Relevant bibliographies by topics / Energy consumption – Data processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Energy consumption – Data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Energy consumption – Data processing"

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Dayarathna, Miyuru, Yuanlong Li, Yonggang Wen, and Rui Fan. "Energy consumption analysis of data stream processing: a benchmarking approach." Software: Practice and Experience 47, no. 10 (2016): 1443–62. http://dx.doi.org/10.1002/spe.2458.

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Li, Yiran, and Tong Zhang. "Reducing DRAM Image Data Access Energy Consumption in Video Processing." IEEE Transactions on Multimedia 14, no. 2 (2012): 303–13. http://dx.doi.org/10.1109/tmm.2011.2177079.

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Hussin, Masnida, Raja Azlina Raja Mahmood, and Mas Rina Mustaffa. "Sensor Communication Model Using Cyber-Physical System Approach for Green Data Center." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 10 (2019): 188. http://dx.doi.org/10.3991/ijim.v13i10.11310.

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Energy consumption in distributed computing system gains a lot of attention recently after its processing capacity becomes significant for better business and economic operations. Comprehensive analysis of energy efficiency in high-performance data center for distributed processing requires ability to monitor a proportion of resource utilization versus energy consumption. In order to gain green data center while sustaining computational performance, a model of energy efficient cyber-physical communication is proposed. A real-time sensor communication is used to monitor heat emitted by processo
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I.J Agabi and J.S Ibrahim. "Energy Evaluation and Processing Cost Reduction in Agudu Maize Processing Industry." International Journal of Engineering and Management Research 11, no. 1 (2021): 142–55. http://dx.doi.org/10.31033/ijemr.11.1.20.

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This study evaluated energy consumption by Agudu Farms Limited (AFL) that processes maize and cassava into flour for human consumption. The objectives of study included to determine energy contribution to processing cost, to minimize the processing cost and to propose a new selling price per unit of sale of the product. The study materials included; a multi-meter, stopwatch, electrical appliances’ nameplates and bills, fuel purchased receipts, and production records. Data was collected through detailed energy audits and measurements of present electricity consumption. This data was converted i
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Iazeolla, Giuseppe, and Alessandra Pieroni. "Energy Saving in Data Processing and Communication Systems." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/452863.

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The power management of ICT systems, that is, data processing (Dp) and telecommunication (Tlc) systems, is becoming a relevant problem in economical terms. Dp systems totalize millions of servers and associated subsystems (processors, monitors, storage devices, etc.) all over the world that need to be electrically powered. Dp systems are also used in the government of Tlc systems, which, besides requiring Dp electrical power, also requireTlc-specificpower, both formobilenetworks (with their cell-phone towers and associated subsystems: base stations, subscriber stations, switching nodes, etc.)
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Wang, Gaojian, Gerd Ascheid, Yanlu Wang, et al. "Optimization of Wireless Transceivers under Processing Energy Constraints." Frequenz 71, no. 9-10 (2017): 379–88. http://dx.doi.org/10.1515/freq-2017-0150.

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Abstract Focus of the article is on achieving maximum data rates under a processing energy constraint. For a given amount of processing energy per information bit, the overall power consumption increases with the data rate. When targeting data rates beyond 100 Gb/s, the system’s overall power consumption soon exceeds the power which can be dissipated without forced cooling. To achieve a maximum data rate under this power constraint, the processing energy per information bit must be minimized. Therefore, in this article, suitable processing efficient transmission schemes together with energy ef
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Song, Ning Xi, Di Ming Wan, Qian Sun, and Jian Feng Yue. "Data Mining-Based Smart Industrial Park Energy Efficiency Management System." Applied Mechanics and Materials 484-485 (January 2014): 585–88. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.585.

