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Journal articles on the topic 'Wine industry Forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

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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2

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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<p>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

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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 spa

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4

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. E

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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

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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

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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 barrie

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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 (Northwes

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Vassallo, Daniel, Raghavendra Krishnamurthy, Thomas Sherman, and Harindra J. S. Fernando. "Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting." Energies 13, no. 20 (October 20, 2020): 5488. http://dx.doi.org/10.3390/en13205488.

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Although the random forest (RF) model is a powerful machine learning tool that has been utilized in many wind speed/power forecasting studies, there has been no consensus on optimal RF modeling strategies. This study investigates three basic questions which aim to assist in the discernment and quantification of the effects of individual model properties, namely: (1) using a standalone RF model versus using RF as a correction mechanism for the persistence approach, (2) utilizing a recursive versus direct multi-step forecasting strategy, and (3) training data availability on model forecasting ac

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Gopalakrishnan, Kasthurirangan, and Konstantina Nadia Gkritza. "FORECASTING TRANSPORTATION INFRASTRUCTURE IMPACTS OF RENEWABLE ENERGY INDUSTRY USING NEURAL NETWORKS." Technological and Economic Development of Economy 19, Supplement_1 (January 28, 2014): S157—S175. http://dx.doi.org/10.3846/20294913.2013.876690.

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Iowa is a state rich in renewable energy resources, especially biomass. The successful development of renewable energy industry in Iowa is concomitant with increase in freight traffic and is likely to have significant impacts on transportation infrastructure condition and increased maintenance expenses for the state and local governments. The primary goal of this paper is to investigate the feasibility of employing the Neural Networks (NN) methodology to forecast the impacts of Iowa's biofuels and wind power industries on Iowa's secondary and local road condition and maintenance-related costs

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Djalalova, Irina V., Laura Bianco, Elena Akish, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Larry K. Berg, et al. "Wind Ramp Events Validation in NWP Forecast Models during the Second Wind Forecast Improvement Project (WFIP2) Using the Ramp Tool and Metric (RT&M)." Weather and Forecasting 35, no. 6 (December 2020): 2407–21. http://dx.doi.org/10.1175/waf-d-20-0072.1.

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AbstractThe second Wind Forecast Improvement Project (WFIP2) is a multiagency field campaign held in the Columbia Gorge area (October 2015–March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ra

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14

Jiang, Ping, and Qingli Dong. "A New Hybrid Model Based on an Intelligent Optimization Algorithm and a Data Denoising Method to Make Wind Speed Predication." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/714605.

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To mitigate the increase of anxiety resulting from the depletion of fossil fuels and destruction of the ecosystem, wind power, as the most common renewable energy, is a flourishing industry. Thus, accurate wind speed forecasting is critical for the efficient function of wind farms. However, affected by complicated influence factors in meteorology and volatile physical property, wind speed forecasting is difficult and challenging. Based on previous research efforts, an intelligent hybrid model was proposed in this paper in an attempt to tackle this difficult task. First, wavelet transform was u

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15

Huang, He, He Huang, and Qiurui Liu. "Intelligent Retail Forecasting System for New Clothing Products Considering Stock-out." Fibres and Textiles in Eastern Europe 25 (February 28, 2017): 10–16. http://dx.doi.org/10.5604/01.3001.0010.1704.

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Improving the accuracy of forecasting is crucial but complex in the clothing industry, especially for new products, with the lack of historical data and a wide range of factors affecting demand. Previous studies more concentrate on sales forecasting rather than demand forecasting, and the variables affecting demand remained to be optimized. In this study, a two-stage intelligent retail forecasting system is designed for new clothing products. In the first stage, demand is estimated with original sales data considering stock-out. The adaptive neuro fuzzy inference system (ANFIS) is introduced i

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Balakrishnan Sivakumar and Chikkamadaiah Nanjundaswamy. "Weather monitoring and forecasting system using IoT." Global Journal of Engineering and Technology Advances 8, no. 2 (August 30, 2021): 008–16. http://dx.doi.org/10.30574/gjeta.2021.8.2.0109.

