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The present paper bridges mathematical modeling and wind resource assessment (WRA). Sensitivity analysis (SA) links portions of output variance to the variance in each input variable. Global SA (GSA) explores inputs globally. One-at-a-time (OAT) SA is dominating in WRA, while GSA is often overlooked. Compared to traditional methods GSA offers poten...
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... importance of accounting for the variance in air density in WRA was pointed out by Jung and Schindler in Ref. 66. Air density was calculated according to Eq. (23) with the collected data of temperature and atmospheric pressure at Masdar City. The Empirical cumulative distribution function (ECDF) of air density (Fig. 7) based on the calculated values was used for distributional modeling of air density. For alternative ways to account for the variability in air density in WRA, refer to Ref. ...
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... Teachers' immense work engagement, intrinsic desire to work, and intense passion for deep involvement for long hours make them expert, experienced, and professional (Stoeber & Damian, 2016). These excellences in work lead to occupational professional excellence (Tsvetkova & Ouarda, 2019). Therefore, there is a need to find a strong link between hard work and technical proficiency, and emphasis should be placed on upgrading the quality of education, making the teaching-learning process effective, taking measures to protect the employment of secondary-level teachers, and evaluating the impact of teachers' commitment with their golden performance (Aziz et al., 2013). ...
The current study was designed to identify the impact of workaholism on teachers' occupational professionalism that influences the teaching-learning process of secondary school teachers to equip them professionally. The major objectives of the study were to explore the impact of workaholism on teachers' occupational professionalism at the secondary school level. All the teachers teaching in secondary and higher secondary schools in tehsil Sargodha were the population of the study. Teachers were selected through multistage random sampling. From 190 schools, 100% of teachers from 20 % of schools were selected, and the sample size was 691. Questionnaires were developed for secondary school teachers' responses & validated through expert opinion. Pilot testing was done, and the reliability coefficient Cronbach Alpha values for workaholism and teacher's occupational professionalism were 0.729 and 0.850, respectively. The collected data were analyzed by using frequencies, percentages, and mean scores. Pearson correlation revealed a moderate positive relationship between workaholism and occupational professionalism of secondary school teachers, which means workaholism enhances teachers' occupational professionalism. It is concluded that if some of the school work needs to be done immediately, it puts a heavy burden on teachers' minds, so it is recommended that after assigning the task to teachers' the school principal should give them some time to complete it or maybe in group work so they can complete school work through mutual consultation & collaboration in this regards they can improve their efficiencies to make proficient.
... In this research paper, the application of the HDF [64] was introduced to model mean wind speed. Long-term measured wind speed data sets of 126 stations in the East of Canada were examined in the case study. ...
We introduce the family of three-parameter heavy-tailed distributions, the Halphen distribution family (HDF), to model the mean wind speed for the purpose of wind energy estimation. The HDF has a number of properties favorable to model wind speed data, such as lower bound at zero (absence of location parameter), flexibility to cover a large range of shapes, and an explicit form of moment generating functions. We examined 126 stations in Eastern Canada (125 stations were heavy-tailed with positive excess kurtosis) and found that HDF provides fit superior to the most commonly used distribution for this purpose, the two-parameter Weibull distribution, in 100% of the stations according to the Akaike information criterion (AIC). HDF was compared against 4 two-parameter models (Gaussian, Weibull, Gamma, and inverse Gamma) and 3 three-parameter models (generalized extreme value, generalized Gamma and Burr). The most common best-fit distribution for the stations in the Eastern Canada case study is the HDF (46% according to AIC). The results of the case study show that wind observations cannot be fit by the Halphen inverse type B, but can be modelled by Halphen A and especially well by Halphen B (minimum of Kolmogorov-Smirnov statistic among candidate distributions). 87 stations exhibited class D tail behavior. No correlation between tail behavior class and best-fit distribution was observed. We encourage the use of Halphen A and Halphen B as candidate distributions for wind resource estimation studies that are based on mean wind speed estimation.
... Low-discrepancy samples not only are exploratory in the ED space but also capture the performance function locally. They have been shown before to perform as global experimental designs in different contexts of application (Tsvetkova & Ouarda, 2019). LHS can also be also implemented as a sequence with refinement of the variable density partitions at additional complexity in their implementation (Blatman & Sudret, 2010). ...
