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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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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 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.</p>
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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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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 accuracy from one to six hours ahead. These questions are investigated utilizing data from the FINO1 offshore platform and Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) C1 site, and testing results are compared to the persistence method. At FINO1, due to the presence of multiple wind farms and high inter-annual variability, RF is more effective as an error-correction mechanism for the persistence approach. The direct forecasting strategy is seen to slightly outperform the recursive strategy, specifically for forecasts three or more steps ahead. Finally, increased data availability (up to ∼8 equivalent years of hourly training data) appears to continually improve forecasting accuracy, although changing environmental flow patterns have the potential to negate such improvement. We hope that the findings of this study will assist future researchers and industry professionals to construct accurate, reliable RF models for wind speed forecasting.
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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 in a panel data framework. The data for this study were obtained from a number of sources and for a total of 24 counties in clusters in Northern, Western, and Southern Iowa over a period of ten years. Back-Propagation NN (BPNN) using a Quasi-Newton secondorder training algorithm was chosen for this study owing to its very fast convergence properties. Since the size of the training set is relatively small, ensembles of well-trained NNs were formed to achieve significant improvements in generalization performance. The developed NN forecasting models could identify the presence of biofuel plants and wind farms as well as large-truck traffic as the most sensitive inputs influencing pavement condition and granular and blading maintenance costs. Pavement deterioration resulting from traffic loads was found to be associated with the presence of both biofuel plants and wind farms. The developed NN forecasting models can be useful in identifying and properly evaluating future transportation infrastructure impacts resulting from the renewable energy industry development and thus help Iowa maintain its competitive edge in the rapidly developing bioeconomy.
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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 ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011–August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and three lidars, and to forecasts from the High-Resolution Rapid Refresh (HRRR), 3-km, and from the High-Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.
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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 utilized to extract the main components of the original wind speed data while eliminating noise. To make better use of the back-propagation artificial neural network, the initial parameters of the network are substituted with optimized ones, which are achieved by using the artificial fish swarm algorithm (AFSA), and the final combination model is employed to conduct wind speed forecasting. A series of data are collected from four different observation sites to test the validity of the proposed model. Through comprehensive comparison with the traditional models, the experiment results clearly indicate that the proposed hybrid model outperforms the traditional single models.
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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 into the second stage to forecast demand. Meanwhile a data selection process is presented due to the limited data of new products. The empirical data are from a Canadian fast-fashion company. The results reveal the relationship between demand and sales, demonstrate the necessity of integrating the demand estimation process into a forecasting system, and show that the ANFIS-based forecasting system outperforms the traditional ANN technique.
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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 for future references. The project even consists of an app that sends notifications as an effective alert system to warn people about sudden and drastic weather changes. For predicting more complex weather forecast that can’t be done by sensors alone we use an API that analyses the data collected by the sensors and predicts an accurate outcome. This API can be used to access the data anywhere and at any time with relative ease and can also be used to store data for future use. Due to the compact design and fewer moving parts this design requires less maintenance. The components in this project don’t consume much power and can even be powered by solar panels. Compared to other devices that are available in the market the Smart weather monitoring system is cheaper and cost effective. This project can be of great use to meteorological departments, weather stations, aviation and marine industries and even the agricultural industry.
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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 predictive performance approach is novel to wind industry, it is essential to establish a viable research road map. This paper proposes a data-mining-based methodology for long term wind forecasting (ANN), which is suitable to deal with large real databases. The paper includes a case study based on a real database of five years of wind speed data for a site and discusses results of wind power density was determined by using the Weibull and Rayleigh probability density functions. Wind speed predicted using wind speed data with Datamining methodology using intelligent technology as Artificial Neural Networks (ANN) and a PROLOG program designed to calculate the monthly mean wind speed.