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The system proposed is an advanced solution for weather monitoring that uses IoT to make its real time data easily accessible over a very wide range. The system deals with monitoring weather and climate changes like temperature, humidity, wind speed, moisture, light intensity, UV radiation and even carbon monoxide levels in the air; using multiple sensors. These sensors send the data to the web page and the sensor data is plotted as graphical statistics. The data uploaded to the web page can easily be accessible from anywhere in the world. The data gathered in these web pages can also be used

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K, Mahesh, Dr M. V. Vijayakumar, and Gangadharaiah Y.H . "A Statistical Analysis and Datamining Approach for Wind Speed Predication." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 2 (December 18, 2014): 5464–78. http://dx.doi.org/10.24297/ijct.v14i2.2077.

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The wind power industry has seen an unprecedented growth in last few years. The surge in orders for wind turbines has resulted in a producers market. This market imbalance, the relative immaturity of the wind industry, and rapid developments in data processing technology have created an opportunity to improve the performance of wind farms and change misconceptions surrounding their operations. This research offers a new paradigm for the wind power industry, data-driven modeling. Each wind Mast generates extensive data for many parameters, registered as frequently as every minute. As the predic

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18

Jiang, Ping, Shanshan Qin, Jie Wu, and Beibei Sun. "Time Series Analysis and Forecasting for Wind Speeds Using Support Vector Regression Coupled with Artificial Intelligent Algorithms." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/939305.

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Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is growing into a large-scale business. Reliable short-term wind speed forecasts play a practical and crucial role in wind energy conversion systems, such as the dynamic control of wind turbines and power system scheduling. In this paper, an intelligent hybrid model for short-term wind speed prediction is examined; the model is based on cross correlation (CC) analysis and a support

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Schütz Roungkvist, Jannik, Peter Enevoldsen, and George Xydis. "High-Resolution Electricity Spot Price Forecast for the Danish Power Market." Sustainability 12, no. 10 (May 22, 2020): 4267. http://dx.doi.org/10.3390/su12104267.

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Energy markets with a high penetration of renewables are more likely to be challenged by price variations or volatility, which is partly due to the stochastic nature of renewable energy. The Danish electricity market (DK1) is a great example of such a market, as 49% of the power production in DK1 is based on wind power, conclusively challenging the electricity spot price forecast for the Danish power market. The energy industry and academia have tried to find the best practices for spot price forecasting in Denmark, by introducing everything from linear models to sophisticated machine-learning

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20

Zhang, Yang, Yidong Peng, Xiuli Qu, Jing Shi, and Ergin Erdem. "A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications." Energies 14, no. 9 (April 21, 2021): 2352. http://dx.doi.org/10.3390/en14092352.

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Enhancing forecasting performance in terms of both the expected mean value and variance has been a critical challenging issue for energy industry. In this paper, the novel methodology of finite mixture Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) approach with Expectation–Maximization (EM) algorithm is introduced. The applicability of this methodology is comprehensively evaluated for the forecasting of energy related time series including wind speed, wind power generation, and electricity price. Its forecasting performances are evaluated by various criteria, and also compa

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Ibrahim, Mariam, Ahmad Alsheikh, Qays Al-Hindawi, Sameer Al-Dahidi, and Hisham ElMoaqet. "Short-Time Wind Speed Forecast Using Artificial Learning-Based Algorithms." Computational Intelligence and Neuroscience 2020 (April 25, 2020): 1–15. http://dx.doi.org/10.1155/2020/8439719.

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The need for an efficient power source for operating the modern industry has been rapidly increasing in the past years. Therefore, the latest renewable power sources are difficult to be predicted. The generated power is highly dependent on fluctuated factors (such as wind bearing, pressure, wind speed, and humidity of surrounding atmosphere). Thus, accurate forecasting methods are of paramount importance to be developed and employed in practice. In this paper, a case study of a wind harvesting farm is investigated in terms of wind speed collected data. For data like the wind speed that are har

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Sun, Rong Xia, Jian Li Wang, Pan Pan Huang, Jian Kang, Xiao Feng Chen, and Yi Tian. "Design of Solar-Wind Complementary Grid-Connected Power Generation Monitoring System for Teaching." Advanced Materials Research 383-390 (November 2011): 3628–32. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.3628.