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Smart industrial park energy efficiency management system is used for solving the demanded side electric power management problem mainly in terms of scientific management. In this system, the data of electric power, electric power quality, and electric energy is acquired in real time by installing an electric power management monitor in the main load points, so as to analyze electric energy efficiency and energy consumption. A large amount of data can be obtained from the system, and if these huge amounts of data are of a value can be further analyzed, finding the unknown factors affecting ent
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Garyaev, Nikolay. "Simulation of energy consumption in urban areas." E3S Web of Conferences 152 (2020): 02006. http://dx.doi.org/10.1051/e3sconf/202015202006.

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One of the problems that may arise in the way of successful implementation of energy supply in urban areas is the difficulty of analyzing and interpreting a large amount of digital data received from various sensors. This problem may adversely affect the performance of energy organizations. The purpose of this study is to study modern tools to solve the problem of processing big data using technologies of simulation and artificial intelligence. This study is dedicated to the development of innovative digital models for the balanced distribution of energy consumption in urban areas.
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Sulaiman, S. M., P. Aruna Jeyanthy, and D. Devaraj. "Smart Meter Data Analysis Using Big Data Tools." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3629–36. http://dx.doi.org/10.1166/jctn.2019.8338.

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In recent years, the problem of electrical load forecasting gained attention due to the arrival of new measurement technologies that produce electrical energy consumption data at very short intervals of time. Such short term measurements become voluminous in very short time. The availability of big electrical consumption data allows machine learning techniques to be employed to analyze consumption behavior of every consumer on a greater detail. Predicting the consumption of a residential customer is crucial at this point of time because tailor-made consumer-specific tariffs will play a vital r
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Lv, Lishu, Zhaohui Deng, Can Yan, Tao Liu, Linlin Wan, and Qianwei Gu. "Modelling and analysis for processing energy consumption of mechanism and data integrated machine tool." International Journal of Production Research 58, no. 23 (2020): 7078–93. http://dx.doi.org/10.1080/00207543.2020.1756508.

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Dissertations / Theses on the topic "Energy consumption – Data processing"

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Reinhardt, Andreas [Verfasser]. "Advances to Energy Informatics : On the Collection, Processing, and Privacy Protection of Electricity Consumption Data / Andreas Reinhardt." Clausthal-Zellerfeld : Technische Universität Clausthal, 2019. http://d-nb.info/1230990429/34.

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Shelor, Charles F. "Dataflow Processing in Memory Achieves Significant Energy Efficiency." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1248478/.

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The large difference between processor CPU cycle time and memory access time, often referred to as the memory wall, severely limits the performance of streaming applications. Some data centers have shown servers being idle three out of four clocks. High performance instruction sequenced systems are not energy efficient. The execute stage of even simple pipeline processors only use 9% of the pipeline's total energy. A hybrid dataflow system within a memory module is shown to have 7.2 times the performance with 368 times better energy efficiency than an Intel Xeon server processor on the ana
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Skön, J. P. (Jukka-Pekka). "Intelligent information processing in building monitoring systems and applications." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526209913.

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Abstract Global warming has set in motion a trend for cutting energy costs to reduce the carbon footprint. Reducing energy consumption, cutting greenhouse gas emissions and eliminating energy wastage are among the main goals of the European Union (EU). The buildings sector is the largest user of energy and CO2 emitter in the EU, estimated at approximately 40% of the total consumption. According to the International Panel on Climate Change, 30% of the energy used in buildings could be reduced with net economic benefits by 2030. At the same time, indoor air quality is recognized more and more as
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Mehar, Sara. "The vehicle as a source and consumer of information : collection, dissemination and data processing for sustainable mobility." Thesis, Dijon, 2014. http://www.theses.fr/2014DIJOS069/document.

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Aujourd'hui, les véhicules sont devenus de plus en plus sophistiqués, intelligents et connectés. En effet, ils sont équipés de capteurs, radars, GPS, interfaces de communication et capacités de traitement et de stockage élevés. Ils peuvent collecter, traiter et communiquer les informations relatives à leurs conditions de travail et leur environnement formant un réseau véhiculaire. L'intégration des technologies de communication sur les véhicules fait l'objet d'une immense attention de l'industrie, des autorités gouvernementales et des organisations de standardisations; elle a ouvert la voie à
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Hrbek, Martin. "Optimalizace informačního systému pro sledování spotřeb energií." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400656.

