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Literatura académica sobre el tema "Wine industry Forecasting"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 19 de febrero de 2022

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Artículos de revistas sobre el tema "Wine industry Forecasting"

Steinhagen, Sigrun, Jenny Darroch, and Bill Bailey. "Forecasting in the Wine Industry: An Exploratory Study." International Journal of Wine Marketing 10, no. 1 (January 1998): 13–24. http://dx.doi.org/10.1108/eb008674.

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Sturman, Andrew, Peyman Zawar-Reza, Iman Soltanzadeh, Marwan Katurji, Valérie Bonnardot, Amber Kaye Parker, Michael C. T. Trought, et al. "The application of high-resolution atmospheric modelling to weather and climate variability in vineyard regions." OENO One 51, no. 2 (May 15, 2017): 99. http://dx.doi.org/10.20870/oeno-one.2016.0.0.1538.

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Grapevines are highly sensitive to environmental conditions, with variability in weather and climate (particularly temperature) having a significant influence on wine quality, quantity and style. Improved knowledge of spatial and temporal variations in climate and their impact on grapevine response allows better decision-making to help maintain a sustainable wine industry in the context of medium to long term climate change. This paper describes recent research into the application of mesoscale weather and climate models that aims to improve our understanding of climate variability at high spatial (1 km and less) and temporal (hourly) resolution within vineyard regions of varying terrain complexity. The Weather Research and Forecasting (WRF) model has been used to simulate the weather and climate in the complex terrain of the Marlborough region of New Zealand. The performance of the WRF model in reproducing the temperature variability across vineyard regions is assessed through comparison with automatic weather stations. Coupling the atmospheric model with bioclimatic indices and phenological models (e.g. Huglin, cool nights, Grapevine Flowering Véraison model) also provides useful insights into grapevine response to spatial variability of climate during the growing season, as well as assessment of spatial variability in the optimal climate conditions for specific grape varieties.

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Sturman, Andrew, Peyman Zawar-Reza, Iman Soltanzadeh, Marwan Katurji, Valérie Bonnardot, Amber Kaye Parker, Michael C. T. Trought, et al. "The application of high-resolution atmospheric modelling to weather and climate variability in vineyard regions." OENO One 51, no. 2 (May 15, 2017): 99–105. http://dx.doi.org/10.20870/oeno-one.2017.51.2.1538.

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Grapevines are highly sensitive to environmental conditions, with variability in weather and climate (particularly temperature) having a significant influence on wine quality, quantity and style. Improved knowledge of spatial and temporal variations in climate and their impact on grapevine response allows better decision-making to help maintain a sustainable wine industry in the context of medium to long term climate change. This paper describes recent research into the application of mesoscale weather and climate models that aims to improve our understanding of climate variability at high spatial (1 km and less) and temporal (hourly) resolution within vineyard regions of varying terrain complexity. The Weather Research and Forecasting (WRF) model has been used to simulate the weather and climate in the complex terrain of the Marlborough region of New Zealand. The performance of the WRF model in reproducing the temperature variability across vineyard regions is assessed through comparison with automatic weather stations. Coupling the atmospheric model with bioclimatic indices and phenological models (e.g. Huglin, cool nights, Grapevine Flowering Véraison model) also provides useful insights into grapevine response to spatial variability of climate during the growing season, as well as assessment of spatial variability in the optimal climate conditions for specific grape varieties.

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Haouas, Nabiha, and Pierre R. Bertrand. "Wind Farm Power Forecasting." Mathematical Problems in Engineering 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/163565.

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Forecasting annual wind power production is useful for the energy industry. Until recently, attention has only been paid to the mean annual wind power energy and statistical uncertainties on this forecasting. Recently, Bensoussan et al. (2012) have pointed that the annual wind power produced by one wind turbine is a Gaussian random variable under a reasonable set of assumptions. Moreover, they can derive both mean and quantiles of annual wind power produced by one wind turbine. The novelty of this work is the obtainment of similar results for estimating the annual wind farm power production. Eventually, we study the relationship between the power production for each turbine of the farm in order to avoid interaction between them.

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Sopeña, Juan Manuel González, Vikram Pakrashi, and Bidisha Ghosh. "Decomposition-Based Hybrid Models for Very Short-Term Wind Power Forecasting." Engineering Proceedings 5, no. 1 (July 7, 2021): 39. http://dx.doi.org/10.3390/engproc2021005039.