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With the development of new energy industry, the technicians in the area of solar-wind complementary grid-connected power generation are urgently needed. For this reason, the monitoring system of 10kW solar-wind complementary grid-connected power generation was designed. Hardware system includes field device, communication network and monitoring host. Software design includes operation monitoring, application analysis, video surveillance, information issuing. It realizes functions of supervise and control, equipment events and alarm, report forms and print, energy management and forecasting, r

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Gharehchopoghi, Farhad Soleimanian, Freshte Dabaghchi Mokri, and Maryam Molany. "A New Approach in Short-Term Prediction of the Electrical Charge with Regression Models A Case Study." International Journal of Applied Metaheuristic Computing 4, no. 3 (July 2013): 34–46. http://dx.doi.org/10.4018/ijamc.2013070103.

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The accuracy of forecasting of electrical load for the electricity industry has a vital significance in the renewal of economic structure as well as various equations including: purchasing and producing energy, load fluctuation, and the development of infrastructures. Its short-term forecasting has a significant role in designing and utilizing power systems and in the distribution systems and having a variety of systems used to maintain security potentials for the system. In this paper, we attempted to carry out a short-term forecasting of electrical distribution company in west Azerbaijan sta

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Aho, Jacob P., Andrew D. Buckspan, Fiona M. Dunne, and Lucy Y. Pao. "Controlling Wind Energy for Utility Grid Reliability." Mechanical Engineering 135, no. 09 (September 1, 2013): S4—S12. http://dx.doi.org/10.1115/1.2013-sep-4.

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This article provides an overview of utility grid operation by introducing the fundamental behavior of the electrical system, explaining the importance of maintaining grid reliability through balancing generation and load, and describing the methods of providing ancillary services using conventional utilities. This article also introduces the basic structural components of wind turbines, explains the traditional control systems for capturing maximum power, and highlights control methods developed in industry and academia to provide active power ancillary services with wind energy. As the penet

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Yuan, Li. "The Research on Control Strategy of Worms Spread in Complex Network in Industry." Advanced Materials Research 487 (March 2012): 758–63. http://dx.doi.org/10.4028/www.scientific.net/amr.487.758.

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Internet worms have been a severe threat to the Internet infrastructure and hosts recently. However, because of its outbreak of a sudden and massive make the worm’s research facing enormous challenges. This article describes a simulation system based on NS2 worm - WSS. First, by processing BGP routing table information to the Internet used for simulation of the abstract network, then the model-based epidemiological model of worm propagation, and finally in the two in NS2 combination to achieve the simulation of worm propagation. Experiments show that, WSS can get in a lab environment similar t

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Ziel, Florian. "Load Nowcasting: Predicting Actuals with Limited Data." Energies 13, no. 6 (March 20, 2020): 1443. http://dx.doi.org/10.3390/en13061443.

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We introduce the problem of load nowcasting to the energy forecasting literature. The recent load of the objective area is predicted based on limited available metering data within this area. Thus, slightly different from load forecasting, we are predicting the recent past using limited available metering data from the supply side of the system. Next, to an industry benchmark model, we introduce multiple high-dimensional models for providing more accurate predictions. They evaluate metered interconnector and generation unit data of different types like wind and solar power, storages, and nucle

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Baranowski, Paweł, Karol Korczak, and Jarosław Zając. "Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland." Business Systems Research Journal 11, no. 1 (March 1, 2020): 73–88. http://dx.doi.org/10.2478/bsrj-2020-0006.

