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Статті в журналах з теми "Energy consumption model":
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Gomez, Carles, Juan Carlos Veras, Rafael Vidal, Lluís Casals, and Josep Paradells. "A Sigfox Energy Consumption Model." Sensors 19, no. 3 (February 7, 2019): 681. http://dx.doi.org/10.3390/s19030681.
Sigfox has become one of the main Low-Power Wide Area Network (LPWAN) technologies, as it has attracted the attention of the industry, academy and standards development organizations in recent years. Sigfox devices, such as sensors or actuators, are expected to run on limited energy sources; therefore, it is crucial to investigate the energy consumption of Sigfox. However, the literature has only focused on this topic to a very limited extent. This paper presents an analytical model that characterizes device current consumption, device lifetime and energy cost of data delivery with Sigfox. In order to capture a realistic behavior, the model has been derived from measurements carried out on a real Sigfox hardware module. The model allows quantifying the impact of relevant Sigfox parameters and mechanisms, as well as frame losses, on Sigfox device energy performance. Among others, evaluation results show that the considered Sigfox device, powered by a 2400 mAh battery, can achieve a theoretical lifetime of 1.5 or 2.5 years while sending one message every 10 min at 100 bit/s or 600 bit/s, respectively, and an asymptotic lifetime of 14.6 years as the message transmission rate decreases.
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Lim, Ki Choo. "Development of Bottom-up model for Residential Energy Consumption by Use." Journal of Energy Engineering 22, no. 1 (March 31, 2013): 38–43. http://dx.doi.org/10.5855/energy.2013.22.1.038.
Jacewicz, Mariusz, Marcin Żugaj, Robert Głębocki, and Przemysław Bibik. "Quadrotor Model for Energy Consumption Analysis." Energies 15, no. 19 (September 28, 2022): 7136. http://dx.doi.org/10.3390/en15197136.
In this paper, a quadrotor dynamic model’s energy efficiency was investigated. A method for the design of the dynamic model which assures energy consumption estimation was presented. This model was developed to analyze the energy efficiency of the quadrotor during each maneuver. A medium-class quadrotor (4.689 kg) was used as a test platform. Thrust force correction factors obtained with FLIGHTLAB software were used to predict object behavior in forward flight. Model validation and long-duration flight tests in outdoor windy conditions are also presented. Monte-Carlo simulation was used to study the influence of uncertainties in model parameters on the simulation reliability. The developed model might be used for practical purposes (for example, energy-efficient coverage path planning).
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Li, Nan, Jing Zhao, and Neng Zhu. "Building Energy Consumption Prediction Evaluation Model." Advanced Materials Research 280 (July 2011): 101–5. http://dx.doi.org/10.4028/www.scientific.net/amr.280.101.
Building energy consumption prediction provides the possibility for regulating running condition of equipments in advance. Then the equipments will keep good movement and building energy consumption will reduce obviously. This paper built an energy consumption prediction evaluation model according to Matlab Artificial Neural Network Toolbox. The model was trained and simulated by operation data in June-September of 2008 and 2009 of a case building. Then it can be used to predict this building energy consumption by special data, such as meteorological characteristics of prediction year, operation load, operation time and energy consumption of last year. With more building samples, the model will be used in wide range of building energy consumption prediction.
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Darroudi, Seyed, Raül Caldera-Sànchez, and Carles Gomez. "Bluetooth Mesh Energy Consumption: A Model." Sensors 19, no. 5 (March 12, 2019): 1238. http://dx.doi.org/10.3390/s19051238.