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Wind power forecasting is a tool used in the energy industry for a wide range of applications, such as energy trading and the operation of the grid. A set of models known as decomposition-based hybrid models have stood out in recent times due to promising results in terms of performance. As many publications on this matter are found in the literature, a comparison of these models is difficult, because they are tested under different conditions in terms of data, prediction horizon, and time resolution. In this paper, we provide a comparison unifying these parameters using the main decomposition algorithms and a set of artificial neural network-based models for very short-term wind power forecasting (up to 30 min ahead). For this purpose, a case study using data from an Irish wind farm is performed to analyze the models in terms of accuracy and robustness for a variety of wind power generation scenarios.

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Li, Guo Jian, and Yan Jun Hu. "Analysis and Discussion of the Influence Factors of the Wind Power." Advanced Materials Research 383-390 (November 2011): 7595–99. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.7595.

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Wind as a renewable energy, is typical of clean energy, and wind power generation has good social and environmental benefits, which has developed rapidly in worldwide. In this paper, the problems of China's wind power industry and the world wind power industry experience are discussed. The distribution of resources for wind energy, wind energy resource assessment, monitoring and forecasting system, wind industry, policy influencing factors are detailed analysis, and based on China conditions for its development were discussed.

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Fang, Jicheng, Dongqin Shen, Xiuyi Li, and Huijia Li. "An efficient power load forecasting model based on the optimized combination." Modern Physics Letters B 34, no. 12 (March 30, 2020): 2050114. http://dx.doi.org/10.1142/s0217984920501146.

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The new energy industry gains more and more attention since the problem of resource scarcity and utilization of the renewable energy has become a global highlight issue. In this paper, we propose a new load forecasting model under the development of new energy industry by choosing the typical wind power as the key subject, which is also an important reference for other energy industries. The wind power load forecasting model is built based on optimized combination, which is forecasted and analyzed by the time series, the Markov and the gray forecasting models individually, and then combined by the optimized weighting coefficients. The method has overcome the limitations of poor adaptability of the single forecasting models and come out with an ideal result. Experimental results show our method has better performance compared with other related algorithms in different datasets.

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Würth, Ines, Laura Valldecabres, Elliot Simon, Corinna Möhrlen, Bahri Uzunoğlu, Ciaran Gilbert, Gregor Giebel, David Schlipf, and Anton Kaifel. "Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36." Energies 12, no. 4 (February 21, 2019): 712. http://dx.doi.org/10.3390/en12040712.

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The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.

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Li, Chun Fa, and Ting Ting Sun. "Research on Technology Roadmaps of the Wind Power Industry Based on Bibliometrics and AHP Method - A Case Study of Wind Blade." Advanced Materials Research 1044-1045 (October 2014): 397–400. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.397.

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By taking wind turbine blade as an example, this article built the technology roadmaps model to research the critical techniques in wind power industry. In particular, the collection and visualized analysis were implemented with the use of bibliometrics and relative software; we realized the technology assessment of critical techniques by structuring AHP model; finally, the technology roadmaps were made based on evaluation result and the technology forecasting of wind blades was realized combining with the industry conditions.Please make t

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Otero-Casal, Carlos, Platon Patlakas, Miguel A. Prósper, George Galanis, and Gonzalo Miguez-Macho. "Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter." Energies 12, no. 16 (August 8, 2019): 3050. http://dx.doi.org/10.3390/en12163050.

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Regional microscale meteorological models have become a critical tool for wind farm production forecasting due to their capacity for resolving local flow dynamics. The high demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting (WRF) atmospheric model with an optimization obtained by the conjunction of a Kalman filter and a Bayesian model. This study focuses on the development and validation of this combined system in a very dense wind farm cluster located in Galicia (Northwest of Spain). A period of one year is simulated at 333 m horizontal resolution, with a daily operational forecasting set-up. The Kalman-Bayesian filter was tested both directly on wind speed and on the U-V (zonal and meridional) components for nowcasting periods from 10 min to 6 h periods, all of them with important applications in the wind industry. The results are quite promising, as the main statistical error indices are significantly improved in a 6 h forecasting horizon and even more in shorter horizon cases. The Mean Annual Error (MAE) for 1 h nowcasting horizon is 1.03 m/s for wind speed and 12.16 ° for wind direction. Moreover, the successful utilization of the integrated system in test cases with different characteristics demonstrates the potential utility that this tool may have for a variety of applications in wind farm operations and energy markets.