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AbstractBackground: Cinema programmes are set in advance (usually with a weekly frequency), which motivates us to investigate the short-term forecasting of attendance. In the literature on the cinema industry, the issue of attendance forecasting has gained less research attention compared to modelling the aggregate performance of movies. Furthermore, unlike most existing studies, we use data on attendance at the individual show level (179,103 shows) rather than aggregate box office sales.Objectives: In the paper, we evaluate short-term forecasting models of cinema attendance. The main purpose

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Soboń, Janusz, Natalia Burkina, Kostiantyn Sapun, and Ruslana Seleznova. "Comphrensive Analysis of a Company's Activity by Means of Statistical Modeling as Support for its Decision-Making System." Financial Internet Quarterly 17, no. 1 (March 1, 2021): 62–69. http://dx.doi.org/10.2478/fiqf-2021-0007.

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Abstract An important role in ensuring effective forms of management and increasing competitiveness is played by the process of forecasting the activity of the enterprise. This work analyzed the performance of a food industry enterprise, for which a wide range of statistical methods were applied such as methods of cluster, correlational and regression analysis, statistical tests of Fisher, Student, Farrar-Glauber, Durbin-Watson, Goldfeld-Quandt, μ-criterion, multifactor regression, trend, auto-regression models, and models of seasonal fluctuations, which provided a view of the economic propert

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Carlsson-Hyslop, Anna. "Patronage and Practice in British Oceanography." Historical Studies in the Natural Sciences 46, no. 3 (June 1, 2016): 270–312. http://dx.doi.org/10.1525/hsns.2016.46.3.270.

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The history of twentieth-century American physical oceanography concentrates on naval patronage, but its significance for British oceanography is largely unknown. This case study analyzes a varied patronage structure, including naval, industrial, academic, and local and central governmental support, for one site of British physical oceanography, the Liverpool Observatory and Tidal Institute and, in particular, its work on storm surges between 1919 and 1959. Storm surges, caused by wind and changes in barometric pressure, can produce dramatic changes in sea levels. The local shipping industry i

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Shaw, William J., Larry K. Berg, Joel Cline, Caroline Draxl, Irina Djalalova, Eric P. Grimit, Julie K. Lundquist, et al. "The Second Wind Forecast Improvement Project (WFIP2): General Overview." Bulletin of the American Meteorological Society 100, no. 9 (September 2019): 1687–99. http://dx.doi.org/10.1175/bams-d-18-0036.1.

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AbstractIn 2015 the U.S. Department of Energy (DOE) initiated a 4-yr study, the Second Wind Forecast Improvement Project (WFIP2), to improve the representation of boundary layer physics and related processes in mesoscale models for better treatment of scales applicable to wind and wind power forecasts. This goal challenges numerical weather prediction (NWP) models in complex terrain in large part because of inherent assumptions underlying their boundary layer parameterizations. The WFIP2 effort involved the wind industry, universities, the National Oceanographic and Atmospheric Administration

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Greenslade, Diana, Mark Hemer, Alex Babanin, Ryan Lowe, Ian Turner, Hannah Power, Ian Young, et al. "15 Priorities for Wind-Waves Research: An Australian Perspective." Bulletin of the American Meteorological Society 101, no. 4 (April 1, 2020): E446—E461. http://dx.doi.org/10.1175/bams-d-18-0262.1.

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Abstract The Australian marine research, industry, and stakeholder community has recently undertaken an extensive collaborative process to identify the highest national priorities for wind-waves research. This was undertaken under the auspices of the Forum for Operational Oceanography Surface Waves Working Group. The main steps in the process were first, soliciting possible research questions from the community via an online survey; second, reviewing the questions at a face-to-face workshop; and third, online ranking of the research questions by individuals. This process resulted in 15 identif

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Wang, Xi Bo, Ya Lin Lei, and Min Yao. "China's Thermal Power Generation Forecasting Based on Generalized Weng Model." Advanced Materials Research 960-961 (June 2014): 503–9. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.503.

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Since the 21st century, China's power industry has been developing very quickly, and the generated electrical energy has been growing rapidly. Although nuclear power, wind power, solar power generations have been increased, thermal power generation still accounts for more than 80% of the total generating capacity. Thermal power provides an important material basis for the development of the national economy. Therefore, the prediction research on China's thermal power generation trend is becoming a topic of great interest. The fuel of thermal power generation-coal, is an exhaustible resource. D

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Fogaing, Mireille B. Tadie, Arman Hemmati, Carlos F. Lange, and Brian A. Fleck. "Performance of Turbulence Models in Simulating Wind Loads on Photovoltaics Modules." Energies 12, no. 17 (August 26, 2019): 3290. http://dx.doi.org/10.3390/en12173290.

