drone tandem systems in areas with lower package demand density affords the drone the potential for larger energy savings as larger portions of the truck distance can be replaced. Further, the lower density translates to greater differences between the road-restricted driving distance and the flying distance between delivery points. Finally, energy savings are highly dependent on the underlying package weight distribution of the system. A heavier average package weight increases the energy consumption of the system, but more importantly the portion of packages above the drone’s payload capacity severely limit the savings afforded by the incorporation of drones.
(9503810), Jose Adrian Chavez Velasco. "COMPREHENSIVE STUDY OF THE ENERGY CONSUMPTION OF MEMBRANES AND DISTILLATION." Thesis, 2020.
Find full textMolecular
separations are essential in the production of many chemicals and purified
products. Of all the available separation technologies, distillation, which is
a thermally driven process, has been and continues to be one of the most
utilized separation methods in chemical and petrochemical plants. Although
distillation and other commercial technologies fulfilled most of the current
separation needs, the energy-intensive nature of many molecular separations and
the growing concern of reducing CO2 emissions has led to intense research to
seek for more energy-efficient separation processes.
Among the emerging separation technologies alternative to distillation, there is special attention on non-thermally driven methods, such as membranes. The growing interest in non-thermal methods, and particularly in the use of membranes, has been influenced significantly from the widespread perception that they have a potential to be markedly less energy-intensive than thermal methods such as distillation. Even though many publications claim that membranes are more energy-efficient than distillation, except for water desalination, the relative energy intensity between these processes in the separation of chemical mixtures has not been deeply studied in the literature. One of the objectives of this work focuses on introducing a framework for comparative analysis of the energy intensity of membranes and distillation.
A complication generally encountered when comparing the energy consumption of membranes against an alternative process is that often the purity and recovery that can be achieved through a single membrane stage is limited. While using a multi-stage membrane process is a plausible solution to achieve both high purity and recovery, even for a simple binary separation, finding the most suitable multistage membrane process is a difficult task. This is because, for a given separation, there exists multiple cascades that fulfill the separation requirements but consume different amounts of energy. Moreover, the energy requirement of each cascade depends on the operating conditions. The first part of this work is dedicated to the development of a Mixed Integer Non-linear Program (MINLP) which allows for a given gaseous or liquid binary separation, finding the most energy-efficient membrane cascade. The permeator model, which is derived from a combination of the cross-flow model and the solution diffusion theory, and is originally expressed as a differential-algebraic equation (DAE) system, was integrated analytically before being incorporated in the optimization framework. This is in contrast to the common practice in the literature, where the DAE system is solved using various discretization techniques. Since many of the constraints have a non-convex nature, local solvers could get trapped in higher energy suboptimal solutions. While an option to overcome this limitation is to use a global solver such as BARON, it fails to solve the MINLP to the desired optimality in a reasonable amount of time for most of the cases. For this reason, we derive additional cuts to the problem by exploiting the mathematical properties of the governing equations and from physical insights. Through numerical examples, we demonstrate that the additional cuts aid BARON in expediting the convergence of branch-and-bound and solve the MINLP within 5%-optimality in all the cases tested in this work.
The proposed optimization model allows identifying membrane cascades with enhanced energy efficiency that could be potentially used for existing or new separations. In addition, it allows to compare the optimum energy consumption of a multistage membrane process against alternative separations methods and aid in the decision of whether or not to use a membrane system. Nevertheless, it should be noted that when a membrane process or any other non-thermal separation process is compared with a thermal process such as distillation, an additional complication often arises because these processes usually use different types of energies. Non-thermal processes, such as membranes, consume electrical energy as work, whereas thermal processes, such as distillations, usually consume heat, which is available in a wide range of temperatures. Furthermore, the amount of fuel consumed by a separation process strongly depends on how its supplied energy is produced, and how it is energy integrated with the rest of the plant. Unfortunately, common approaches employed to compare the energy required by thermal and non-thermal methods often lead to incorrect conclusions and have driven to the flawed perception that thermal methods are inherently more energy-intensive than non-thermal counterparts. In the second part of this work, we develop a consistent framework that enables a proper comparison of the energy consumption between processes that are driven by thermal and non-thermal energy (electrical energy). Using this framework, we refute the general perception that thermal separation processes are necessarily the most energy-intensive and conclusively show that in several industrially important separations, distillation processes consume remarkably lower fuel than non-thermal membrane alternatives, which have often been touted as more energy efficient.