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Tesis sobre el tema "Wine industry Forecasting"

Berger, Nicholas. "Modelling structural and policy changes in the world wine market into the 21st century." Title page, contents and abstract only, 2000. http://web4.library.adelaide.edu.au/theses/09ECM/09ecmb496.pdf.

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Includes bibliographical references. Addresses the question of what an economic model of the world wine market suggests will happen to wine production, consumption, trade and prices in various regions in the early 21st century. A subsidiary issue is what difference would global or European regional wine liberalisation make to that outlook, according to such a model. Accompanying CD-ROM comprises spreadsheet written by Nick Berger, November 2000, for the Windows and Office97 versions of Excel; a seven region world wine model (WWM7) - base version projecting the world wine market 1996-2005 as a non-linear Armington model. System requirements for accompanying CD-ROM: IBM compatible computer ; Microsoft Excel 97 or later.

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Jourdier, Bénédicte. "Study and implementation of mesoscale weather forecasting models in the wind industry." Thesis, KTH, Kraft- och värmeteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91322.

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As the wind industry is developing, it is asking for more reliable short-term wind forecasts to better manage the wind farms’ operations and electricity production. Developing new wind farms also requires correct assessments of the long-term wind potentials to decide whether to install a wind farm at a specific location. This thesis is studying a new generation of numerical weather forecasting models, named mesoscale models, to see how they could answer those needs. It is held at the company Maïa Eolis which operates several wind farms in France. A mesoscale model, the Weather Research and Forecasting model (WRF), was chosen and used to generate high resolution forecasts based on lower resolution forecasts from NCEP’s Global Forecasting System. The stages for implementation of daily forecasts for the company’s wind farms were: explore and configure the model, automate the runs, develop post-processing tools and forecasts visualization software which was intended to be used by the management team. WRF was also used to downscale wind archives of NCEP’s Final Analysis and determine the possibility to use these in assessing wind potentials. Finally the precision of the model in both cases and for each wind farm was assessed by comparing attained data from the model with real power production.

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Lledó, Ponsatí Llorenç. "Climate variability predictions for the wind energy industry: a climate services perspective." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670882.