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The goal of this thesis is to optimize the existing energy consumption monitoring system and expand the presentation options for the measured data in a way that would be suitable for large volume of data and a long period of time. Optimization concerns especially data processing and data presentation.
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Thomasson, Anton. "Measuring energy consumption characteristics in mobile data communication." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71954.

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This report looks at the modern cellular wireless network environment and the factors of energy consumption therein. The consumption of connectivity re- lated hardware is gradually becoming a larger part of the power consumption of virtually any mobile device. This report studies measurements of a mobile broadband module energy usage due to data transfer. It is found that switch- ing between technologies is still beneficial and savings are very feasible when using technologies with different traits if done correctly. Further the possibility of energy savings within a single high-bandwidth tech
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Wu, Minji. "Energy-efficient query processing in wireless sensor networks." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/724.

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Holm, Andreas, and Elling Oscar Johansson. "Disaggregating Household Energy Consumption Data Using an NIALM Algorithm." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214727.

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To enhance the control utilities of the energyprovider, smart meters are being installed nationwide to measurethe households energy consumption in real time. There is agroup of algorithms that is able to read these energy readingsand determines the states of the appliances. This opens up newpossibilities for someone who wants to exploit it and might lead toinfringement of privacy. It might lead to infringement of privacy.This paper will take a look on one approach of these algorithmsand counter measures to ensure privacy. The NIALM we chosewere working poorly in presence of more appliances hen
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Faisal, S. M. "Towards Energy Efficient Data Mining & Graph Processing." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440364739.

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García-Martín, Eva. "Extraction and Energy Efficient Processing of Streaming Data." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15532.

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The interest in machine learning algorithms is increasing, in parallel with the advancements in hardware and software required to mine large-scale datasets. Machine learning algorithms account for a significant amount of energy consumed in data centers, which impacts the global energy consumption. However, machine learning algorithms are optimized towards predictive performance and scalability. Algorithms with low energy consumption are necessary for embedded systems and other resource constrained devices; and desirable for platforms that require many computations, such as data centers. Data s
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Books on the topic "Energy consumption – Data processing"

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American Society of Heating, Refrigerating and Air-Conditioning Engineers. Real-time energy consumption measurements in data centers. American Society of Heating, Refrigerating, and Air-Conditioning Engineers, 2009.

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Beck, Fredric A. Energy smart data centers: Applying energy efficient design and technology to the digital information sector. Renewable Energy Policy Project, 2001.

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Held, Gilbert. Making your data center energy efficient. Taylor & Francis, 2012.

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Energy and analytics: BIG DATA and building technology integration. Fairmont Press, Inc., 2015.

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Pinhorn, Paul. Vessel analysis computing system (ENER SEA). Dept. of Fisheries and Oceans, Fisheries Development Branch, 1986.

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Pinhorn, Paul. Ener Sea - 1987/88: (Vessel Analysis Computing System). Dept. of Fisheries and Oceans, 1988.

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1936-, Hamel Bernard B., and Hedman Bruce A. 1950-, eds. Energy analysis of 108 industrial processes. Fairmont Press, 1985.

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American Society of Heating, Refrigerating and Air-Conditioning Engineers. PUE: A comprehensive examination of the metric. ASHRAE, 2013.

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Sragovits, Daṿid, та Elyo Suzi. Tokhnah DOE le-nituaḥ energeti shel mivnim. Miśrad ha-energiyah ṿeha-tashtit, ha-Agaf le-shimur energiyah, 1993.

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Alanen, Raili. Analysis of electrical energy consumption and neural network estimation and forecasting of loads in a paper mill. Technical Research Centre of Finland, 2000.

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Book chapters on the topic "Energy consumption – Data processing"

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Kadioglu, Yasin Murat. "Energy Consumption Model for Data Processing and Transmission in Energy Harvesting Wireless Sensors." In Communications in Computer and Information Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47217-1_13.