In order to gain more understanding of the conditions where membranes or distillation are more energy-efficient, we carried out a comprehensive analysis of the energy consumed by these two processes under different operating conditions. The introduced energy comparison analysis was applied to two important separation examples; the separation of p-xylene/o-xylene, and propylene/propane. Our results showed that distillation is more energy favored than membranes when the target purity and recovery of the most volatile (resp. most permeable) component in the distillate (resp. permeate) are high, and particularly when the feed is not too concentrated in the most volatile (resp. most permeable) component. On the other hand, when both the recovery and purity of the most volatile (resp. most permeable) component are required at moderate levels, and particularly when the feed is highly enriched in the most volatile (resp. most permeable) component, membranes show potential to save energy as compared to distillation.
(10506350), Amogh Agrawal. "Compute-in-Memory Primitives for Energy-Efficient Machine Learning." Thesis, 2021.
Find full text(5930180), Ashish Ranjan. "Energy-efficient Memory System Design with Spintronics." Thesis, 2019.
Find full textModern computing platforms, from servers to mobile devices, demand ever-increasing amounts of memory to keep up with the growing amounts of data they process, and to bridge the widening processor-memory gap. A large and growing fraction of chip area and energy is expended in memories, which face challenges with technology scaling due to increased leakage, process variations, and unreliability. On the other hand, data intensive workloads such as machine learning and data analytics pose increasing demands on memory systems. Consequently, improving the energy-efficiency and performance of memory systems is an important challenge for computing system designers.
Spintronic memories, which offer several desirable characteristics - near-zero leakage, high density, non-volatility and high endurance - are of great interest for designing future memory systems. However, these memories are not drop-in replacements for current memory technologies, viz. Static Random Access Memory (SRAM) and Dynamic Random Access Memory (DRAM). They pose unique challenges such as variable access times, and require higher write latency and write energy. This dissertation explores new approaches to improving the energy efficiency of spintronic memory systems.
The dissertation first explores the design of approximate memories, in which the need to store and access data precisely is foregone in return for improvements in energy efficiency. This is of particular interest, since many emerging workloads exhibit an inherent ability to tolerate approximations to their underlying computations and data while still producing outputs of acceptable quality. The dissertation proposes that approximate spintronic memories can be realized either by reducing the amount of data that is written to/read from them, or by reducing the energy consumed per access. To reduce memory traffic, the dissertation proposes approximate memory compression, wherein a quality-aware memory controller transparently compresses/decompresses data written to or read from memory. For broader applicability, the quality-aware memory controller can be programmed to specify memory regions that can tolerate approximations, and conforms to a specified error constraint for each such region. To reduce the per-access energy, various mechanisms are identified at the circuit and architecture levels that yield substantial energy benefits at the cost of small probabilities of read, write or retention failures. Based on these mechanisms, a quality-configurable Spin Transfer Torque Magnetic RAM (STT-MRAM) array is designed in which read/write operations can be performed at varying levels of accuracy and energy at runtime, depending on the needs of applications. To illustrate the utility of the proposed quality-configurable memory array, it is evaluated as an L2 cache in the context of a general-purpose processor, and as a scratchpad memory for a domain-specific vector processor.
The dissertation also explores the design of caches with Domain Wall Memory (DWM), a more advanced spintronic memory technology that offers unparalleled density arising from a unique tape-like structure. However, this structure also leads to serialized access to the bits in each bit-cell, resulting in increased access latency, thereby degrading overall performance. To mitigate the performance overheads, the dissertation proposes a reconfigurable DWM-based cache architecture that modulates the active bits per tape with minimal overheads depending on the application's memory access characteristics. The proposed cache is evaluated in a general purpose processor and improvements in performance are demonstrated over both CMOS and previously proposed spintronic caches.
In summary, the dissertation suggests directions to improve the energy efficiency of spintronic memories and re-affirms their potential for the design of future memory systems.
(6185759), Manish Nagaraj. "Energy Efficient Byzantine Agreement Protocols for Cyber Physical Resilience." Thesis, 2019.
Find full textCyber physical systems are deployed in a wide range of applications from sensor nodes in a factory setting to drones in defense applications. This distributed setting of nodes or processes often needs to reach agreement on a set of values. Byzantine Agreement protocols address this issue of reaching an agreement in an environment where a malicious entity can take control over a set of nodes and deviates the system from its normal operation. However these protocols do not consider the energy consumption of the nodes. We explore Byzantine Agreement protocols from an energy efficient perspective providing both energy resilience where the actions of the Byzantine nodes can not adversely effect the energy consumption of non-malicious nodes as well as fairness in energy consumption of nodes over multiple rounds of agreement.
(8088431), Gopalakrishnan Srinivasan. "Training Spiking Neural Networks for Energy-Efficient Neuromorphic Computing." Thesis, 2019.