The recent publication of the Bluetooth Mesh standard is a remarkable milestone in the evolution of Bluetooth Low Energy (BLE). As a new technology in the Internet of Things (IoT) market, it is crucial to investigate the performance of Bluetooth Mesh. However, while a fundamental feature of Bluetooth Mesh is its suitability for energy-constrained devices, this aspect has not yet been properly considered in the literature. In this paper, we model the current consumption, lifetime and energy cost per delivered bit of a battery-operated Bluetooth Mesh sensor node. The model is based on measurements performed on a real hardware platform. Evaluation results quantify the impact of crucial Bluetooth Mesh parameters. Among others, we have found that a sensor device running on a simple 235 mAh battery, and sending a data message every 10 s, can achieve a lifetime of up to 15.6 months, whereas the asymptotic lifetime is 21.4 months.
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Naseri, Alireza, Ramin Vafaeipour Sorkhabi, Arash Dalili, and Masoud Naseri. "Optimal Energy Consumption Model in Transport." Energy Procedia 61 (2014): 2379–82. http://dx.doi.org/10.1016/j.egypro.2014.12.009.
Ma, Yun Long, Xiao Hua Chen, Bo Liu, and Guo Feng Zhang. "Energy Efficiency Assessment Oriented Building Energy Consumption System Model." Applied Mechanics and Materials 415 (September 2013): 734–40. http://dx.doi.org/10.4028/www.scientific.net/amm.415.734.
This paper analyzes the characteristics and composition of the energy consumption system of the building from the perspective of systematic energy conservation and presents the systematic framework of the consumption model. Based on the framework, the paper focuses on how to establish a building energy consumption assessment system, find the energy efficiency index system and assessment approaches, and apply the results directly into building energy conservation and emission reduction. It not only facilitates greatly the overall and efficient management of the energy consumption system of the building, but also serves as another new approach to achieve energy conservation and emission reduction.
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Wardhany, Arum Kusuma, Iwa Garniwa, Rima Kurnia Putri, and Raja Dweyndo Hasintongan Gurning. "Energy Consumption Model for Open-Space Geometry Building: A Study Case in Jakarta." Journal of Clean Energy Technologies 6, no. 4 (July 2018): 344–48. http://dx.doi.org/10.18178/jocet.2018.6.4.486.
Kim, Ho-Young, Seul-Ye Lim, and Seung-Hoon Yoo. "Analysis of residential natural gas consumption distribution function in Korea - a mixture model." Journal of Energy Engineering 23, no. 3 (September 30, 2014): 36–41. http://dx.doi.org/10.5855/energy.2014.23.3.036.
Degefa, Mehari Weldemariam. "Ethiopian energy consumption forecast." Mehran University Research Journal of Engineering and Technology 41, no. 4 (October 1, 2022): 42. http://dx.doi.org/10.22581/muet1982.2204.04.
This Energy consumption forecast is vital and has a great economic impact. Mathematical models developed for energy forecast can also serve as inputs for further studies. This study is intended to develop an energy consumption forecast using the grey prediction model GM (1,1), based on the actual energy consumption data from the year 2008 to 2017. The models are developed for the total, solid biomass, oil products, and electrical energy consumption; and the accuracy for each model is ratified. These developed forecasting models were used to anticipate six-year Ethiopian consumption of major energy types. The outcomes of models for all four energy consumption types show an upward trend; simulating and forecasting are found suited with the grey system model with development coefficient values less than 0.3 for all selected energy forms.
Bae, Kyungcho. "Energy consumption forecasting: Econometric model vs state space model." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/187010.
This study examines the forecasting performance of two major multivariate methodologies: econometric modeling and multivariate state space modeling. The same variables are used in both models to facilitate comparison. They are evaluated by both expost and exante accuracy of U.S. energy consumption forecasts. Econometric models are highly simplified and a model selection procedure is applied to the models. Two different formats of multivariate state space models are examined: economic structure and identity structure. Goodrich's algorithm is employed to estimate the state space models. The state space models in both the econometric structure and the identity structure provided generally good estimates, usually, but not always, these forecasts were more accurate than those by the single econometric models.
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Csereklyei, Zsuzsanna, and Stefan Humer. "Modelling Primary Energy Consumption under Model Uncertainty." WU Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3706/1/wp147.pdf.