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The performance of five conventional turbulence models, commonly used in the wind industry, are examined in predicting the complex wake of an infinite span thin normal flat plate with large pressure gradients at Reynolds number of 1200. This body represents a large array of Photovoltaics modules, where two edges of the plate dominate the flow. This study provided a benchmark for capabilities of conventional turbulence models that are commonly used for wind forecasting in the wind energy industry. The results obtained from Reynolds Averaged Navier-Stokes (RANS) k - ε , Reynolds Normalization Gr

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Napoli, Christian, Francesco Bonanno, and Giacomo Capizzi. "An hybrid neuro-wavelet approach for long-term prediction of solar wind." Proceedings of the International Astronomical Union 6, S274 (September 2010): 153–55. http://dx.doi.org/10.1017/s174392131100679x.

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AbstractNowadays the interest for space weather and solar wind forecasting is increasing to become a main relevance problem especially for telecommunication industry, military, and for scientific research. At present the goal for weather forecasting reach the ultimate high ground of the cosmos where the environment can affect the technological instrumentation. Some interests then rise about the correct prediction of space events, like ionized turbulence in the ionosphere or impacts from the energetic particles in the Van Allen belts, then of the intensity and features of the solar wind and mag

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Wilczak, James, Cathy Finley, Jeff Freedman, Joel Cline, Laura Bianco, Joseph Olson, Irina Djalalova, et al. "The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs." Bulletin of the American Meteorological Society 96, no. 10 (October 1, 2015): 1699–718. http://dx.doi.org/10.1175/bams-d-14-00107.1.

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Abstract The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to b

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Song, Xinfu, Gang Liang, Changzu Li, and Weiwei Chen. "Electricity Consumption Prediction for Xinjiang Electric Energy Replacement." Mathematical Problems in Engineering 2019 (March 20, 2019): 1–11. http://dx.doi.org/10.1155/2019/3262591.

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In recent years, the phenomenon of wind and solar energy abandoned in Xinjiang’s new energy has become severe, the contradiction between the supply and demand of the power grid is obvious, and the proportion of power in the energy consumption structure is relatively low, thus hindering the development of Xinjiang’s green power. In this context, the focus of Xinjiang’s power has shifted to promote the development of electric energy replacement. Therefore, using the Xinjiang region as an example, we first select the important indicators such as the terminal energy substitution in Xinjiang, added

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Cifuentes, Jenny, Geovanny Marulanda, Antonio Bello, and Javier Reneses. "Air Temperature Forecasting Using Machine Learning Techniques: A Review." Energies 13, no. 16 (August 14, 2020): 4215. http://dx.doi.org/10.3390/en13164215.

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Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, a

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Huang, Yang, Gao, Jiang, and Dong. "A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression." Energies 12, no. 21 (November 1, 2019): 4181. http://dx.doi.org/10.3390/en12214181.

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Energy consumption issues are important factors concerning the achievement of sustainable social development and also have a significant impact on energy security, particularly for China whose energy structure is experiencing a transformation. Construction of an accurate and reliable prediction model for the volatility changes in energy consumption can provide valuable reference information for policy makers of the government and for the energy industry. In view of this, a novel improved model is developed in this article by integrating the modified state transition algorithm (MSTA) with the G

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El Hariri, Mohamad, Eric Harmon, Tarek Youssef, Mahmoud Saleh, Hany Habib, and Osama Mohammed. "The IEC 61850 Sampled Measured Values Protocol: Analysis, Threat Identification, and Feasibility of Using NN Forecasters to Detect Spoofed Packets." Energies 12, no. 19 (September 29, 2019): 3731. http://dx.doi.org/10.3390/en12193731.

