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Rozprawy doktorskie na temat „Demand prediction”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

McElroy, Wade Allen. "Demand prediction modeling for utility vegetation management." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117973.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 63-64).<br>This thesis proposes a demand prediction model for utility vegetation management (VM) organizations. The primary uses of the model is to aid in the technology adoption proce

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Zhou, Yang. "Multi-Source Large Scale Bike Demand Prediction." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703413/.

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Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we p

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Sun, Rui S. M. Massachusetts Institute of Technology. "Analytics for hotels : demand prediction and decision optimization." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111438.

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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 69-71).<br>The thesis presents the work with a hotel company, as an example of how machine learning techniques can be applied to improve demand predictions and help a hotel property to make better decisions on its pricing and capacity allocation strategies. To sol

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Svensk, Gustav. "TDNet : A Generative Model for Taxi Demand Prediction." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158514.

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Supplying the right amount of taxis in the right place at the right time is very important for taxi companies. In this paper, the machine learning model Taxi Demand Net (TDNet) is presented which predicts short-term taxi demand in different zones of a city. It is based on WaveNet which is a causal dilated convolutional neural net for time-series generation. TDNet uses historical demand from the last years and transforms features such as time of day, day of week and day of month into 26-hour taxi demand forecasts for all zones in a city. It has been applied to one city in northern Europe and on

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Lu, Hongwei Marketing Australian School of Business UNSW. "Small area market demand prediction in the automobile industry." Publisher:University of New South Wales. Marketing, 2008. http://handle.unsw.edu.au/1959.4/43027.

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The general aim of this research is to investigate approaches to: improve small area market demand (i.e. SAMD) prediction accuracy for the purchase of automobiles at the level of each Census Collection District (i.e. CCD); and enhance understanding of meso-level marketing phenomena (i.e. geographically aggregated phenomena) relating to SAMD. Given the importance of SAMD prediction, and the limitations posed by current methods, four research questions are addressed: What are the key challenges in meso-level SAMD prediction? What variables affect SAMD prediction? What techniques can be use

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6

Eressa, Muluken Regas. "Probabilistic Models for Demand Supply Prediction in The Eenergy Sector." Electronic Thesis or Diss., Université Gustave Eiffel, 2024. http://www.theses.fr/2024UEFL2005.

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Cette thèse étudie des modèles prédictifs probabilistes basés sur les processus gaussiens et l'apprentissage en profondeur pour la prévision de la demande d'électricité. Étant donné que les processus gaussiens sont des modèles prédictifs basés sur des noyaux, leur performance est limitée par le type, le nombre et la dimension du noyau sélectionné. Pour répondre à ces limitations, premièrement, elle propose une nouvelle technique d'approximation gaussienne qui aborde le goulot d'étranglement computationnel bayésien. Deuxièmement, elle propose un algorithme d'estimation de noyau compositionnel s

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7

Lönnbark, Carl. "On Risk Prediction." Doctoral thesis, Umeå universitet, Nationalekonomi, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-22200.

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This thesis comprises four papers concerning risk prediction. Paper [I] suggests a nonlinear and multivariate time series model framework that enables the study of simultaneity in returns and in volatilities, as well as asymmetric effects arising from shocks. Using daily data 2000-2006 for the Baltic state stock exchanges and that of Moscow we find recursive structures with Riga directly depending in returns on Tallinn and Vilnius, and Tallinn on Vilnius. For volatilities both Riga and Vilnius depend on Tallinn. In addition, we find evidence of asymmetric effects of shocks arising in Moscow an

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8

Wong, Wai Ho. "Predicting Demand in Cloud Computing Environments." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9497.

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Cloud computing is a new computing paradigm that enables elastic on-demand pay-per-use access to shared computational resources. However, there are current limitations on the elasticity of cloud resources specifically deployment delays and mismatches in pricing granularity. Demand prediction - the estimation of future demand - serves to mitigate these limitations of elasticity. The focus of this thesis is the demand predictor that operates within the context of cloud resource management. It fulfils three tasks: 1) learn demand patterns from past demand behaviour; 2) predict upcoming demand

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Bernhardsson, Viktor, and Rasmus Ringdahl. "Real time highway traffic prediction based on dynamic demand modeling." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112094.