This paper examines the long-term relationship between primary energy consumption and other key macroeconomic variables, including real GDP, labour force, capital stock and technology, using a panel dataset for 64 countries over the period 1965-2009. Deploying panel error correction models, we find that there is a positive relationship running from physical capital, GDP, and population to primary energy consumption. We observe however a negative relationship between total factor productivity and primary energy usage. Significant differences arise in the magnitude of the cointegration coefficients, when we allow for differences in geopolitics and wealth levels. We also argue that inference on the basis of a single model without taking model uncertainty into account can lead to biased conclusions. Consequently, we address this problem by applying simple model averaging techniques to the estimated panel cointegration models. We find that tackling the uncertainty associated with selecting a single model with model averaging techniques leads to a more accurate representation of the link between energy consumption and the other macroeconomic variables, and to a significantly increased out-of-sample forecast performance. (authors' abstract) Series: Department of Economics Working Paper Series
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Tran, Martina. "Energy Consumption Optimizations for 5G networks." Thesis, Uppsala universitet, Signaler och System, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395146.
The importance of energy efficiency has grown alongside awareness of climate change due to the rapid increase of greenhouse gases. With the increasing trend regarding mobile subscribers, it is necessary to prevent an expansion of energy consumption via mobile networks. In this thesis, the energy optimization of the new radio access technology called 5G NR utilizing different sleep states to put base stations to sleep when they are not transmitting data is discussed. Energy savings and file latency with heterogeneous and super dense urban scenarios was evaluated through simulations with different network deployments. An updated power model has been proposed and the sensitivity of the new power model was analyzed by adjusting wake-up time and sleep factors. This showed that careful implementation is necessary when adjusting these parameter settings, although in most cases it did not change the end results by much. Since 5G NR has more potential in energy optimization compared to the previous generation mobile network 4G LTE, up to 4 sleep states was implemented on the NR base stations and one idle mode on LTE base stations. To mitigate unnecessary sleep, deactivation timers are used which decides when to put base stations to sleep. Without deactivation timers, the delay could increase significantly, while with deactivation timers the delay increase would only be a few percent. Up to 42.5% energy could be saved with LTE-NR non-standalone deployment and 72.7% energy with NR standalone deployment compared to LTE standalone deployment, while minimally impacting the delay on file by 1%.
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Gupta, Deepak Prakash. "Energy sensitive machining parameter optimization model." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4406.
Thesis (M.S.)--West Virginia University, 2005. Title from document title page. Document formatted into pages; contains ix, 71 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 67-71).
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Csereklyei, Zsuzsanna, and Stefan Humer. "Projecting Long-Term Primary Energy Consumption." WU Vienna University of Economics and Business, 2013. http://epub.wu.ac.at/3874/1/wp152.pdf.
In this paper we use the long-term empirical relationship among primary energy consumption,
real income, physical capital, population and technology, obtained by averaged
panel error correction models, to project the long-term primary energy consumption of 56
countries up to 2100. In forecasting long-term primary energy consumption, we work with four
different Shared Socioeconomic Pathway Scenarios (SSPs) developed for the Intergovernmental
Panel on Climate Change (IPCC) framework, assuming different challenges to adaptation and
mitigation. We find that in all scenarios, China, the United States and India will be the largest
energy consumers, while highly growing countries will also significantly contribute to energy
use. We observe for most scenarios a sharp increase in global energy consumption, followed
by a levelling-out and a decrease towards the second half of the century. The reasons behind
this pattern are not only slower population growth, but also infrastructure saturation and
increased total factor productivity. This means, as countries move towards more knowledge
based societies, and higher energy efficiency, their primary energy usage is likely to decrease as
a result. Global primary energy consumption is expected however to increase significantly in
the coming decades, thus increasing the pressure on policy makers to cope with the questions
of energy security and greenhouse gas mitigation at the same time. (authors' abstract) Series: Department of Economics Working Paper Series
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Tamaro, Courtney Alex. "Vehicle powertrain model to predict energy consumption for ecorouting purposes." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71635.