Different emerging threats highlighted the relevance of recovery and adaptation modelling in the functioning of societal systems. However, as modelling of systems becomes more complex, its effort increases challenging the practicality of the engineering analyses required for efficient recovery and adaptation. In the present work, metamodels are researched as a tool to enable these analyses in traffic networks. One of the main advantages of metamodeling is their synergy with the short decision times required in recovery and adaptation. A sequential global metamodeling technique is proposed and applied to three macroscopic day-to-day user-equilibrium models. Two reference contexts of application are researched: optimal recovery to a perturbation (with response times reduced by 98% with loss of accuracy lower than 1%) and adaptation under uncertainty with perturbation-dependent optimality. Results show that metamodeling-based metaheuristics enable fast resource-intensive engineering analyses of traffic recovery and adaptation, which may change the paradigm of decision-making in this field.
... For Baş, [19] the main disadvantage of RSM is to adjust the data to a secondorder polynomial. With this, other sampling and modeling techniques such as, SOBOL, [20] Monte Carlo, quasi-Monte Carlo sampling, Latin hypercube design, [21] ; multi-objective optimization, [22] robust response surfaces, [23] artificial neural network, [24] and Kriging, can be used. [25] Despite this, Montgomery [16] presents the desirability function as a helpful approach for optimizing multiple responses. ...
Inconel 718 superalloys are known materials with high applicability to components that demand excep-tional chemical and mechanical properties at higher working temperatures combined with excellent surface finish requirements. However, due to the lack of in-depth knowledge regarding its machining process, the material is considered a difficult-to-machine material. Therefore, to improve the machining process of the Inconel 718 superalloy, by finding the optimal processing conditions, it was necessary to analyze the influence of the cutting parameters on the workpiece surface quality. In this work, the evaluation of quality indicators was made through the influence analysis of cutting speed, feed, tool, and lubrication parameters. The surface roughness (Ra, Rt), consumed power, vibration, and tool wear indicators were evaluated by following a Taguchi experimental design. To perform the machining process, the tool CNMG120408-M1 coating TS2500 with minimum quantity of lubricant was used. Results have proven that feed rate adjusted to 0.10 mm/rev and the cutting speed of 70 m/min provided the optimal processing results. It was observed that the feed increase led to an increase of all indicators. Consumed power denoted high sensibility concerning the machining parameters, suggesting its potential use in tool wear prediction studies applied for the Inconel 718 machining.
... SA in WRA is no exception. Tsvetkova et al. [13] stated that OATSA is typical for WRA (evidence required) and voiced concern about the validity of OATSA for nonlinear models (nonLMs) in WRA. However, are nonLMs typical for WRA? ...
... However, are nonLMs typical for WRA? The review intends to provide evidence of SA pitfalls present in WRA [13], OATSA and nonLMs prevalence in WRA. Reviews of UA in WRA exist in the literature, e.g. ...
... There are four significant clusters of ongoing research in neighbouring topics outside the review's scope: (1) SA of wind turbine fatigue loads (WTFLs), (2) SA of weather research and forecasting models (WRFM's), (3) SA of hybrid energy systems (HES's) with a wind component, and (4) SA used in wind farm (WF) layout optimization [16]. SA of WTFL tends to use universal GSA, and of WRFMs -OATSA [13]. SA of HES's mostly employs OATSA [17]. ...
A review of sensitivity analysis in wind resource assessment is presented, offering classifications by sensitivity analysis output variable (or model), method, application, country, and software. No review of sensitivity analysis in wind resource assessment is currently available in the literature. The review pool consists of 102 articles with models dealing with statistical and economic aspects of wind resource assessment (goodness-of-fit metrics, wind power, wind energy, the net present value, the payback period, the internal rate of return, the payback period, the levelized cost of energy, capital and operational expenses). Sensitivity analysis studies, where the wind is predicted with weather research and forecasting models, sensitivity analysis of hybrid energy systems with a wind component, and sensitivity analysis of wind turbine fatigue loads, are beyond the scope of this review. This review reveals the lack of collective agreement on the definition of sensitivity analysis in the literature, the dominance of nonlinear models (100%), and the prevalence of one-at-a-time sensitivity analysis method (82%). The review highlights the existing gaps in the field, provides evidence of the common pitfalls, possibly leading to costly misinterpretations of the data at the site and hence to erroneous feasibility assessments. The review urges to rethink how to conduct sensitivity analysis in wind resource assessment. It also includes comparison of one-at-a-time sensitivity analysis and global sensitivity analysis for a linear and nonlinear models.