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Traffic problems caused by congestion are increasing in cities all over the world. As a traffic management tool traffic predictions can be used in order to make prevention actions against traffic congestion. There is one software for traffic state estimations called Mobile Millennium Stockholm (MMS) that are a part of a project for estimate real-time traffic information.In this thesis a framework for running traffic predictions in the MMS software have been implemented and tested on a stretch north of Stockholm. The thesis is focusing on the implementation and evaluation of traffic prediction

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Jones, Simon Andrew. "Prediction of demand for emergency care in an acute hospital." Thesis, Kingston University, 2005. http://eprints.kingston.ac.uk/20739/.

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This thesis describes some models that attempt to forecast the number of occupied beds due to emergency admissions each day in an acute general hospital. Hospital bed managers have two conflicting demands: they must not only ensure that at all times they have sufficient empty beds to cope with possible emergency admissions but they must fill as many empty beds as possible with people on the waiting list. This model is important as it could help balance these two conflicting demands. The research is based on data from a district general and a postgraduate teaching hospital in South East London.

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Shen, Ni. "Prediction of International Flight Operations at U.S. Airports." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/35687.

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This report presents a top-down methodology to forecast annual international flight operations at sixty-six U.S. airports, whose combined operations accounted for 99.8% of the total international passenger flight operations in National Airspace System (NAS) in 2004. The forecast of international flight operations at each airport is derived from the combination of passenger flight operations at the airport to ten World Regions. The regions include: Europe, Asia, Africa, South America, Mexico, Canada, Caribbean and Central America, Middle East, Oceania and U.S. International. <p> In the forecast

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Paul, Udita. "Efficient access network selection and data demand prediction for 5G systems." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29729.

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The massive proliferation of sophisticated mobile terminals with advanced capabilities have led to an enormous surge in the demand for mobile broadband data. Also, the recent popularity of bandwidth intensive applications such as Netflix and YouTube has contributed to this demand for the wireless resources. In order to cope with this massive demand, fifth generation (5G) of wireless network is on the verge of deployment. This new generation of the wireless networks would pose different challenges for both subscribers and service providers, and the challenges need to be carefully addressed. Due

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13

Li, Yapeng. "Dynamic energy demand prediction and related control system for UK households." Thesis, University of Newcastle upon Tyne, 2015. http://hdl.handle.net/10443/2824.

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Domestic energy consumption is not only based on the type of appliances, weather conditions, and house type; it is also highly depended on related occupancy profiles. In order to manage and optimise energy generation and the effective use of energy storage, it is important to be able to accurately predict energy demand in advance. However, high-resolution (like below 1-min) occupancy profiles for domestic UK households are not ideally possible to be recorded or measured in nature. Therefore, an alternative approach to transfer particular electricity load to the number of active occupancy durin

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Lindsey, Matthew Douglas. "Reliable Prediction Intervals and Bayesian Estimation for Demand Rates of Slow-Moving Inventory." Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc3946/.

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Application of multisource feedback (MSF) increased dramatically and became widespread globally in the past two decades, but there was little conceptual work regarding self-other agreement and few empirical studies investigated self-other agreement in other cultural settings. This study developed a new conceptual framework of self-other agreement and used three samples to illustrate how national culture affected self-other agreement. These three samples included 428 participants from China, 818 participants from the US, and 871 participants from globally dispersed teams (GDTs). An EQS procedur

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15

Pan, Song. "On demand DBS for Parkinson's Disease : tremor prediction using artificial neural networks." Thesis, University of Reading, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.567590.

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In this thesis results are presented which relate to using artificial neural networks to predict the onset of Parkinson's disease tremors in human subjects. Data for the networks was obtained from implanted deep brain electrodes in human subjects. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings. Parkinson's disease (PO) is one disease in a group of conditions called movement disorders. One of the primary symptoms of Parkinson's disease is tremor, and in the extreme case, the patient can suffer loss of physical m

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Lindsey, Matthew Douglas Pavur Robert J. "Reliable prediction intervals and Bayesian estimation for demand rates of slow-moving inventory." [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-3946.

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Stojanovski, Filip. "Churn Prediction using Sequential Activity Patterns in an On-Demand Music Streaming Service." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228226.

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In data-driven companies, churn analysis aims to make use of novel machine learning and data mining techniques for the purpose of better understanding of the customers. The most common approach is to engineer a vast number of features describing users, products, services, and actions, which are then used to infer knowledge by means of machine learning and data mining. However, one aspect is typically neglected since it appears more difficult to model and utilize, and that is the time. This work presents the modeling of user activity on a music streaming service in the form of sequential tempor

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18

Zhang, Huimin. "User Behavior Analysis and Prediction Methods for Large-scale Video-on- demand System." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-263261.