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In order to mitigate the climate change effects, the world is undergoing an energy transition from polluting sources towards renewable energies. This transition is turning the electricity system more dependent on atmospheric conditions and more prone to suffer the effects of climate variability. The atmospheric circulation is changing in certain aspects due to increasing concentrations of greenhouse gases in the atmosphere, but it also varies from year to year due to natural variability processes occurring in the Earth system at timescales of weeks, months and years. The atmosphere interacts with other components of the Earth System such as the ocean, the cryosphere or the continental surface, that evolve more slowly than the atmosphere and drive the low-frequency variability. The natural climate oscillations that occur at those timescales impact wind speed and wind power generation. Therefore a better knowledge of how the wind resource varies at sub-seasonal, seasonal and decadal time scales is key to understand the risks that the electricity system is facing. Anticipating this variability would also be helpful to many stakeholders in the energy sector to take precautionary actions. Forecasts at sub-seasonal, seasonal and decadal timescales are starting to be possible recently thanks to advances in climate modelling capabilities. Because climate variability is partly driven by coupled physical processes occurring in the Earth, numerical models that represent the interaction between different components of the Earth system can be employed to produce forecasts at these scales. The science of climate prediction deals with the challenge of producing predictions beyond meteorological timescales (i.e. weeks, months and years ahead) although not reaching the centennial timescales, which are studied with scenario-based climate projections. Climate predictions employ the current state of the atmosphere, the ocean, the cryosphere, and the land surface to produce numerical integrations of each component and the forcings and interactions between them to model the evolution of the Earth system as a whole. However, the usage of climate predictions in the wind power sector (or more generally in any specific decision-making context) poses a series of difficulties due to many complex aspects of this type of predictions. The efforts devoted in many initiatives to bring the needs of the users to the center of the discussion have given rise to the field of climate services. In order to assist decision-making, it is not only desirable to have the best predictions available but also to tailor them to the specific needs of each user. To achieve this goal, a dialogue with stakeholders needs to be established, and a trans- disciplinary approach needs to be set up to take advantage of the developments in many research fields regarding knowledge transfer and communication. The work presented in this dissertation advances the knowledge required to produce and successfully apply climate predictions to decision-making in the wind power sector and deals with the three aforementioned challenges: a) understanding the impact of climate oscillations at sub-seasonal and seasonal timescales on wind resource; b) developing methods to produce forecasts of wind speed and wind power generation at this scales; and c) facilitating the uptake of those predictions by means of a climate-services-based approach. Per tal de mitigar els efectes del canvi climàtic, tots els països del món estan duent a terme una transició energètica de fonts contaminants cap a energies renovables. Aquesta transició està incrementant la sensibilitat del sistema elèctric a les condicions atmosfèriques i fent-lo més vulnerable als efectes de la variabilitat climàtica. A escales de setmanes, mesos i anys, l'atmosfera interacciona amb altres components del sistema Terra com l'oceà, la criosfera o la superfície continental, que evolucionen més lentament que l'atmosfera, condicionant-ne la seva variabilitat a baixa freqüència. Al seu torn, les oscil·lacions que tenen lloc a aquestes escales temporals impacten el vent i la generació d'energia eòlica. Per tant, un millor coneixement de com varia el recurs eòlic a escales sub-estacionals, estacionals i decadals permetrà anticipar els riscs a què el sistema elèctric està sotmès. En segon lloc, anticipar aquesta variabilitat climàtica seria de gran utilitat a diversos actors del sistema energètic. L'ús de models climàtics que representen les interaccions entre les diferents components del sistema Terra permet abordar el repte de produir pronòstics més enllà de l'escala meteorològica (és a dir, a setmanes, mesos i anys vista). Malgrat tot, l'ús de les prediccions climàtiques en el sector de l'energia eòlica presenta una sèrie de dificultats degut a les complexitats d'aquest tipus de previsions. Per tal d'assistir la presa de decisions, no només és necessari disposar de les millors prediccions possibles sinó que cal també ajustar-les a les necessitats específiques de cada ús. Aquest objectiu només es pot assolir amb un diàleg constant i transdisciplinari entre els científics i les parts interessades que integri els avenços en diferents àmbits respecte la transferència de coneixement i la comunicació. Aquesta tesi avança el coneixement necessari per tal de produir i aplicar prediccions climàtiques a la presa de decisions per part de la indústria eòlica, abordant tres reptes: a) avaluar l'impacte d'oscil·lacions climàtiques sub-estacionals i estacional en el recurs eòlic; b) desenvolupar mètodes per produir prediccions de vent o de generació eòlica a aquestes escales; i c) facilitar l'adopció d'aquestes previsions mitjançant una aproximació basada en els serveis climàtics.

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Dias, Paula Samara Oliveira Araújo Coelho de Souza. "Aplicação de princípios e ferramentas Lean na melhoria de processos de uma indústria de vinhos." Master's thesis, 2019. http://hdl.handle.net/1822/64151.

