Influence of ambient conditions on wind speed measurement: Impact on the annual energy production assessment

Guerrero-Villar, F.; Dorado-Vicente, R.; Fike, Matej; Cruz, Francisco J. Gómez-de la

doi:10.1016/j.enconman.2019.05.067

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…In practical applications, when solving a problem, it is necessary to fully perform several other tasks at the same time, such as predicting the final valuation of stocks and futures, which first needs to be analyzed and summarized. Record the historical discussion of a period, and then formulate an accurate planning model on this basis, make scientific and accurate predictions for the future, formulate possible ideas, make effective decisions and management, and achieve the purpose of reducing losses and costs [11][12].…”

Section: Gray Systemmentioning

confidence: 99%

The Output Value Prediction Model of Regional Fitness and Leisure Industry

2022

IJFS

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The fitness and leisure industry is a part of the sports industry. The sports industry is a huge industry group, such as sports equipment manufacturing, sports events, etc. The fitness and leisure industry is an industry that caters to the public with the continuous increase of people's living income. The purpose of this paper is to study the construction and demonstration of the output value forecasting model of the regional fitness and leisure industry. And use the GM model (1, N) to make a gray forecast model for each year's GDP, and judge the rationality of the model by comparing it with the data of previous years. Forecast GDP from 2017 to 2022. By comparing and analyzing the results obtained by the two forecasting methods, it is concluded that the forecasting results of the two forecasting methods have high accuracy, and the fluctuation of the forecast error of the structure value is relatively small. However, the gray system is simpler than regression analysis. The average relative error of the output value prediction of the fitness and leisure industry in the gray system area is 2.104%. It can be seen that the gray system prediction method has great application value in structural prediction.

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Section: Gray Systemmentioning

confidence: 99%