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The operation of the smart grid is anticipated to rely profoundly on distributed microprocessor-based control. Therefore, interoperability standards are needed to address the heterogeneous nature of the smart grid data. Since the IEC 61850 emerged as a wide-spread interoperability standard widely accepted by the industry, the Sampled Measured Values method has been used to communicate digitized voltage and current measurements. Realizing that current and voltage measurements (i.e., feedback measurements) are necessary for reliable and secure noperation of the power grid, firstly, this manuscri

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Chernyakov, Mikhail, Olesya Usacheva, and Maria Chernyakova. "Impact of Digitalisation on Corporate Finance in the Agricultural Sector." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 15, no. 1 (May 17, 2021): 48–66. http://dx.doi.org/10.17323/j.jcfr.2073-0438.15.1.2021.48-66.

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The purpose of our paper is to examine the interrelation between digitalisation indicators of dairy industry government regulation and economic efficiency, using large corporations of Novosibirsk Region as an example. We propose to identify an integrated system approach to evaluating the influence of state programs related to digitalisation of the dairy industry on industry performance. A system-wide transition to digital technology in the infrastructure of dairy industry regulation is nearly totally absent from academic research. The existing literature considers the influence of state

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Klyuchnikova, E. M., L. G. Isaeva, A. V. Masloboev, T. E. Alieva, L. V. Ivanova, and G. N. Kharitonova. "Future narratives for key sectors of the economy of the Murmansk region in the context of global changes in the Arctic." Arctic: Ecology and Economy, no. 1(25) (March 2017): 19–31. http://dx.doi.org/10.25283/2223-4594-2017-1-19-31.

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This article presents forecast of the future development of the key industries of the Murmansk region under the climate change conditions, and developments that can be used as the background for discussing measures for adaptation to climate changes and producing long-term documents. We have revealed a wide range of scenarios to identify the uncertainties that the region will inevitably face and that should be taken into account when making decisions already now. We have used the forecasting method taking into account the two critical parameters: the climate change on the regional level and the

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Noskov, S. I., M. P. Bazilevskiy, and I. P. Vrublevskiy. "Assessment of the results of the medium-term forecast of railway performance." Herald of the Ural State University of Railway Transport, no. 1 (2020): 51–57. http://dx.doi.org/10.20291/2079-0392-2020-1-51-57.

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The results of the so-called descriptive forecast calculations of the future values of the key performance indicators of the transport industry are of considerable interest. The article presents a comparison of the results of the multivariate forecast of the performance indicators of the Krasnoyarsk Railway for 2015-2018, published in one of the previous works by the authors, with their actual values. These results were derived from a regressive mathematical model, which is an open dynamic recursive discrete system consisting of thirteen equations. The output variables in this model are the tr

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Letson, F., T. J. Shepherd, R. J. Barthelmie, and S. C. Pryor. "WRF Modeling of Deep Convection and Hail for Wind Power Applications." Journal of Applied Meteorology and Climatology 59, no. 10 (October 1, 2020): 1717–33. http://dx.doi.org/10.1175/jamc-d-20-0033.1.

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AbstractDeep convection and the related occurrence of hail, intense precipitation, and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading-edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the U.S. southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-d

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Ulazia, Alain, Gabriel Ibarra-Berastegi, Jon Sáenz, Sheila Carreno-Madinabeitia, and Santos J. González-Rojí. "Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula." Sustainability 11, no. 13 (July 2, 2019): 3648. http://dx.doi.org/10.3390/su11133648.

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A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed’s Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variati

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Skliris, Nikolaos, Robert Marsh, Meric Srokosz, Yevgeny Aksenov, Stefanie Rynders, and Nicolas Fournier. "Assessing Extreme Environmental Loads on Offshore Structures in the North Sea from High-Resolution Ocean Currents, Waves and Wind Forecasting." Journal of Marine Science and Engineering 9, no. 10 (September 24, 2021): 1052. http://dx.doi.org/10.3390/jmse9101052.