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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 vector regression (SVR) model that is coupled with brainstorm optimization (BSO) and cuckoo search (CS) algorithms, which are successfully utilized for parameter determination. The proposed hybrid models were used to forecast short-term wind speeds collected from four wind turbines located on a wind farm in China. The forecasting results demonstrate that the intelligent hybrid models outperform single models for short-term wind speed forecasting, which mainly results from the superiority of BSO and CS for parameter optimization.
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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 approaches. This paper presents a linear model for price forecasting—based on electricity consumption, thermal power production, wind production and previous electricity prices—to estimate long-term electricity prices in electricity markets with a high wind penetration levels, to help utilities and asset owners to develop risk management strategies and for asset valuation.
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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 compared with those of the conventional AutoRegressive Moving-Average (ARMA) model and the less conventional ARMA-GARCH model. It is found that the proposed mixture GARCH model outperforms the other two models in terms of volatility modeling for all the energy related time series considered. This is proven to be statistically significant because the p-values of likelihood ratio test are less than 0.0001. On the other hand, in terms of estimations of mean wind speed, mean wind power output, and mean electricity price, no significant improvement from the proposed model is obtained. The results indicate that the proposed finite mixture GARCH model is a viable approach for mitigating the associated risk in energy related predictions thanks to the reduced errors on volatility modeling.
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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 hard to be predicted, a well built and tested forecasting algorithm must be provided. To accomplish this goal, four neural network-based algorithms: artificial neural network (ANN), convolutional neural network (CNN), long short-term memory (LSTM), and a hybrid model convolutional LSTM (ConvLSTM) that combines LSTM with CNN, and one support vector machine (SVM) model are investigated, evaluated, and compared using different statistical and time indicators to assure that the final model meets the goal that is built for. Results show that even though SVM delivered the most accurate predictions, ConvLSTM was chosen due to its less computational efforts as well as high prediction accuracy.
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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, remote monitoring and so on. This research can be used to demonstrate experimental teaching in high shool and train power enterprise technicians.
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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 state in Iran's electricity in a few days on the basis of regression multi linear model. This forecasting which was done during a three-day period is and categorized weekdays into three groups including working days, weekends, and holidays was carried out in an hourly manner. This model regardless of parameters like humidity, wind velocity, daylight time, etc. by minimizing the forecasting error managed to maximize the reliability of the results as well as the safety potential of the system. In this model the only influential parameter on the forecasting was the reliance of the forecasting day on previous days. The main purpose of the present study was to maximize the accuracy and reliability of forecasting for certain days (religious holidays, national holidays …). In this paper, the authors managed to decrease the error of forecasting for particular and regular off days to a great extent.
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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 penetration of wind energy continues to grow, the participation of wind turbines and wind farms in grid frequency stability is becoming more important. The future of wind energy development and deployment depends on many factors, such as policy decisions, economic markets, and technology improvements. Improvements through research and development in areas such as forecasting, turbine manufacturing processes, blade aerodynamics, power electronics, and active power control systems will continue to be a key driver for wind energy technology.
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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 to actual worm outbreak statistics, in understanding the macroscopic behavior of the worm, the worm flow forecasting, transmission speed and damage have a wide range of applications.
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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 nuclear and fossil power plants. Additionally, we augment the model by seasonal and autoregressive components to improve the nowcasting performance. We consider multiple estimation techniques based on the lassoand ridge and study the impact of the choice of the training/calibration period. The methodology is applied to a European TSO dataset from 2014 to 2019. The overall results show that in comparison to the industry benchmark, an accuracy improvement in terms of MAE and RMSE of about 60% is achieved. The best model is based on the ridge estimator and uses a specific non-standard shrinkage target. Due to the linear model structure, we can easily interpret the model output.
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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 of the study is to find the factors that are useful in forecasting cinema attendance at the individual show level (i.e., the number of tickets sold for a particular movie, time and cinema).Methods/Approach: We apply several linear regression models, estimated for each recursive sample, to produce one-week ahead forecasts of the attendance. We then rank the models based on the out-of-sample fit.Results: The results show that the best performing models are those that include cinema- and region-specific variables, in addition to movie parameters (e.g., genre, age classification) or title popularity.Conclusions: Regression models using a wide set of variables (cinema- and region-specific variables, movie features, title popularity) may be successfully applied for predicting individual cinema shows attendance in Poland.