The automotive industry is facing some of the most difficult design challenges in industry history. Developing innovative methods to reduce fossil fuel dependence is imperative for maintaining compliance with government regulations and consumer demand. In addition to powertrain design, route selection contributes to vehicle environmental impact.
The objective of this thesis is to develop a methodology for evaluating the energy consumption of each route option for a specific vehicle. A 'backwards' energy tracking method determines tractive demand at the wheels from route requirements and vehicle characteristics. Next, this method tracks energy quantities at each powertrain component. Each component model is scalable such that different vehicle powertrains may be approximated. Using an 'ecorouting' process, the most ideal route is selected by weighting relative total energy consumption and travel time.
Only limited powertrain characteristics are publicly available. As the future goal of this project is to apply the model to many vehicle powertrain types, the powertrain model must be reasonably accurate with minimal vehicle powertrain characteristics. Future work expands this model to constantly re-evaluate energy consumption with real-time traffic and terrain information.
While ecorouting has been applied to conventional vehicles in many publications, electrified vehicles are less studied. Hybrid vehicles are particularly complicated to model due to additional components, systems, and operation modes. This methodology has been validated to represent conventional, battery electric, and parallel hybrid electric vehicles. A sensitivity study demonstrates that the model is capable of differentiating powertrains with different parameters and routes with different characteristics. Master of Science
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Jansén, Ekberg Ida. "Energy Consumption Model of Cutting Fluid in a Machining System." Thesis, KTH, Industriell produktion, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228059.
Idag uppmärksammas energiförbrukningen allt mer och mer, både inom privata och industriella sektorer. Emellertid ägnas inom industrin mycket lite uppmärksamhet åt skärvätskesystem. Alltsom oftast är studier gällande vätskesystem mer intresserade av den mängd vätska som används, inte mängden energi.I denna avhandling var syftet att skapa en interaktiv modell som gör det enkelt för användaren att kartlägga energikonsumtionen i sina skärvätskesystem och spåra trender av komponenter av hög energiförbrukning. Modellfunktionerna är huvudsakligen baserade på resultaten av företagsbesök och online litteraturforskning. Modellen har skapats i Microsoft Excel. När modellen byggts användes den för att analysera de observerade systemen. Modellen beräknade energiförbrukningarna och identifierade även pumpar som de högst energikrävande komponenterna för de flesta systemen.Emedan modellen är ganska enkel som den är, beror detta på brist av befintlig information och oförmågan att få information gällande systemdesign och layout på ett en rimligtvis enkelt sätt. Framtida expansioner av modellen diskuteras, och är främst i fråga om utbyte av programvara. In the current environment, energy consumption is gaining more and more attention, both within private and industrial settings. However, within the industrial world, very little attention is being paid to cutting fluid systems. More often than not, fluid system studies are more concerned with the amount of fluid used, not the amount of energy.In this thesis, the purpose was to create an interactive model that makes it easy for the user to map down the energy consumption of their cutting fluid systems and track down trends of components of high energy consumption. The model features are mainly based on the results of company visits and online literature research. The model itself is created in Microsoft Excel.Once the model was built, it was utilized to analyze the observed systems. The model calculated the energy consumptions, and also identified pumps as the major energy consuming components for the majority of the systems.While the model is fairly simple as it stands, this is due to lack of existing information and the inability to gain data rearing system design and layout in a reasonably simplistic form. Future expansion of the model is discussed, and are mainly in regard of software data exchange.
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Parthasarathy, Prithwick. "Model for energy consumption of 2D Belt Robot : Master’s thesis work." Thesis, Högskolan Väst, Avdelningen för produktionssystem (PS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-9871.