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Dissertação de mestrado em Engenharia e Gestão da Qualidade Para se manterem competitivas e assegurarem a sustentabilidade dos seus negócios, as organizações, em particular as indústrias do vinho, precisam de definir e rever continuamente a sua estratégia e, a partir desta, elaborar e por em prática um plano de ação orientado para os resultados desejados, que integre os planos de vendas e de produção. Assim, para o sucesso da estratégia empresarial é importante que o plano de vendas esteja assente num bom modelo de previsão de procura e que o processo produtivo seja eficiente. Nesse sentido, através do estudo do caso da Lavradores de Feitoria, Vinhos de Quinta S.A. (LDF), buscou-se validar a hipótese de que a aplicação da filosofia Lean por empresas do setor dos vinhos, pode melhorar os seus processos operacionais e de gestão, aumentando o valor para o cliente e contribuindo para o alcance dos seus resultados estratégicos. Para este efeito, o presente trabalho descreve como se dava o processo de S&OP da LDF, assim como o seu processo de rotulagem, e explica de que forma a implementação e uso de princípios e técnicas Lean promovem a tomada de decisões baseada em evidências, a redução e/ou eliminação de desperdícios e a realização de pequenas melhorias na produção. Entre as ações de melhoria propostas ao longo do trabalho, aponta-se: a adoção de indicadores de desempenho; o ajuste da previsão de vendas para passar a ser feita não só ao nível do volume anual de vendas, por tipo de vinho, mas também ao nível das quantidades que se espera vender por mês, de cada produto, com recurso à estatística; a implementação da técnica SMED para redução dos tempos de setup; a proposição de um modelo de mapa de trabalho, para colmatar os desperdícios associados a sua inexistência; a identificação de desperdícios e suas causas, através do estudo dos tempos e movimentos da rotulagem e de diagramas de causa e efeito; o cálculo do índice de rotatividade dos artigos em stock; e, finalmente, a demonstração de como uma metodologia básica da qualidade, como o 5S, poderia otimizar o armazém. In order to remain competitive and ensure the sustainability of their business, organizations, particularly the wine industries, need to continually define and review their strategy and, from this, develop and implement a results-oriented action plan, integrating sales and production plans. Thus, for the success of the business strategy it is important that the sales plan is based on a good demand forecasting model and that the production process is efficient. In this sense, by studying the case of Lavradores de Feitoria, Vinhos de Quinta S.A., it was sought to validate the hypothesis that the application of Lean principles and techniques by companies in the wine sector, can improve their operational and management processes, increase customer´s value perception and contribute to the achievement of their strategic results. To this end, this paper describes how is LDF´s S&OP process, as well as its labeling process, and explains how the implementation and use of Lean principles and techniques promote evidence-based decision making, reducing and/or eliminating waste and making little improvements in production. Among the improvement actions proposed throughout the work, are: the adoption of performance indicators; the adjustment of the sales forecast to be made not only at the level of annual sales volume, by type of wine, but also at the level of the quantities expected to be sold per month, for each product, using statistics; the implementation of the SMED technique to reduce setup times; the proposition of a work map model to avoid the waste associated with its non-existence; the identification of waste and its causes through the study of labeling times and movements, and cause and effect diagrams; the calculation of stock´s turnover; and finally, the demonstration of how a basic quality methodology, such as the 5S, could optimize the warehouse.

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Lee, Hsing-Wei, and 李興煒. "A Sales Forecasting Model for Low Voltage Power Cable - A Case Study of Taiwan Wire and Cable Industry." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/syavws.

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碩士 輔仁大學 企業管理學系管理學碩士在職專班 107 The traditional industry of wire and cable in Taiwan has constructed over six decades, not only has the supply chain in the region complete built, but also has developed overseas and spanned to different industries. However, the international environment has become more and more intense due to the Sino-US trade war. Not only caused China’s 40 years lasting economic sprinting development staggered, and fallen into a major adjustment. The Taiwanese manufacturers are facing the rising wolf-like competitors from China and break into the ferocious red sea market. Under the harsh external environment, the traditional Taiwanese wire and cable manufacturers have adapted to the "digital" reengineering, and reorganized that there is the only way to maintain competitiveness and become excellent under the digital wave. The manufacturers of wire and cable in Taiwan usually forecast demand based on empirical rules. Hence, it’s difficult to make production efficiently and economically. The purpose of this study is to propose a sales forecasting model for low voltage power cable by data mining technique. The transaction data extracted from a case company was used to evaluate the forecasting model. The sales forecasting model proposed by this study is expected to provide quick response service which is one of the important requirements of customers in the low voltage power cable industry. This study analyzed the demand of upstream power industry and wire & cable industry. Then a sales forecasting model was proposed based on business indicators, real estate indicators, copper price, and competitive density. The research results of this study provide useful implications for manufacturers to develop sales forecasting model.

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Libros sobre el tema "Wine industry Forecasting"

Ali, Abdalla, and Australian Bureau of Agricultural and Resource Economics., eds. Wine grapes: Projections of wine grape production and winery intake to 1994-95. Canberra: Australian Bureau of Agricultural and Resource Economics, 1992.

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Industrieller, Vereinigung Österreichischer, Österreichische Investitionskredit Aktiengesellschaft, and Österreichische Industrieverwaltungs-Aktiengesellschaft, eds. Industrie 2000: Im Haus der Industrie : eine Konferenz über die österreichische Industrie im Jahr 2000, Wien, 25.-27. Oktober 1985 : Dokumentation. Wien: Signum, 1986.