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The fast development of the offshore energy industry becomes an essential component of resilient economies in most of the countries around the North Sea, addressing an increasing demand for cost-efficient and environmentally safe energy sources. Offshore wind farms are planned to be installed further away from the coasts to ensure stronger and more stable wind resources in this region. Oil and gas extraction infrastructures are also planned to move into deeper areas of the continental shelf and continental shelf slopes to explore new fields. These deeper areas of the ocean are characterised by

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Rehan, R., M. Nehdi, and S. P. Simonovic. "Policy making for greening the concrete industry in Canada: a systems thinking approach." Canadian Journal of Civil Engineering 32, no. 1 (February 1, 2005): 99–113. http://dx.doi.org/10.1139/l04-086.

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Portland cement production in Canada increased from 1.7 million tonnes in 1946 to more than 13 million tonnes in 2002. Although the industry is a major player in meeting infrastructural needs of Canadians, it is also a major user of natural resources such as water, natural minerals, and aggregates. Moreover, for each tonne of cement clinker produced, 1 t of CO2 is released into the atmosphere. At the same time, Canada produces nearly 5 million tonnes of fly ash each year, and yet the use of fly ash as cement replacement remains dismally low at around 17%. It is believed that through product an

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Mitchell, Meghan J., Brian Ancell, Jared A. Lee, and Nicholas H. Smith. "Configuration of Statistical Postprocessing Techniques for Improved Low-Level Wind Speed Forecasts in West Texas." Weather and Forecasting 35, no. 1 (January 20, 2020): 129–47. http://dx.doi.org/10.1175/waf-d-18-0186.1.

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Abstract The wind energy industry needs accurate forecasts of wind speeds at turbine hub height and in the rotor layer to accurately predict power output from a wind farm. Current numerical weather prediction (NWP) models struggle to accurately predict low-level winds, partially due to systematic errors within the models due to deficiencies in physics parameterization schemes. These types of errors are addressed in this study with two statistical postprocessing techniques—model output statistics (MOS) and the analog ensemble (AnEn)—to understand the value of each technique in improving rotor-l

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Shirinov, A. Sh o. "Experience of Localizing Value Chains in the Automotive Industry." Economics and Management 27, no. 2 (May 1, 2021): 117–31. http://dx.doi.org/10.35854/1998-1627-2021-2-117-131.

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Aim. The presented study aims to illustrate that by making use of industrial localization tools, the one business space (OBS) system solves the problem of replacing global value chains with local ones, leading to positive innovation-oriented structural sectoral transformations and the development of the economy’s export-oriented potential.Tasks. The authors examine the types of localization and the strategy of an automotive company for the formation and operation of regional sectoral OBS systems over time (2012–2019) through the example of the petrochemical and metalworking industries in the R

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Buhr, Renko, Hassan Kassem, Gerald Steinfeld, Michael Alletto, Björn Witha, and Martin Dörenkämper. "A Multi-Point Meso–Micro Downscaling Method Including Atmospheric Stratification." Energies 14, no. 4 (February 23, 2021): 1191. http://dx.doi.org/10.3390/en14041191.

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In wind energy site assessment, one major challenge is to represent both the local characteristics as well as general representation of the wind climate on site. Micro-scale models (e.g., Reynolds-Averaged-Navier-Stokes (RANS)) excel in the former, while meso-scale models (e.g., Weather Research and Forecasting (WRF)) in the latter. This paper presents a fast approach for meso–micro downscaling to an industry-applicable computational fluid dynamics (CFD) modeling framework. The model independent postprocessing tool chain is applied using the New European Wind Atlas (NEWA) on the meso-scale and

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Houghton, Ronald C. C. "Aircraft Fuel Savings in Jet Streams by Maximising Features of Flight Mechanics and Navigation." Journal of Navigation 51, no. 3 (September 1998): 360–67. http://dx.doi.org/10.1017/s0373463398007966.

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Performance enhancement and cost reduction are driving forces in today's airline industry. In a world of cost pressures and escalating charges, research was conducted into better use of jet streams as a means of reducing costs. When operating on international airline routes, specific features of flight mechanics were adapted and tailored to fit a B747-200 aircraft, major emphasis being placed on intercepting, or avoiding where necessary, the high energy jet stream winds of the global weather system, adjusting flight profiles and modifying route structures. Operations were conducted both into w

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