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Video-on-demand (VOD) systems are some of the best-known examples of 'next-generation' Internet applications. With their growing popularity, huge amount of video content imposes a heavy burden on Internet traffic which, in turns, influences the user experience of the systems. Predicting and pre- fetching relevant content before user requests is one of the popular methods used to reduce the start-up delay. In this paper, a typical VOD system is characterized and user's watching behavior is analyzed. Based on the characterization, two pre- fetching approaches based on user behavior are investiga

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19

Sun, Wenzhe. "Bus Bunching Prediction and Transit Route Demand Estimation Using Automatic Vehicle Location Data." Kyoto University, 2020. http://hdl.handle.net/2433/253498.

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Hast, Matteus. "Evaluation of machine learning algorithms for customer demand prediction of in-flight meals." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255020.

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This study aims to evaluate multiple Machine Learning Algorithms (MLAs) for estimating the customer demand of in-flight meals. As a result of the review of related works, four MLAs were selected, namely Linear Regression (LR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost) and a Multilayer Perceptron Neural Network (MLP). The study investigates which MLA is best suited for the problem at hand and which features are most influential for customer demand prediction of in-flight meals. Focus is put on finding applicable MLAs and on evaluating, comparing and tweaking the param

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Farzana, Fatema Hoque. "Estimation and Prediction of Mobility and Reliability Measures Using Different Modeling Techniques." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3880.

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The goal of this study is to investigate the predictive ability of less data intensive but widely accepted methods to estimate mobility and reliability measures. Mobility is a relatively mature concept in the traffic engineering field. Therefore, many mobility measure estimation methods are already available and widely accepted among practitioners and researchers. However, each method has their inherent weakness, particularly when they are applied and compared with real-world data. For instances, Bureau of Public Roads (BPR) Curves are very popular in static route choice assignment, as part of

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22

Eriksson, Niclas. "Predicting demand in districtheating systems : A neural network approach." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175082.

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To run a district heating system as efficiently as possible correct unit-commitmentdecisions has to be made and in order to make those decisions a good forecast ofheat demand for the coming planning period is necessary. With a high quality forecastthe need for backup power and the risk for a too high production are lowered. Thisthesis takes a neural network approach to load forecasting and aims to provide asimple, yet powerful, tool that can provide accurate load forecasts from existingproduction data without the need for extensive model building.The developed software is tested using real lif

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Ghareeb, Ahmed. "Data mining for University of Dayton campus buildings to predict future demand." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1490472227466522.

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Kerakos, Emil, Oscar Lindgren, and Vladislav Tolstoy. "Machine Learning for Ambulance Demand Prediction in Stockholm County : Towards efficient and equitableDynamic Deployment Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282420.

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Pre-hospital care is a widely discussed subject with many actors working on figuring out what factors determine the outcome for the patient and how those factors can be affected. One factor believed to have a major impact on patient outcome is ambulance response time. A proposed way to improve response time is dynamic deployment systems. These systems require detailed predictions of spatio-temporal ambulance demand in order to function effectively. The purpose of the study is to explore the possibility of using machine learning to build a high-resolution predictor that dynamic deployment syste

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Xu, Yizheng. "Probabilistic estimation and prediction of the dynamic response of the demand at bulk supply points." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/probabilistic-estimation-and-prediction-of-the-dynamic-response-of-the-demand-at-bulk-supply-points(b9e427ec-7e5e-49a5-aec4-0f34032d71a9).html.

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The dynamic response of the demand is defined as the time-domain real and reactive power response to a voltage disturbance, and it represents the dynamic load characteristics. This thesis develops a methodology for probabilistic estimation and prediction of dynamic responses of the demand at bulk supply points. The main outcome of the research is being able to predict the contribution of different categories of loads to the total demand mix and their controllability without conducting detailed customer surveys or collecting smart meter data, and to predict the dynamic response of the demand wi

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Karlsson, Niclas, and Zandra Karlsson. "Detecting Disruption: : an Ex-ante Study in the Automotive Industry." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12834.

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In history there are numerous examples of strong market-leaders who have lost everything through the emergence of a new breakthrough technology which has replaced the existing one. That could be the reason why Christensen received such high attention when he presented his famous work about disruptive technologies in 1997. In his work, and in many following studies, several aspects of this phenomenon have been investigated. However, the key point for the market leaders, the ability of identifying a market disruption before it happens, ex ante, is still a field that has not reached a definedstat

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Ahmed, Kishwar. "Energy Demand Response for High-Performance Computing Systems." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3569.