Find full textSpiking Neural Networks (SNNs), widely known as the third
generation of artificial neural networks, offer a promising solution to
approaching the brains' processing capability for cognitive tasks. With more
biologically realistic perspective on input processing, SNN performs neural
computations using spikes in an event-driven manner. The asynchronous
spike-based computing capability can be exploited to achieve improved energy
efficiency in neuromorphic hardware. Furthermore, SNN, on account of
spike-based processing, can be trained in an unsupervised manner using Spike
Timing Dependent Plasticity (STDP). STDP-based learning rules modulate the strength
of a multi-bit synapse based on the correlation between the spike times of the
input and output neurons. In order to achieve plasticity with compressed
synaptic memory, stochastic binary synapse is proposed where spike timing
information is embedded in the synaptic switching probability. A bio-plausible
probabilistic-STDP learning rule consistent with Hebbian learning theory is
proposed to train a network of binary as well as quaternary synapses. In
addition, hybrid probabilistic-STDP learning rule incorporating Hebbian and
anti-Hebbian mechanisms is proposed to enhance the learnt representations of
the stochastic SNN. The efficacy of the presented learning rules are
demonstrated for feed-forward fully-connected and residual convolutional SNNs
on the MNIST and the CIFAR-10 datasets.
STDP-based learning is limited to shallow SNNs (<5
layers) yielding lower than acceptable accuracy on complex datasets. This
thesis proposes block-wise complexity-aware training algorithm, referred to as
BlocTrain, for incrementally training deep SNNs with reduced memory
requirements using spike-based backpropagation through time. The deep network
is divided into blocks, where each block consists of few convolutional layers
followed by an auxiliary classifier. The blocks are trained sequentially using
local errors from the respective auxiliary classifiers. Also, the deeper blocks
are trained only on the hard classes determined using the class-wise accuracy
obtained from the classifier of previously trained blocks. Thus, BlocTrain
improves the training time and computational efficiency with increasing block
depth. In addition, higher computational efficiency is obtained during
inference by exiting early for easy class instances and activating the deeper
blocks only for hard class instances. The ability of BlocTrain to provide
improved accuracy as well as higher training and inference efficiency compared
to end-to-end approaches is demonstrated for deep SNNs (up to 11 layers) on the
CIFAR-10 and the CIFAR-100 datasets.
Feed-forward SNNs are typically used for static image recognition while recurrent Liquid State Machines (LSMs) have been shown to encode time-varying speech data. Liquid-SNN, consisting of input neurons sparsely connected by plastic synapses to randomly interlinked reservoir of spiking neurons (or liquid), is proposed for unsupervised speech and image recognition. The strength of the synapses interconnecting the input and liquid are trained using STDP, which makes it possible to infer the class of a test pattern without a readout layer typical in standard LSMs. The Liquid-SNN suffers from scalability challenges due to the need to primarily increase the number of neurons to enhance the accuracy. SpiLinC, composed of an ensemble of multiple liquids, where each liquid is trained on a unique input segment, is proposed as a scalable model to achieve improved accuracy. SpiLinC recognizes a test pattern by combining the spiking activity of the individual liquids, each of which identifies unique input features. As a result, SpiLinC offers comparable accuracy to Liquid-SNN with added synaptic sparsity and faster training convergence, which is validated on the digit subset of TI46 speech corpus and the MNIST dataset.
(8815964), Minsuk Koo. "Energy Efficient Neuromorphic Computing: Circuits, Interconnects and Architecture." Thesis, 2020.
Find full textBooks on the topic "Energy efficiency not elsewhere classified"
Office, General Accounting. Information security: Advances and remaining challenges to adoption of public key infrastructure technology : report to the Chairman, Subcommittee on Government Efficiency, Financial Management and Intergovernmental Relations, Committee on Government Reform, House of Representatives. Washington, D.C: The Office, 2001.
Find full textBook chapters on the topic "Energy efficiency not elsewhere classified"
McElroy, Michael B. "Power from the Sun Abundant But Expensive." In Energy and Climate. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780190490331.003.0015.
Full textAl-Khdour, Tayseer A., and Uthman Baroudi. "Literature Review of MAC, Routing and Cross Layer Design Protocols for WSN." In Wireless Sensor Networks and Energy Efficiency, 70–118. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0101-7.ch005.
Full textPonnusamy, Vasaki, Azween Abdullah, and Alan G. Downe. "Energy Efficient Routing Protocols in Wireless Sensor Networks." In Wireless Sensor Networks and Energy Efficiency, 237–61. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0101-7.ch010.
Full textAyaz, Muhammad, Azween Abdullah, and Ibrahima Faye. "A Taxonomy of Routing Techniques in Underwater Wireless Sensor Networks." In Wireless Sensor Networks and Energy Efficiency, 119–47. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0101-7.ch006.
Full textJung, Low Tang, and Azween Abdullah. "Wireless Sensor Networks." In Wireless Sensor Networks and Energy Efficiency, 305–28. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0101-7.ch014.