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Potential economic benefits from commercial wind power facilities in the state of New Mexico. Denver, Colo: BBC Research & Consulting, 2000.

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Capítulos de libros sobre el tema "Wine industry Forecasting"

Phan, Kenny, and Tugrul Daim. "Forecasting the Maturity of Alternate Wind Turbine Technologies Through Patent Analysis." In Research and Technology Management in the Electricity Industry, 189–211. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5097-8_8.

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Brenner, Daniel, Dietmar Tilch, and Patrick Bangert. "Forecasting wind power plant failures." In Machine Learning and Data Science in the Power Generation Industry, 241–53. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-819742-4.00012-3.

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Chen, Zixin, Yongqian Liu, Aimei Lin, Shuang Han, Li Li, and Jie Yan*. "Wind power ramp forecasting based on deep metric learning." In Emerging Developments in the Power and Energy Industry, 572–80. CRC Press, 2019. http://dx.doi.org/10.1201/9780429295300-73.

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Abdou, Alaa, John Lewis, Moh’d A. Radaideh, and Sameera Al Zarooni. "Web-Based Information Systems in Construction Industry." In Encyclopedia of Internet Technologies and Applications, 702–10. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59140-993-9.ch099.

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This paper describes the development and construction of a Web-based system for the appraisal stage of public healthcare construction projects in the United Arab Emirates. The system is implemented on the World Wide Web. PHP and MySQL were selected as the scripting language and database management system to build this system prototype. Its main objectives focus on assisting decision-makers in examining different function program alternatives and their associated conceptual budgets. In addition, the system facilitates reflecting uncertainty and risk factors associated with healthcare space programming into cost estimating and forecasting processes.

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Deo, Ravinesh C., Sujan Ghimire, Nathan J. Downs, and Nawin Raj. "Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 116–47. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch007.

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The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model.

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Pal, Kamalendu. "Building High Quality Big Data-Based Applications in Supply Chains." In Supply Chain Management Strategies and Risk Assessment in Retail Environments, 1–24. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3056-5.ch001.

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Global retail business has become diverse and latest Information Technology (IT) advancements have created new possibilities for the management of the deluge of data generated by world-wide business operations of its supply chain. In this business, external data from social media and supplier networks provide a huge influx to augment existing data. This is combined with data from sensors and intelligent machines, commonly known as Internet of Things (IoT) data. This data, originating from the global retail supply chain, is simply known as Big Data - because of its enormous volume, the velocity with which it arrives in the global retail business environment, its veracity to quality related issues, and values it generates for the global supply chain. Many retail products manufacturing companies are trying to find ways to enhance their quality of operational performance while reducing business support costs. They do this primarily by improving defect tracking and better forecasting. These manufacturing and operational improvements along with a favorable customer experience remain crucil to thriving in global competition. In recent years, Big Data and its associated technologies are attracting huge research interest with academics, industry practitioners, and government agencies. Big Data-based software applications are widely used within retail supply chain management - in recommendation, prediction, and decision support systems. The spectacular growth of these software systems has enormous potential for improving the daily performance of retail product and service companies. However, there are increasingly data quality problems resulting in erroneous tesing costs in retail Supply Chain Management (SCM). The heavy investment made in Big Data-based software applications puts increasing pressure on management to justify the quality assurance in these software systems. This chapter discusses about data quality and the dimensions of data quality for Big Data applications. It also examines some of the challenges presented by managing the quality and governance of Big Data, and how those can be balanced with the need of delivery usable Big Data-based software systems. Finally, the chapter highlights the importance of data governance; and it also includes some of the Big Data managerial practice related issues and their justifications for achieving application software quality assurance.

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Actas de conferencias sobre el tema "Wine industry Forecasting"

Elsaraiti, Meftah, Adel Merabet, and Ahmed Al-Durra. "Time Series Analysis and Forecasting of Wind Speed Data." In 2019 IEEE Industry Applications Society Annual Meeting. IEEE, 2019. http://dx.doi.org/10.1109/ias.2019.8912392.