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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 properties of the enterprise profit process, in particular the auto-regression component of revenue dependence on its value last year, seasonal quarterly dependence on sales and marketing costs, product price, etc. The detected patterns will allow us to take into account these features for forecasting future revenues and for adjusting the enterprise’s decision-making system taking into account seasonal features and results of the previous year.
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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 initially funded the Institute’s research on surge forecasting to improve the accuracy of tidal predictions. After a flood in 1928, however, the focus shifted to flood forecasting. Local government then backed their work, during the Second World War support came from the Royal Navy, and since a flood in 1953, from central government. This case study reveals the range of negotiations carried out between patrons and researchers, and demonstrates how researchers managed competing demands from academic interests and those of industry, the navy, and the government. Studying institutions that did not see a dramatic increase in state patronage during the early Cold War enables us to see the impact of patronage more clearly, highlighting how research interests and methods differed (or not) between institutions with different patronage structures.
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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 (NOAA), and the DOE’s national laboratories in an integrated observational and modeling study. Observations spanned 18 months to assure a full annual cycle of continuously recorded observations from remote sensing and in situ measurement systems. The study area comprised the Columbia basin of eastern Washington and Oregon, containing more than 6 GW of installed wind capacity. Nests of observational systems captured important atmospheric scales from mesoscale to NWP subgrid scale. Model improvements targeted NOAA’s High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to National Weather Service (NWS) operational forecast models, and these modifications have already yielded quantitative improvements for the short-term operational forecasts. This paper describes the general WFIP2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailed BAMS papers addressing WFIP2 observational science, modeling challenges and solutions, incorporation of forecasting uncertainty into decision support tools for the wind industry, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.
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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 identified priorities, covering research activities and the development of infrastructure. The top five priorities are 1) enhanced and updated nearshore and coastal bathymetry; 2) improved understanding of extreme sea states; 3) maintain and enhance the in situ buoy network; 4) improved data access and sharing; and 5) ensemble and probabilistic wave modeling and forecasting. In this paper, each of the 15 priorities is discussed in detail, providing insight into why each priority is important, and the current state of the art, both nationally and internationally, where relevant. While this process has been driven by Australian needs, it is likely that the results will be relevant to other marine-focused nations.
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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. Due to the exhaustible constraints the fuel, thermal power generation trend is bound to show a similar trend bell curve as the coal production trend, similar to a bell-shaped curve—a gradual increase to maximum output and then a short peak and a gradual decline. To get more accurate results of future thermal power generation, this paper applies the generalized Weng model to forecast China's thermal power generation peak and trend. The result indicted that the peak of China's thermal power generation appears in 2022 with generating capacity of 51,702 TWh. The generating capacity of thermal power will decrease gradually after 2022. Based on the results, the paper proposes some policy recommendations for the sustainable development of China's electrical energy. China should decrease the percentage of the capacity which comes from thermal generation and reduce the dependence on thermal power generation. Moreover, nuclear, hydraulic, wind and solar power should be developed before the thermal power generation peak.
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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 Group (RNG) k - ε , RANS k - ω Shear Stress Transport (SST) and Reynolds Stress Model (RSM) were compared with existing Direct Numerical Simulations (DNS). The mean flow features and unsteady wake characteristics were used as testing criteria amongst these models. All turbulence models over-predicted the mean recirculation length and under-predicted the mean drag coefficient. The major differences between numerical results in predicting the mean recirculation length, mean drag and velocity gradients, leading to deficits in turbulence kinetic energy production and diffusion, hint at major difficulties in modeling velocity gradients and thus turbulence energy transport terms, by traditional turbulence models. Unsteadiness of flow physics and nature of eddy viscosity approximations are potential reasons. This hints at the deficiencies of these models to predict complex flows with large pressure gradients, which are commonly observed in wind and solar farms. The under-prediction of wind loads on PV modules and over-estimation of the recirculation length behind them significantly affects the efficiency and operational feasibility of solar energy systems.