Full textObayya, Salah, Nihal Fayez Fahmy Areed, Mohamed Farhat O. Hameed, and Mohamed Hussein Abdelrazik. "Optical Nano-Antennas for Energy Harvesting." In Renewable and Alternative Energy, 161–96. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1671-2.ch006.
Full textGelan, Ayele Ulfata, and Ahmad Shareef AlAwadhi. "Distributional Effects of Reduction in Energy Subsidy." In Handbook of Research on Energy and Environmental Finance 4.0, 102–43. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8210-7.ch004.
Full textObayya, Salah, Nihal Fayez Fahmy Areed, Mohamed Farhat O. Hameed, and Mohamed Hussein Abdelrazik. "Optical Nano-Antennas for Energy Harvesting." In Innovative Materials and Systems for Energy Harvesting Applications, 26–62. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8254-2.ch002.
Full textPleșea, Doru Alexandru, Bogdan Cristian Onete, and Ion Daniel Zgură. "Smart Homes as a Solution for Sustainable and More Inclusive Retrofitting of Existing Buildings." In Retrofitting for Optimal Energy Performance, 97–120. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-9104-7.ch005.
Full textMcElroy, Michael B. "Plant Biomass as a Substitute for Oil in Transportation." In Energy and Climate. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780190490331.003.0018.
Full textConference papers on the topic "Energy efficiency not elsewhere classified"
Stillwell, Ashlynn S., and Michael E. Webber. "Feasibility of Wind Power for Brackish Groundwater Desalination: A Case Study of the Energy-Water Nexus in Texas." In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90158.
Full textPrill, Katarzyna. "CAN SHIP ENERGY EFFICIENCY MANAGEMENT PLAN BE CLASSIFIED AS QUALITY SYSTEM?" In SGEM2017 17th International Multidisciplinary Scientific GeoConference and EXPO. Stef92 Technology, 2011. http://dx.doi.org/10.5593/sgem2017/54/s23.042.
Full textHao, Wu, Chang Xiaoqing, and Xue Jiai. "Measurement and Analysis on Energy Efficiency of Elevators in Shanghai." In ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/esda2014-20342.
Full textFriedman, Barry, Lori Bird, and Galen Barbose. "Energy Savings Certificate Markets: Opportunities and Implementation Barriers." In ASME 2009 3rd International Conference on Energy Sustainability collocated with the Heat Transfer and InterPACK09 Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/es2009-90036.
Full textBhattacharjee, Suchismita, and Georg Reichard. "Socio-Economic Factors Affecting Individual Household Energy Consumption: A Systematic Review." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54615.
Full textJain, Samarth, Soumya Roy, Abhishek Aggarwal, Dhruv Gupta, Vasu Kumar, and Naveen Kumar. "Study on the Parameters Influencing Efficiency of Micro-Gas Turbines: A Review." In ASME 2015 Power Conference collocated with the ASME 2015 9th International Conference on Energy Sustainability, the ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2015 Nuclear Forum. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/power2015-49417.
Full textNalbandian, Rozina N., Karen U. Girgis, Benjamin T. Kong, Ulyses Aguirre, Adrian Gil C. Victorio, Justin Andrew Lee, and Reza Baghaei Lakeh. "Simulation of an ROC-Based Thermal Energy Storage System in Charge and Discharge Cycles." In ASME 2021 15th International Conference on Energy Sustainability collocated with the ASME 2021 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/es2021-63930.
Full textYoru, Yilmaz, T. Hikmet Karakoc, Arif Hepbasli, and Enis T. Turgut. "Energetic and Exergetic Analysis of Building Cogeneration Systems." In ASME 2008 2nd International Conference on Energy Sustainability collocated with the Heat Transfer, Fluids Engineering, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/es2008-54033.
Full textKakihara, Takahiro, and Kiyoshi Yanagihara. "Development of Bio-Mass Fuel for Small Displacement Engine to Reduce CO2: Feasibility of Disposed Alcoholic Beverages as Bio-Mass Source." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54736.
Full textAsher, William E., and Steven J. Eckels. "Analysis of Cavitating High Speed Liquid Flow Through a Converging-Diverging Nozzle." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-52060.
Full textReports on the topic "Energy efficiency not elsewhere classified"
Lucas, Brian. Behaviour Change Interventions for Energy Efficiency. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.138.
Full textBrosh, Arieh, Gordon Carstens, Kristen Johnson, Ariel Shabtay, Joshuah Miron, Yoav Aharoni, Luis Tedeschi, and Ilan Halachmi. Enhancing Sustainability of Cattle Production Systems through Discovery of Biomarkers for Feed Efficiency. United States Department of Agriculture, July 2011. http://dx.doi.org/10.32747/2011.7592644.bard.
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