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The growing computational demand of scientific applications has greatly motivated the development of large-scale high-performance computing (HPC) systems in the past decade. To accommodate the increasing demand of applications, HPC systems have been going through dramatic architectural changes (e.g., introduction of many-core and multi-core systems, rapid growth of complex interconnection network for efficient communication between thousands of nodes), as well as significant increase in size (e.g., modern supercomputers consist of hundreds of thousands of nodes). With such changes in architect

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Школа, Вікторія Юріївна, Виктория Юрьевна Школа та Viktoriia Yuriivna Shkola. "Прогнозування попиту на інновації для формування стратегії розвитку держави". Thesis, Видавництво СумДУ, 2006. http://essuir.sumdu.edu.ua/handle/123456789/3811.

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Zhou, Xuesong. "Dynamic origin-destination demand estimation and prediction for off-line and on-line dynamic traffic assignment operation." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1819.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Civil Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Candela, Garza Eduardo. "Revenue optimization for a hotel property with different market segments : demand prediction, price selection and capacity allocation." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113433.

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Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 53-55).<br>We present our work with a hotel company as an example of how machine learning techniques can be used to improve the demand predictions of a hotel property, as well as its pricing and capacity allocation decisions. Firs

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Ghias, Nezhad Omran Nima. "Power grid planning for vehicular demand: forecasting and decentralized control." IEEE Transactions on Smart Grid, 2014. http://hdl.handle.net/1993/23891.

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Temporal and spatial distribution of incoming vehicular charging demand is a significant challenge for the future planning of power systems. In this thesis the vehicular loading is-sue is categorized into two classes of stationary and mobile; they are then addressed in two phases. The mobile vehicular load is investigated first; a location-based forecasting algorithm for the charging demand of plug-in electric vehicles at potential off-home charging stations is proposed and implemented for real-world case-studies. The result of this part of the re-search is essential to realize the scale of f

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Boulin, Juan Manuel. "Call center demand forecasting : improving sales calls prediction accuracy through the combination of statistical methods and judgmental forecast." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59159.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 79-81).<br>Call centers are important for developing and maintaining healthy relationships with customers. At Dell, call centers are also at the core of the company's renowned direct model. For sales call centers in particular, the impact of proper operations is reflected n

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Martyr, Randall. "Optimal prediction games in local electricity markets." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/optimal-prediction-games-in-local-electricity-markets(976e566d-e942-444a-9ee0-df17f46188d4).html.

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Local electricity markets can be defined broadly as 'future electricity market designs involving domestic customers, demand-side response and energy storage'. Like current deregulated electricity markets, these localised derivations present specific stochastic optimisation problems in which the dynamic and random nature of the market is intertwined with the physical needs of its participants. Moreover, the types of contracts and constraints in this setting are such that 'games' naturally emerge between the agents. Advanced modelling techniques beyond classical mathematical finance are therefor

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Sapp, James Christopher. "Electricity Demand Forecasting in a Changing Regional Context: The Application of the Multiple Perspective Concept to the Prediction Process." PDXScholar, 1987. https://pdxscholar.library.pdx.edu/open_access_etds/574.

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In 1982, the Bonneville Power Administration (BPA), a marketer of hydroelectric power in the Pacific Northwest, found itself in a new role which required it to acquire power resources needed to meet the demands of the region's utilities. In particular, it had to deal with the Washington Public Power Supply System's nuclear plant cost escalations. In response, BPA prepared its first independent regional power forecast. The forecast development process was intricate and multidimensional and involved a variety of interested parties. Application of the Multiple Perspective Concept uncovers strengt

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Borges, Viviana Marli Nogueira de Aquino. "Acoplamento de um modelo de previsão de demanda de água a um modelo simulador em tempo real - estudo de caso: sistema adutor metropolitano de São Paulo." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/3/3147/tde-17092004-101640/.

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O presente trabalho propõe uma evolução metodológica na operação do Sistema Adutor Metropolitano de São Paulo, em tempo real. Foi implantado um modelo matemático, em tempo real, de previsão de consumo de água horário para uma melhoria na performance operacional. Descrevem-se vários procedimentos de sistema de controle operacional, desde manual até totalmente automático, em sistemas de abastecimento. O sistema de abastecimento de São Paulo é classificado neste contexto. Foi analisada a possibilidade de desenvolvimento da situação atual rumo a um controle mais eficiente, através do uso de um mod

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Delin, Sofia. "Site-specific nitrogen fertilization demand in relation to plant available soil nitrogen and water : potential for prediction based on soil characteristics /." Skara : Department of Soil Sciences, Swedish University of Agricultural Sciences, 2005. http://epsilon.slu.se/200506.pdf.