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Peng, Ying, Nao Wang, and Gaocai Wang. "An Optimization Strategy of Energy Consumption for Data Transmission Based on Optimal Stopping Theory in Mobile Networks." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27140-8_20.

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Fang, Xiang, Yi Wang, Zhiqing Sun, Lin Xia, and Jian Jiang. "Characteristics of Energy Consumption and Energy Saving Potentiality of Hotel Power System Based on Big Data of Electrical Power." In 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1726-3_24.

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Martinez, Jabier, Alejandra Ruiz, Javier Puelles, Ibon Arechalde, and Yuliya Miadzvetskaya. "Smart Grid Challenges Through the Lens of the European General Data Protection Regulation." In Lecture Notes in Information Systems and Organisation. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49644-9_7.

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Abstract The General Data Protection Regulation (GDPR) was conceived to remove the obstacles to the free movement of personal data while ensuring the protection of natural persons with regard to the processing of such data. The Smart Grid has similar features as any privacy-critical system but, in comparison to the engineering of other architectures, has the peculiarity of being the source of energy consumption data. Electricity consumption constitutes an indirect means to infer personal information. This work looks at the Smart Grid from the perspective of the GDPR, which is especially relevant now given the current growth and diversification of the Smart Grid ecosystem. We provide a review of existing works highlighting the importance of energy consumption as valuable personal data as well as an analysis of the established Smart Grid Architecture Model and its main challenges from a legal viewpoint, in particular the challenge of sharing data with third parties.
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Yue, Xingzuo, and Lei Wu. "A Comprehensive Energy Consumption Measurement Model for Building Envelope Components Based on Thermal Imaging Detection." In 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1726-3_104.

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Sato, Aki-Hiro. "Energy Consumption." In Applied Data-Centric Social Sciences. Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54974-1_9.

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Gregorio, Fernando, Gustavo González, Christian Schmidt, and Juan Cousseau. "Energy Consumption." In Signal Processing Techniques for Power Efficient Wireless Communication Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32437-7_3.

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Marowka, Ami. "Energy Consumption Modeling for Hybrid Computing." In Euro-Par 2012 Parallel Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32820-6_8.

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Wang, Lijun. "Energy Consumption and Reduction Strategies in Food Processing." In Sustainable Food Processing. John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118634301.ch16.

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Takaguchi, H. "The Use of Energy Consumption Data." In Sustainable Houses and Living in the Hot-Humid Climates of Asia. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8465-2_29.

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Conference papers on the topic "Energy consumption – Data processing"

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Brayner, Angelo, and Ronaldo Menezes. "Balancing energy consumption and memory usage in sensor data processing." In the 2007 ACM symposium. ACM Press, 2007. http://dx.doi.org/10.1145/1244002.1244207.

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Schirmer, Pascal A., Christian Geiger, and Iosif Mporas. "Residential Energy Consumption Prediction Using Inter-Household Energy Data and Socioeconomic Information." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287395.

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Krishnamurthy, Sharath, and S. B. RASHMI. "Data Encoding Techniques for Reducing Energy Consumption in Network-on-Chip." In Second International Conference on Signal Processing, Image Processing and VLSI. Research Publishing Services, 2015. http://dx.doi.org/10.3850/978-981-09-6200-5_d-62.

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Szwedko, Jesse, Panayiotis Neophytou, Panos K. Chrysanthis, Alexandros Labrinidis, and Mohamed A. Sharaf. "Visualization of Energy Consumption of Continuous Query Processing with Mobile Clients." In 2011 12th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2011. http://dx.doi.org/10.1109/mdm.2011.70.

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Neophytou, Panayiotis, Jesse Szwedko, Mohamed A. Sharaf, Panos K. Chrysanthis, and Alexandros Labrinidis. "Optimizing the Energy Consumption of Continuous Query Processing with Mobile Clients." In 2011 12th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2011. http://dx.doi.org/10.1109/mdm.2011.71.