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Liu, Meng, Franklin L. Quilumba, and Wei-Jen Lee. "Dispatch scheduling for a wind farm with hybrid energy storage based on wind and LMP forecasting." In 2014 IEEE Industry Applications Society Annual Meeting. IEEE, 2014. http://dx.doi.org/10.1109/ias.2014.6978378.

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Wu, Yuan-Kang, Po-En Su, and Jing-Shan Hong. "Stratification-based wind power forecasting in a high penetration wind power system using a hybrid model with charged system search algorithm." In 2015 IEEE Industry Applications Society Annual Meeting. IEEE, 2015. http://dx.doi.org/10.1109/ias.2015.7356793.

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Ma, Hui-Meng, Bei Li, and Xiao-Qing Xiu. "Configuration method of energy storage based on short-term wind power forecasting technique." In 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI 2012). IEEE, 2012. http://dx.doi.org/10.1109/carpi.2012.6356286.

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Hung, Tzu-Chieh, and Kuei-Yuan Chan. "Probability-Based Power Dispatch in Wind-Integrated Electrical Grid for Energy Storage Capacity Determination." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59809.

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Implementing microgrids has become a current trend in the electric utility industry to either improve system reliability or energy access for energy sustainability. This study proposes a probability-based strategy for both long- and short-term power dispatch with wind and load uncertainty. The long-term power dispatch is used to determine a suitable capacity of energy storage, and the short-term power dispatch is used for real-time operation. For both short- and long-term power dispatch, the trends of wind energy and electricity demand are extracted using the wavelet packet analysis method and the moving average technique. The uncertainties from wind speed and power generation data are modeled with log-normal and extreme value distributions, respectively. From the obtained power dispatch and model forecasting, the capacity of energy storage is determined. To validate the proposed approach, a real-time operating simulation is used as a case study to observe the behavior of the wind-integrated electrical system. Results show that the proposed method can estimate the uncertainty variation range of wind energy and the state of charge of energy storage effectively.

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DeLeon, Rey, Kyle Felzien, and Inanc Senocak. "Toward a GPU-Accelerated Immersed Boundary Method for Wind Forecasting Over Complex Terrain." In ASME 2012 Fluids Engineering Division Summer Meeting collocated with the ASME 2012 Heat Transfer Summer Conference and the ASME 2012 10th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/fedsm2012-72145.

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A short-term wind power forecasting capability can be a valuable tool in the renewable energy industry to address load-balancing issues that arise from intermittent wind fields. Although numerical weather prediction models have been used to forecast winds, their applicability to micro-scale atmospheric boundary layer flows and ability to predict wind speeds at turbine hub height with a desired accuracy is not clear. To address this issue, we develop a multi-GPU parallel flow solver to forecast winds over complex terrain at the micro-scale, where computational domain size can range from meters to several kilometers. In the solver, we adopt the immersed boundary method and the Lagrangian dynamic large-eddy simulation model and extend them to atmospheric flows. The computations are accelerated on GPU clusters with a dual-level parallel implementation that interleaves MPI with CUDA. We evaluate the flow solver components against test problems and obtain preliminary results of flow over Bolund Hill, a coastal hill in Denmark.

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Zhang, Chen, Tao Yang, Wei Gao, Weiqiu Chen, Jing He, and Xingwang Yang. "A Spare Parts Demand Prediction Method for Wind Farm Based on Periodic Maintenance Strategy." In ASME 2017 Power Conference Joint With ICOPE-17 collocated with the ASME 2017 11th International Conference on Energy Sustainability, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/power-icope2017-3077.

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Nowadays, the management level and information construction of wind power industry are still relatively backward, for example, the existing maintenance models for wind farm are much too single, and corrective maintenance strategy is the most commonly used, which means that maintenance measures are initiated only after a breakdown occurs in the system. Moreover, the wind farm spare parts management is out-dated, no practical and accurate spares demand assessment method is available. In order to enrich the choices of maintenance methods and eliminate the subjective influence in the demand analysis of spare parts, a spare parts demand prediction method for wind farm based on periodic maintenance strategy considering combination of different maintenance models for wind farms is proposed in this paper, which consists of five major steps, acquire the reliability functions of components, establish the maintenance strategy, set the maintenance parameters, maintenance strategy simulation and spare parts demand prediction. The discrete event simulation method is used to solve the prediction model, and results demonstrate the operability and practicality of the proposed demand forecasting method, which can provide guidance for the actual operation and maintenance of wind farms.