A production industry with many robots working 24 hours a day, 7 days a week consumes a lot of energy. Industries aim to reduce the energy consumed per machine so as to support their financial budgets and also to be a more sustainable, energy efficient entity. Energy models can be used to predict the energy consumed by robot(s) for optimising the input parameters which determine robot motion and task execution. This work presents an ener-gy model to predict the energy consumption of 2D belt robots used for press line tending. Based on the components' specifications and the trajectory, an estimation of the energy consumption is computed. As part of this work, the proposed energy model is formulated, implemented in MATLAB and experimentally validated. The energy model is further used to investigate the effect of tool weight on energy consumption which includes predicting potential energy reductions achieved by reducing the weight of the gripper tools. Further, investigation of potential energy savings which can be achieved when mechanical brakes are used when the robot is idle is also presented. This illustrates the purpose and usefulness of the proposed energy model.
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Mardikar, Yogesh Mukesh. "Establishing baseline electrical energy consumption in wood processing sawmills a model based on energy analysis and diagnostics /." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5412.
Thesis (Ph. D.)--West Virginia University, 2007. Title from document title page. Document formatted into pages; contains xvi, 222 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 203-207).
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Alcántar-Sánchez, Karla. "Housing energy consumption, social behaviour and C02 emissions : a holistic model." Thesis, University of East Anglia, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501749.
Energy use in houses causes the indirect emission of Carbon Dioxide (CO2) and there is an increasing need to develop effective models to predict such emissions to establish benchmarks against which reductions can be targeted. The Prometheus model described in this thesis is such a tool which was developed under a holistic approach integrating the physical conditions of the house, climatic variations and also social variables all of which affect consumption. The model is sufficiently robust that with minimal modification in the extension of underlying databases it could be used in other areas within the UK.
Tessmer, Raymond G. The TVA strategic analysis model: Its structure and uses. [Chattanooga, Tenn.?]: System Forecasting Group, Tennessee Valley Authority, 1985.
Sliwinski, B. A model of U.S. Army Materiel Command (AMC) energy consumption. Champaign, Ill: US Army Corps of Engineers, Construction Engineering Research Laboratory, 1986.
United Nations. Economic Commission for Africa. Development of the energy balance statistics and energy systems model for the Union of Comoros. Moroni, Comoros]: United Nations Economic Commission for Africa, 2018.
Robertson, Joseph. Evaluation of automated model calibration techniques for residential building energy simulation. Golden, CO: National Renewable Energy Laboratory, 2013.
Organisation for Economic Co-operation and Development. Dept. of Economics and Statistics. Use of the Edmonds-Reilly model to model energy-related greenhouse gas emissions. Paris: Organisation for Economic Co-operation and Development, 1992.
Espada, Ana Rosario, María del Mar Gallardo, Alberto Salmerón, and Pedro Merino. "Runtime Verification of Expected Energy Consumption in Smartphones." In Model Checking Software, 132–49. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23404-5_10.
Nakajima, Shin. "Model Checking of Energy Consumption Behavior." In Complex Systems Design & Management Asia, 3–14. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12544-2_1.
Zheng, Xiang-wei, Zhen-hua Chen, Dian-jie Lu, and Hong Liu. "Virtual Network Embedding Model Minimizing Energy Consumption." In Human Centered Computing, 646–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15554-8_53.
Thabet, Mohamad, David Sanders, and Victor Becerra. "Analytical Model for Compressed Air System Analysis." In Springer Proceedings in Energy, 99–104. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_13.
AbstractThis paper presents a simple analytical model for a compressed air system (CAS) supply side. The supply side contains components responsible for production, treatment and storage of compressed air such as a compressor, cooler and a storage tank. Simulation of system performance with different storage tank size and system pressure set-point were performed. Results showed that a properly sized tank volume reduces energy consumption while maintaining good system pressure stability. Moreover, results also showed that reducing system pressure reduced energy consumption, however a more detailed model that considers end-user equipment is required to study effect of pressure set-point on energy consumption. Future work will focus on developing a supply-demand side coupled model and on utilizing model in developing new control strategies for improved energy performance.