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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 magnetospheric response. The problem of data prediction can be faced using hybrid computation methods so as wavelet decomposition and recurrent neural networks (RNNs). Wavelet analysis was used in order to reduce the data redundancies so obtaining representation which can express their intrinsic structure. The main advantage of the wavelet use is the ability to pack the energy of a signal, and in turn the relevant carried informations, in few significant uncoupled coefficients. Neural networks (NNs) are a promising technique to exploit the complexity of non-linear data correlation. To obtain a correct prediction of solar wind an RNN was designed starting on the data series. As reported in literature, because of the temporal memory of the data an Adaptative Amplitude Real Time Recurrent Learning algorithm was used for a full connected RNN with temporal delays. The inputs for the RNN were given by the set of coefficients coming from the biorthogonal wavelet decomposition of the solar wind velocity time series. The experimental data were collected during the NASA mission WIND. It is a spin stabilized spacecraft launched in 1994 in a halo orbit around the L1 point. The data are provided by the SWE, a subsystem of the main craft designed to measure the flux of thermal protons and positive ions.
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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 be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.
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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 value of the secondary industry, population, terminal power consumption intensity, and per capita disposable income. Subsequently, eight combined forecasting models based on the grey model (GM), multiple linear regression (MLR), and error back propagation neural network (BP) are constructed to predict and analyse the electricity consumption of the whole society in Xinjiang. The results indicate the optimal weighted combination forecasting model, GM-MLR-BP of the induced ordered weighted harmonic averaging operator (IOWHA operator), exhibits better prediction accuracy, and the effectiveness of the proposed method is proven.
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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, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.
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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 Gaussian processes regression (GPR) approach for non-fossil energy consumption predictions for China at the end of the 13th Five-Year Project, in which the MSTA is utilized for effective optimization of hyper-parameters in GPR. Aiming for validating the superiority of MSTA, several comparisons are conducted on two well-known functions and the optimization results show the effectiveness of modification in the state transition algorithm (STA). Then, based on the latest statistical renewable energy consumption data, the MSTA-GPR model is utilized to generate consumption predictions for overall renewable energy and each single renewable energy source, including hydropower, wind, solar, geothermal, biomass and other energies, respectively. The forecasting results reveal that the proposed improved GPR can promote the forecasting ability of basic GPR and obtain the best prediction effect among all the other comparison models. Finally, combined with the forecasting results, the trend of each renewable energy source is analyzed.
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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 manuscript provides a detailed analysis of the Sampled Measured Values protocol emphasizing its advantages, then, it identifies vulnerabilities in this protocol and explains the cyber threats associated to these vulnerabilities. Secondly, current efforts to mitigate these vulnerabilities are outlined and the feasibility of using neural network forecasters to detect spoofed sampled values is investigated. It was shown that although such forecasters have high spoofed data detection accuracy, they are prone to the accumulation of forecasting error. Accordingly, this paper also proposes an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed methods is experimentally verified in a laboratory-scale smart grid testbed.
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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 programs and policies on the industry and proposes various performance indicators. However, it is uncertain how industry digitalisation may affect these performance indicators.
To address this gap in the literature, we propose a hypothesis of dependency between digitalisation indicators and performance indicators of dairy corporations. The basis of the methodology is the calculation of a digitalisation index used to assess the efficiency of government support of the industry corporations. In order to substantiate the hypothesis, we apply a correlation and regression analysis and established interrelations between the offered criteria (digitalisation index and share) and operating performance of dairy industry economic entities.