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Vogt, Brett D., and Raymond “Buddy” E. Belcher. "Deploying Mobile Construction Inspection Forms as a Case Study for Technology Adoption." In 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64637.

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Within pipeline construction field inspection data collection relies mostly on archaic systems and processes. For almost all projects, paper-based or at best word processor and spreadsheet reports are manually collected, reviewed, aggregated and archived. The effort and error in this typical process is reduced using a mobile inspection form system that simplifies the field data collection workflow, increases data accuracy and quality, and can be used to generate dynamic project management dashboards. An evaluation of two case study projects provides insight to overcoming technology adoption for pipeline construction as well as performance, quality and forecasting benefits witnessed during these projects. The use of a mobile inspection form system creates the ability for improved analytics such as detailed construction tracking, dynamic forecasting and spatial overlays of construction progress. Improved data standardization and data integrity from the use of tablet forms produces detailed and functional key performance indicators (KPIs) delivered on-demand through a project dashboard. When both field data quality is improved and project managers are provided timely KPIs, projects have the opportunity to be delivered safer, faster and with higher quality, which is a win for the entire pipeline industry.

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Kooij, C., A. P. Colling, and C. L. Benson. "When will autonomous ships arrive? A technological forecasting perspective." In 14th International Naval Engineering Conference and Exhibition. IMarEST, 2018. http://dx.doi.org/10.24868/issn.2515-818x.2018.016.

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Autonomous ships have received significant attention in recent years. However, they are not widely adopted in the maritime industry yet. A wide range of predictions have been made about when the technological change will occur. This paper analyses technologies that are critical to autonomous shipping and forecasts a range of times when they will reach technical and economic viability. The researched technologies are data transfer, navigation, cargo handling, fuel cells and diesel engines. The results indicate that the GPS precision required for autonomous mooring is not yet technically feasible and the expected feasibility time frame is between 2030 and 2058. The remaining technologies all show technological feasibility, but not yet economic viability. The forecasted range for economic viability of data transfer is a range of 2026-2041, while cost of automated cargo handling will reach the current expense levels somewhere between 2037 and 2101. Finally, the cost of a medium speed diesel engine and an LT-PEMFC Fuel Cell will be approximately equal somewhere between 2025 and 2060.

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Ivanov, Leonid, Rafael Ramos, and Drew Gustafson. "Energetics and Kinematics of Inertial Oscillations in the Central Northern GOM." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31020-ms.

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Abstract Understanding the physics of generation, propagation, and dissipation of inertial currents is important from a variety of aspects. For the Gulf of Mexico, one such aspect is that these oscillations represent an uncertainty in the measurements and forecasting of the longer-period currents, such as those due to the Loop Current (LC) and meso-scale eddies. The Industry has a practice of applying an ‘uplift’ to estimates of current velocity to account for the effect of tidal and inertial currents in cases when observations or model estimates do not resolve the high-frequency current variability. The value of the ‘uplift’ is assumed to be proportional to the intensity of the low-frequency flow. Our analysis aims at testing whether this assumption is valid by providing a detailed description of the space-time variability, including seasonal changes, of inertial oscillations in the central northern Gulf of Mexico. From the analysis of long-term current profile observations and drifter data we found that, on average, near-inertial oscillations have higher amplitudes outside of the areas of strong low-frequency currents associated with a Loop Current Eddy (LCE). Within the upper 200m of the water column, periods characterized by the downward energy propagation dominate. In the layer below 200m, near-inertial waves propagate upward and downward, and the wave trains cannot be traced to a single source of energy. This suggests near-inertial waves within the main part of the water column are of ‘global’ rather than of ‘local’ origin. For most near-inertial wave generation events through wind forcing, the downward energy propagation could not be traced for any extended period of time and no deeper than approximately 200-m depth. The rate of downward energy propagation in the upper pycnocline is on the order of 10-12 m/day. For the near-inertial currents, the first two Empirical Orthogonal Functions (EOF) contribute only 40% into the total current variability for the period of LCE presence and 52% for the period of benign current conditions. The mode shapes vary within a wide range that, most likely, reflects a random distribution of mode shapes that depend on the lateral geometry of the forcing, mixed layer depth, and stratification.

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