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Fernandes, Luis Felipe de O., Flavia Bernardini, Edwin Mitacc Meza, Leandro Miranda, and Jose Viterbo. "Energy Consumption Prediction using Data Stream Learning for Commercial Buildings." In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2020. http://dx.doi.org/10.1109/iwssip48289.2020.9145123.

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Chen, Ming, Zengrui Huang, Qiang Wu, Wei Xu, and Boyue Xiong. "Pre-processing and audit of power consumption data based on composite mathematical statistics model." In 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2018. http://dx.doi.org/10.1109/ei2.2018.8582623.

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Pei, Maofeng, Min Dong, Sheng Bi, et al. "Data processing method for smart streetlamp energy consumption analysis systems based on data mining." In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2017. http://dx.doi.org/10.1109/cyber.2017.8446456.

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Clemente, Jose, Fangyu Li, and WenZhan Song. "OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS." In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018. http://dx.doi.org/10.1109/globalsip.2018.8646641.

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Zhang, Yingmei, Genghuang Yang, Xiayi Hao, and Minglin Li. "Research on Identification and Processing Method for Abnormal Data of Residential Electric Power Consumption." In 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC). IEEE, 2019. http://dx.doi.org/10.1109/cieec47146.2019.cieec-2019345.

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Reports on the topic "Energy consumption – Data processing"

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Author, Not Given. State energy data report 1995 - consumption estimates. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/563836.

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Author, Not Given. State energy data report: Consumption estimates, 1960--1987. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6121275.

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Tawil, J. J. Statistical evaluation of Pacific Northwest Residential Energy Consumption Survey weather data. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/6216833.

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Kotchen, Matthew. Do Building Energy Codes Have a Lasting Effect on Energy Consumption? New Evidence From Residential Billing Data in Florida. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21398.

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Harter, Rachel M., Pinliang (Patrick) Chen, Joseph P. McMichael, Edgardo S. Cureg, Samson A. Adeshiyan, and Katherine B. Morton. Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey. RTI Press, 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0041.1705.

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The 2015 Residential Energy Consumption Survey design called for stratification of primary sampling units to improve estimation. Two methods of defining strata from multiple stratification variables were proposed, leading to this investigation. All stratification methods use stratification variables available for the entire frame. We reviewed textbook guidance on the general principles and desirable properties of stratification variables and the assumptions on which the two methods were based. Using principal components combined with cluster analysis on the stratification variables to define s
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Moon, Ron. RECOVERY ACT: DYNAMIC ENERGY CONSUMPTION MANAGEMENT OF ROUTING TELECOM AND DATA CENTERS THROUGH REAL-TIME OPTIMAL CONTROL (RTOC): Final Scientific/Technical Report. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1018478.

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Truett, L., and T. Yow. A study of auditing methodologies for the Energy Information Administration data collection and processing system quality assessment program. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/5318803.

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Taasevigen, Danny J. User's Guide to Pre-Processing Data in Universal Translator 2 for the Energy Charting and Metrics Tool (ECAM). Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1032417.

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Kurnik, Charles W., Ken Agnew, and Mimi Goldberg. Chapter 8: Whole-Building Retrofit with Consumption Data Analysis Evaluation Protocol. The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1407847.

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Mazari, Mehran, Siavash F. Aval, Siddharth M. Satani, David Corona, and Joshua Garrido. Developing Guidelines for Assessing the Effectiveness of Intelligent Compaction Technology. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1923.

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Many factors affect pavement compaction quality, which can vary. Such variability may result in an additional number of passes required, extended working hours, higher energy consumption, and negative environmental impacts. The use of Intelligent Compaction (IC) technology during construction can improve the quality and longevity of pavement structures while reducing risk for contractors and project owners alike. This study develops guidelines for the implementation of IC in the compaction of pavement layers as well as performing a preliminary life-cycle cost analysis (LCCA) of IC technology c
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