Our results indicate general consistent patterns and interrelations between digitalisation of state regulatory programs and the performance of dairy industry corporations. Our statistical analysis reveals digital technology as a tool of government has a significant impact on business performance. The offered digitalisation criteria and patterns of performance efficiency are indicative of the possibility to manage the digitalisation process based upon preset parameters of business performance.
Our research will be of interest to specialists developing state programs and policies applying digital technology, directors of dairy companies, and scientists who conduct research in related fields, who may use our approach for evaluating and forecasting performance in the dairy industry, accounting for the impact of government regulation.
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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 global trends in the socio-economic development. The narratives from the Shared Socioeconomic Pathways (SSPs) have been used as boundary conditions for creating scenarios of Murmansk region development. The local experts - representatives of industries, regional and local authorities, non-governmental and scientific organizations were involved in the forecasting process. The foresight research methodology was chosen because it is more than a long-term and strategic planning and forecasting corresponds to the social progress, in particular, the society democratization in its main areas: engaging citizens to managing the state affairs and creating conditions for manifestation of their initiatives. As a result, the issues of forecasting the future trends and challenges in the key sectors of the economy of the Arctic under the changing climate, depending on the forecast global development trends were considered. The necessity of using a structured, coherent to the global trends approach to working out regional and corporate development strategies is substantiated. On the example of the Murmansk region, the possible scenarios of development of the mining industry, and energy and human potentials depending on the global changes, including the climate change are considered.
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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 transport freight turnover, the locomotive productivity, the sector speed, the average weight of the freight train, the local car demurrage, the demurrage at the technical station, and loading volumes. In total, pessimistic, neutral and optimistic versions of the forecast have been developed. The comparison of the actual (real) and forecasted (estimated) values of the endogenous (internal, output, dependent, explicable) variables showed that 23 of the 28 values fell into the forecast ranges, which makes it possible to conclude that the model is highly adequate and can be used effectively for a wide range of mid-term forecasting tasks, connected with mid-term forecasting. Some ways of enhancing the predictive capability of the model are proposed.
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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-day Weather Research and Forecasting (WRF) Model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modeling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements, and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates, and precipitation totals show good agreement with observations. The occurrence of precipitation during power-producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modeled with a mean proportion correct of 0.77 and an odds ratio of 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry, and there is evidence for positive model bias in cloud reflectivity.
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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 variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes.
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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 harsh environmental conditions: stronger winds, larger waves and strong shelf slope currents, inducing considerably larger loads on offshore structures. This study brings together operational physical oceanography and the mathematics of fluid-structure interactions to estimate the likelihood of extreme environmental loads on offshore structures in the North Sea. We use the state-of-the-art Met Office high resolution ocean forecasting system, which provides high-frequency data on ocean and tidal currents, wave heights and periods and winds at a ~7 km horizontal resolution grid, spanning the North–West European Shelf. The Morison equation framework is used to calculate environmental loads on various types of offshore structures that are typically employed by the offshore industries in the North Sea. We use hourly data for a 2-year period to analyse the spatio-temporal variability of mean and extreme hydrodynamic loads and derive the relative contributions of currents, waves and winds in the region. The results indicate that waves dominate extreme hydrodynamic forces on the shallow shelf, whereas the current contribution is important at the shelf break and in the English Channel.
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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 and process innovations, more fly ash can be used in concrete, thus preserving natural resources, reducing CO2 emissions, and helping Canada meet the requirements of the Kyoto Protocol. Forecasting the future impact of using fly ash in concrete has been based on qualitative and linear estimates, however, without accounting for the complexity of the problem and its dynamic feedbacks. In this paper, a novel application of system dynamics modeling, a feedback-based object-oriented modeling paradigm, is proposed to create a rational model that departs from current approaches used in modeling CO2 emissions of cement production. The model accounts for the various enablers and barriers for using fly ash in concrete, including market dynamics and technology development. It allows the user to test a wide variety of scenarios and policies, its flexible architecture permits coupling it with general economic or service life models, and its modular nature allows expanding its boundaries to include other facets of the holistic CO2 emissions problem in Canada.Key words: concrete, blended cements, CO2 emission, global warming, sustainable development, system dynamics, modeling.