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Mrazek, Michal, Daniel Honc, Eleonora Riva Sanseverino, and Gaetano Zizzo. "Predictive Model of Energy Consumption of a Home." In Software Engineering Perspectives in Intelligent Systems, 531–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63319-6_49.
Wang, Yan, Cheng-Lin Liu, and Zhi-Cheng Ji. "Energy Consumption Model of the Discrete Manufacturing System." In Quantitative Analysis and Optimal Control of Energy Efficiency in Discrete Manufacturing System, 51–102. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4462-0_3.
Bhosale, Varsha, and Vijay Raisinghani. "A Linear Energy Consumption Model for 802.15.4 MAC." In Innovations in Computer Science and Engineering, 343–51. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8201-6_39.
Sun, Liang, Ling Lin, Liang Chen, Mei Liu, Wei Bao, and Li Wang. "Railway Energy Consumption Analysis Based on Regression Model." In Green Intelligent Transportation Systems, 925–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3551-7_74.
Rao, Chavala Lalithya, Kurapati Sainath Raju, Pragati Mishra, P. S. G. Aruna Sri, and V. A. Narayana. "Building Energy Consumption Prediction Model Using Machine Learning." In Cybernetics, Cognition and Machine Learning Applications, 9–18. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1484-3_2.
Run, Keovathana, Franck Cévaër, and Jean-François Dubé. "Preliminary Multiple Linear Regression Model to Predict Hourly Electricity Consumption of School Buildings." In Future Energy, 119–27. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33906-6_10.
Тези доповідей конференцій з теми "Energy consumption model":
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Jia, Taihua. "Analysis of Energy Consumption Model and Energy Consumption Characteristics of Electric Vehicle." In 8th International Conference on Social Network, Communication and Education (SNCE 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/snce-18.2018.181.
NIKBAY OYLUM, Kader, and Bekir Tevfik AKGUN. "Estimation of Home Energy Consumption and a Consumption Model Proposal." In 2020 Turkish National Software Engineering Symposium (UYMS). IEEE, 2020. http://dx.doi.org/10.1109/uyms50627.2020.9247019.
Reviriego, P., V. Sivaraman, Z. Zhao, J. A. Maestro, A. Vishwanath, A. Sanchez-Macian, and C. Russell. "An energy consumption model for Energy Efficient Ethernet switches." In 2012 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2012. http://dx.doi.org/10.1109/hpcsim.2012.6266897.
Zhou, Mengting, Tao Jia, Tian Li, Qianqian Guo, Yinghui Cao, and Yunxiao Wang. "Operation Energy Consumption Calculation Model Based on Energy Conservation." In 2023 2nd International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT). IEEE, 2023. http://dx.doi.org/10.1109/ccpqt60491.2023.00049.
Marcu, Marius, and Dacian Tudor. "Energy consumption model for mobile wireless communication." In the 9th ACM international symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2069131.2069168.
Jwo, Jung-Sing, Jing-Yu Wang, Chun-Hao Huang, Shyh-Jon Two, and Hsu-Cheng Hsu. "An Energy Consumption Model for Enterprise Applications." In 2011 IEEE/ACM International Conference on Green Computing and Communications (GreenCom). IEEE, 2011. http://dx.doi.org/10.1109/greencom.2011.44.
Kara, D., and P. Baxendale. "An agent based building energy consumption model." In 2012 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). IEEE, 2012. http://dx.doi.org/10.1109/isgt-asia.2012.6303237.
Muhammad Mehar, Arshad, Asif Qumer Gill, and Kenan Matawie. "Analytical Model for Residential Predicting Energy Consumption." In 2018 IEEE 20th Conference on Business Informatics (CBI). IEEE, 2018. http://dx.doi.org/10.1109/cbi.2018.10049.