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Kang, Ying. "Estimation and prediction of dynamic origin-destination (O-D) demand and system consistency control for real-time dynamic traffic assignment operation /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Goutham, Mithun. "Machine learning based user activity prediction for smart homes." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595493258565743.

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Vicente, Rosmeiry Vanzella. "Modelo de operação para centros de controle de sistemas de abastecimento de água: estudo de caso - Sistema Adutor Metropolitano de São Paulo." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/3/3147/tde-10042006-141721/.

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O presente trabalho propõe um modelo de operação sustentado por um sistema de suporte à decisão para operar a distribuição de água em tempo real atendendo a condições / restrições hidráulicas com o mínimo custo de energia elétrica. O atendimento às condições / restrições hidráulicas são avaliadas por um modelo simulador hidráulico previamente montado e calibrado. O conjunto de resultados avaliados pelo modelo de simulação hidráulica é analisado por um modelo de otimização proposto com solução de programação linear. As condições de operação em tempo real geram a necessidade de alimentação de in

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Faber, Andreas D. [Verfasser], Stefan [Gutachter] Spinler, and Arnd [Gutachter] Huchzermeier. "Data analytics in supply chain planning : applications in intermittent demand forecasting, partial defection prediction and price discrimination / Andreas D. Faber ; Gutachter: Stefan Spinler, Arnd Huchzermeier." Vallendar : WHU - Otto Beisheim School of Management, 2021. http://d-nb.info/1240764359/34.

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Jonsson, Estrid, and Sara Fredrikson. "An Investigation of How Well Random Forest Regression Can Predict Demand : Is Random Forest Regression better at predicting the sell-through of close to date products at different discount levels than a basic linear model?" Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302025.

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Allt eftersom klimatkrisen fortskrider ökar engagemanget kring hållbarhet inom företag. Växthusgaser är ett av de största problemen och matsvinn har därför fått mycket uppmärksamhet sedan det utnämndes till den tredje största bidragaren till de globala utsläppen. För att minska sitt bidrag rabatterar många matbutiker produkter med kort bästföredatum, vilket kommit att kräva en förståelse för hur priskänslig efterfrågan på denna typ av produkt är. Prisoptimering görs vanligtvis med så kallade Generalized Linear Models men då efterfrågan är ett komplext koncept har maskininl ärningsmetoder börja

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Souza, Daniel Morais de. "Comparação de abordagens econométricas alternativas para modelagem da demanda anual de eletricidade no Brasil nos segmentos residencial, industrial e comercial." Universidade Federal de Juiz de Fora (UFJF), 2018. https://repositorio.ufjf.br/jspui/handle/ufjf/6891.

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Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-06-14T13:10:24Z No. of bitstreams: 1 danielmoraisdesouza.pdf: 1277495 bytes, checksum: 7c2517a5b98a6f70aa787d954cd0c84a (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-06-27T15:04:32Z (GMT) No. of bitstreams: 1 danielmoraisdesouza.pdf: 1277495 bytes, checksum: 7c2517a5b98a6f70aa787d954cd0c84a (MD5)<br>Made available in DSpace on 2018-06-27T15:04:32Z (GMT). No. of bitstreams: 1 danielmoraisdesouza.pdf: 1277495 bytes, checksum: 7c2517a5b98a6f70aa787d954cd0c84a (MD5) Previous issue da

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Drélichová, Stanislava. "Studie řízení průběhu zakázky firmou." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2007. http://www.nusl.cz/ntk/nusl-221414.

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This masterś thesis, titled as The Study of Order Process Control in Company Invensys Appliance Controls s.r.o., deals with optimization of order process from the first entering of new order till the delivery of finished goods to final customer. Further I provide the basic characteristics and analysis of Invensys Company. The project part is divided into two parts. The first one is focused on explaining of operation of Kanban system and itś possibility to improve the material flow in company. The second one is focused on long-term forecasts (demand predictions) provided by customers, which can

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Steen, Englund Jessika. "Prediction of Energy Use of a Swedish Secondary School Building : Building Energy Simulation, Validation, Occupancy Behaviour and Potential Energy-Efficiency Measures." Licentiate thesis, Högskolan i Gävle, Energisystem och byggnadsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-33313.