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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-layer wind forecasts. This study is unique in that it compares the techniques using a sonic detection and ranging (SODAR) wind speed dataset that spans the entire turbine rotor layer. This study uses reforecasts from the Weather Research and Forecasting (WRF) Model and observations in west Texas over periods of up to two years to examine the skill added to forecasts when applying both MOS and the AnEn. Different aspects of the techniques are tested, including model horizontal and vertical resolution, number of predictors, and training set length. Both MOS and the AnEn are applied to several levels representing heights in the turbine rotor layer (40, 60, 80, 100, and 120 m). This study demonstrates the degree of improvement that different configurations of each technique provides to raw WRF forecasts, to help guide their use for low-level wind speed forecasts. It was found that both AnEn and MOS show significant improvement over the raw WRF forecasts, but the two methods do not differ significantly from each other.
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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 Republic of Tatarstan, showing the possibility of transforming industrial localization into import substitution.Methods. This study uses modern achievements of the theory of knowledge, innovation-driven development, methods of systematization and analysis, empirical research, including the collection, examination, and generalization of practical material, forecasting, analysis of the works of Russian and foreign scientists in the field of technological and non-technological activities, principles of change management.Results. By using industrial localization tools for the formation of multiple value chains in the host region within the framework of a partnership, it is possible to create an industry-wide vertically integrated management system for the innovation-driven development of automotive components production at the regional level. Horizontal integration into the social, scientific, and educational spheres of regions is a mandatory aspect for the development of such systems, which leads, among other things, to the development of scientific potential while also improving the quality of human capital. Within the framework of OBS, regional value chains are formed — from the extraction of raw materials to the final assembly of the car — with a total average annual turnover of 25 billion rubles. A significant number of jobs are created, as well as new automotive components and materials with unique properties for the Russian industry. This facilitates the production of Russian analogues in the interests of other OEMs and consumers, as well as the transformation of industrial localization into import substitution. In addition, products of localization are beginning to replace their analogues in the global supply chains of automotive components, thereby developing the export-oriented component of the regional economy.Conclusions. In certain cases, industrial localization in OBS systems is transformed into import substitution, significantly increasing the efficiency of industrialization in the region and the country as a whole.
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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 THETA on the micro-scale side. We adapt on a previously developed methodology and extend it using a micro-scale model including stratification. We compare a single- and multi-point downscaling in critical flow situations and proof the concept on long-term mast data at Rödeser Berg in central Germany. In the longterm analysis, in respect to the pure meso-scale results, the statistical bias can be reduced up to 45% with a single-point downscaling and up to 107% (overcorrection of 7%) with a multi-point downscaling. We conclude that single-point downscaling is vital to combine meso-scale wind climate and micro-scale accuracy. The multi-point downscaling is further capable to include wind shear or veer from the meso-scale model into the downscaled velocity field. This adds both, accuracy and robustness, by minimal computational cost. The new introduction of stratification in the micro-scale model provides a marginal difference for the selected stability conditions, but gives a prospect on handling stratification in wind energy site assessment for future applications.
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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 wind and down wind, over a period of five years. Techniques employed show fuel may be saved regardless of the wind being a tailwind or headwind. Both fuel and time have a significant bearing on airline direct operating costs: savings of more than 1·1 percent being made on fuel and 0·786 percent on time. Limitations on using the techniques to gain maximum benefit are related to the high volume of aircraft blocking all major airways, and better quality, real time weather forecasts. The discussion looks at ways of improving the use of jet streams, as the world's airline traffic continues to grow. Forecasting upper winds, particularly in oceanic areas, needs to improve if airlines are to derive maximum benefits from these winds. There is need for further study utilising other aircraft types to ascertain what savings can result. Initial results were encouraging, using a Tristar L1011 aircraft.