Abousleiman, Rami, and Osamah Rawashdeh. "Energy consumption model of an electric vehicle." In 2015 IEEE Transportation Electrification Conference and Expo (ITEC). IEEE, 2015. http://dx.doi.org/10.1109/itec.2015.7165773.
Wang, Tao, Shuai Tang, Xuefei Liu, Guangyi Li, Tong Zhao, and Pengfei Sun. "Comprehensive Energy flow Optimization Model Based on Energy Consumption Structure." In 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE, 2019. http://dx.doi.org/10.1109/icicas48597.2019.00050.
Звіти організацій з теми "Energy consumption model":
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Oakil, Abu Toasin, Ahm Mehbub Anwar, Alma Alhussaini, Nourah Al Hosain, Abdelrahman Muhsen, and Anvita Arora. Urban Transport Energy Demand Model for Riyadh: Methodology and Preliminary Analysis. King Abdullah Petroleum Studies and Research Center, June 2023. http://dx.doi.org/10.30573/ks--2023-mp03.
Saudi Arabia intends to reduce its greenhouse gas (GHG) emissions by 278 million tonnes of CO2 equivalent annually by 2030, according to its Nationally Determined Contribution to the United Nations Framework Convention on Climate Change (UNFCCC). Among many policies it is introducing, a mass transit system and transit-oriented development are being advanced with the expectation of reducing energy consumption and GHG emissions in Riyadh. To what extent such an initiative can reduce energy consumption and GHG emissions is an important question. In this paper, a methodology is developed to systematically measure the impact of mass transit and transit-oriented development in Riyadh on energy demand.
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Islam, Md Imranul, Sarif Ullah Patwary, and Melody L. A. LeHew. Energy Consumption Model for Apparel Sewing Process: An Approach to Environmental Sustainability. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-1875.
Stermieri, Lidia, Tom Kober, Russell McKenna, Thomas J. Schmidt, and Evangelos Panos. Socio-economic energy model for digitalization (SEED) overview design concept and details (ODD) protocol. Paul Scherrer Institute, PSI, December 2023. http://dx.doi.org/10.55402/psi:56617.
The Socio-Economic Energy Model for Digitalization (SEED) quantifies digitalization's impacts on technology investment choices, energy consumption, and emissions in different energy sectors. It belongs to the class of Agent Based Models and simulates the decision processes of various heterogenous actors of the energy system: households, services sectors and industry sectors. These decision processes relate to the adoption of digital services and practices, as well as the investment in end-use energy technologies to support them. The model represents a large set of complex interactions between its actors, such as peereffects, producer-consumer relationships and employee-employer relationships. It can also be linked to the Swiss TIMES energy system model (STEM) to evaluate broader implications to the energy supply and infrastructure arising from the decisions of the SEED actors. The current article formally describes the SEED model based on the Overview Design Concept and Details (ODD) protocol. It also lists its main features, assumptions and data sources.
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Meidani, Hadi, and Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-036.
Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncertainties such as random truck geometries, variable truck speed, random wind direction, and wind magnitude. Once trained, such a surrogate-based model can be readily employed for platoon-routing problems or the study of pavement performance.
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Witzig, Andreas, Camilo Tello, Franziska Schranz, Johannes Bruderer, and Matthias Haase. Quantifying energy-saving measures in office buildings by simulation in 2D cross sections. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541623658.
A methodology is presented to analyse the thermal behaviour of buildings with the goal to quantify energy saving measures. The solid structure of the building is modelled with finite elements to fully account for its ability to store energy and to accurately predict heat loss through thermal bridges. Air flow in the rooms is approximated by a lumped element model with three dynamical nodes per room. The dynamic model also contains the control algorithm for the HVAC system and predicts the net primary energy consumption for heating and cooling of the building for any time period. The new simulation scheme has the advantage to avoid U-values and thermal bridge coefficients and instead use well-known physical material parameters. It has the potential to use 2D and 3D geometries with appropriate automatic processing from BIM models. Simulations are validated by comparison to IDA ICE and temperature measurement. This work aims to discuss novel approaches to disseminating building simulation more widely.