This research paper investigates the factors influencing wind energy adoption and sustainability in the United Arab Emirates (UAE). The investigation is motivated by a research gap, as there is limited existing research on the specific factors influencing the adoption of wind energy in the UAE. Understanding these factors is identified as a crucial problem for the successful transition to sustainable energy sources in the nation. Employing a systematic review methodology, data spanning from 1999 to 2023 across six databases were collected using specified keywords. A total of 203 articles were screened, resulting in 64 selected articles. The study categorizes derived factors into technological innovation, environmental impact, grid integration, and socioeconomic aspects. Utilizing a structured methodology involving questionnaire surveys, structural equation modeling (SEM), and multiple linear regression (MLR) analyses. 75% of hypotheses were accepted, affirming significant positive relationships between technological innovation, environmental impact, grid integration, wind energy adoption, and sustainability were confirmed. While socioeconomic factors exhibit a nuanced influence, the findings underscore the UAE's remarkable progress in harnessing wind energy for a sustainable future. These findings underscore the importance of further investigating these factors and sub-factors for successfully transitioning to sustainable energy sources in the UAE.
During the actual use stage, the uncertainty of weather leads to errors between the performance of building energy and the simulated values. Usually, uncertainty of weather cannot be eliminated, and difficult to quantify accurately due to complex occurrence rules. Based on the influence of weather uncertainty on solar power generation, this research propose a highly reliable and high-efficiency uncertainty modeling method based on two neural networks: (i) dual-stage attention-based recurrent neural network(DARNN): due to the use of two-stage attention mechanism, as the method to achieve importance interpretability in this study. (ii) Bayesian recurrent neural network(Bayesian RNN): regard the weight and bias value as an unknown distribution, as the method of uncertainty modeling in this study. DARNN analyzes the weight value of each weather element at each time point as the importance through a single learning, and the results show that the importance of solar radiation and relative humidity to solar power generation ranks in the top two. Based on the above results, Bayesian RNN establishes the probability distribution models of solar radiation and relative humidity at each time point of a year by learning the potential correlation between weather elements and taking the historical data of low importance weather elements as input.
The assessment of wind energy potential is generally based on the analysis of the statistical distribution of observed wind speed of short time resolution. Record periods of observational data used in practice at sites of interest are often very short, often ranging from a few months to a few years. Predictions based on such small record periods are likely to be biased as it is recognized that wind speed is subject to important interannual variability and long-term trends. Large-scale atmospheric circulation patterns have an important influence on wind speed. Their predictable nature can make them useful for the prediction of wind speed during the lifetime of wind farm projects. This feature is not exploited in practice. It is proposed in this study to introduce predictors of the wind speed in non-stationary statistical models. This approach allows the development of predictions of the wind speed distribution conditionally on the state of the predictors. The predictors used here are indices of atmospheric circulation to account for the interannual variability and a temporal index to account for the long-term temporal trend. The proposed approach was applied to hourly wind speed data at selected meteorological stations in the province of Québec (Canada). 20 stations with long record periods of over 30 years of data were used. The most important circulation indices identified in the study area are the North-Atlantic Oscillation (NAO) during the winter season and the Pacific North American (PNA) during the spring season. Results indicate that the annual goodness-of-fit at the stations of the case study improved on average when the non-stationary model is used compared to the stationary model. The proposed approach can potentially be used to model wind speed during the projected lifetime of wind farms using forecasts of the predictors.
The authors regret that in the above-mentioned article, the expression that appears twice in the paper, first in Eq. (25) and then in Table 2, is incorrect. The correct form of the expression in Eq. (25) is:
AEP = (100-EL)/100 * a/2 * ρ * λ^3 * Г((c+3)/c) * 8760 * A_swept,
Where A_swept is the swept area of the wind turbine. A_swept is a constant for a given wind turbine configuration.
Results of the UAE case study consisted of global sensitivity analysis (Figure 14) and uncertainty analysis (Figure 15). Figure 14 remains intact and the values in Figure 15 should be multiplied by the value of the swept area of a chosen wind turbine. This corrigendum does not change the overall results of the work and the conclusions of the original paper.