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Residential and public buildings account for about 40% of the annual energy use in Europe. Many buildings are in urgent need of renovation, and reductions in energy demand in the built environment are of high importance in both Europe and Sweden. Building energy simulation (BES) tools are often used to predict building performance. However, it can be a challenge to create a reliable BES model that predicts the real building performance accurately. BES modelling is always associated with uncertainties, and modelling occupancy behaviour is a challenging task. This research presents a case study

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Sassi, Kamal M. "Optimal scheduling, design, operation and control of reverse osmosis desalination : prediction of RO membrane performance under different design and operating conditions, synthesis of RO networks using MINLP optimization framework involving fouling, boron removal, variable seawater temperature and variable fresh water demand." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5671.

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An accurate model for RO process has significant importance in the simulation and optimization proposes. A steady state model of RO process is developed based on solution diffusion theory to describe the permeation through membrane and thin film approach is used to describe the concentration polarization. The model is validated against the operation data reported in the literature. For the sake of clear understanding of the interaction of feed temperature and salinity on the design and operation of RO based desalination systems, simultaneous optimization of design and operation of RO network i

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Shah, Ismail. "Modeling and Forecasting Electricity Market Variables." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3427110.

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In deregulated electricity markets, accurate modeling and forecasting of different variables, e.g. demand, prices, production etc. have obtained increasing importance in recent years. As in most electricity markets, the daily demand and prices are determined the day before the physical delivery by means of (semi-) hourly concurrent auctions, accurate forecasts are necessary for the efficient management of power systems. However, it is well known that electricity (demand/price) data exhibit some specific features, among which, daily, weekly and annual periodic patterns as well as non-constant m

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Lin, Chuan-Heng, and 林泉亨. "MRT Demand Prediction through Social Media." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/08325576544480906430.

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碩士<br>國立臺灣大學<br>土木工程學研究所<br>103<br>With the technological improvements of mobile devices and the increasing number of social media posts, there are more and more data on human mobility based on which information could potentially be extracted. Current research related to social media are mostly focused on inter-person behaviors. Conversely, related topics on system level performances are rarely discussed. This thesis applies feature extraction methods on quantitative, textual, and image data to retrieve useful features from social media. In addition, a machine learning pipeline based on suppor

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LIANG, WAN YU, and 萬友良. "Prediction of Potential Demand on Mass Transportation." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/44g673.

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碩士<br>大葉大學<br>工業工程與科技管理學系<br>101<br>The object of this study is to set up logistics regression models to predict the probability that people shift to use mass-transportation by making use of major energy issues and the characteristics of mass-transportation as independent variables under the rise of gas price. First, the principal component analysis is employed to extract the major issues in energy. Secondly, canonical correlation analysis is applied to validate the relation between the major energy issues and the characteristics of mass-transportation. Finally, we surveyed the people around T

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Cheng, Chih-Hsien, and 鄭志賢. "A Study on Domestic Water Demand Prediction." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/73008774254015920073.

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碩士<br>義守大學<br>資訊管理學系碩士班<br>96<br>Today, enterprises are forced to confront the challenges of global competition. They have had to grasp, and promptly make use of information ever since the Internet triggered more intensive competition in terms of time and space. Correctly anticipating client needs and quickly adapting to supply-demand trends are the foundation for enterprise''s sustainable development in the age of information explosion. In view of the nation’s self-awareness concerning raising economic, competitive advantages and the necessity of satisfying water resources demand, this resear

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"A prediction model for short term electricity demand." Chinese University of Hong Kong, 1990. http://library.cuhk.edu.hk/record=b5886391.

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by Yung Kai-man.<br>Thesis (M.B.A.)--Chinese University of Hong Kong, 1990.<br>Bibliography: leaves 90-92.<br>ABSTRACT --- p.ii<br>ACKNOWLEDGMENTS --- p.iii<br>TABLE OF CONTENTS --- p.iv<br>LIST OF ILLUSTRATIONS --- p.vi<br>LIST OF TABLES --- p.vii<br>Chapter<br>Chapter I. --- INTRODUCTION --- p.1<br>Background --- p.1<br>Methodology Review --- p.6<br>Chapter II. --- DATA BASE AND VARIABLES --- p.8<br>The Data Base --- p.8<br>The Dependent Variables --- p.9<br>The Independent Variables --- p.14<br>Chapter III. --- METHODOLOGY --- p.24<br>Regression Analysis --- p.24<br>Selection

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