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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0739.
The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.
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Aldubyan, Mohammad, and Moncef Krarti. Impact of Stay-home Orders on the Electricity Demand of Residential Buildings: Case Study of Saudi Arabia. King Abdullah Petroleum Studies and Research Center, April 2022. http://dx.doi.org/10.30573/ks--2022-dp02.
The analysis presented in this paper evaluates the impact of the COVID-19 stay-home order (or lockdown) on electricity consumption among Saudi residential building stock. Our analysis is based on an assessment of monitored data obtained for a sample of housing units as well as the results from a residential energy model (REEM). Specifically, we estimate the impact of the stay-home order imposed due to COVID-19 in most Saudi regions between March 15 and June 15, 2020, on residential electricity consumption.
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Balyk, Nadiia, Svitlana Leshchuk, and Dariia Yatsenyak. Developing a Mini Smart House model. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3741.
The work is devoted to designing a smart home educational model. The authors analyzed the literature in the field of the Internet of Things and identified the basic requirements for the training model. It contains the following levels: command, communication, management. The authors identify the main subsystems of the training model: communication, signaling, control of lighting, temperature, filling of the garbage container, monitoring of sensor data. The proposed smart home educational model takes into account the economic indicators of resource utilization, which gives the opportunity to save on payment for their consumption. The hardware components for the implementation of the Mini Smart House were selected in the article. It uses a variety of technologies to conveniently manage it and use renewable energy to power it. The model was produced independently by students involved in the STEM project. Research includes sketching, making construction parts, sensor assembly and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Research includes sketching, making some parts, assembly sensor and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Approbation Mini Smart House researches were conducted within activity the STEM-center of Physics and Mathematics Faculty of Ternopil Volodymyr Hnatiuk National Pedagogical University, in particular during the educational process and during numerous trainings and seminars for pupils and teachers of computer science.
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Mikayilov, Jeyhun, and Abdulelah Darandary. Modeling and Projecting Regional Electricity Demand for Saudi Arabia. King Abdullah Petroleum Studies and Research Center, July 2023. http://dx.doi.org/10.30573/ks--2023-mp01.
This paper utilizes a structural time series approach to model Saudi Arabia’s regional electricity demand, capturing undetected forces of variability in the data-generating process that include improvements in technology, energy-saving behavior, and other underlying trends that are excluded under conventional estimation methods. National models of aggregate electricity consumption might not be representative, as electricity prices are administered regionally and Saudi Arabia’s regions have unique social and economic characteristics. We find evidence that the regions have unique responses to prices and income levels with regard to electricity demand. Additionally, we use our estimated model to project the regional baseline demand for electricity for Saudi Arabia and create a scenario to demonstrate how a price increase would impact these regions differently. This information is valuable for policymakers in Saudi Arabia, as the fuel mix to generate electricity differs between regions. Our baseline electricity demand projections indicate that under the assumptions of moderate economic growth and no price changes, total electricity demand in Saudi Arabia will reach 366 TWh by 2030.
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Diakite-Kortlever, Aicha, Priji Balakrishnan, Stanislav Darula, David Geisler-Moroder, Alstan Jakubiec, Martine Knoop, Tao Luo, et al. Spectral sky models for advanced daylight simulations. Edited by Aicha Diakite-Kortlever. IEA SHC Task 61, June 2021. http://dx.doi.org/10.18777/ieashc-task61-2021-0005.
Lighting accounts for approximately 15 % of the global electric energy consumption and 5 % of greenhouse gas emissions. Growing economies, higher user demands for quality lighting and rebound effects as a result of low priced and more versatile electric lighting continuously still lead to an absolute increase of lighting energy consumption. More light is used, often less consciously.
