This paper presents and compares two different methods using in the FORECASTing of wind power turbine (WPT) outputs. These two FORECASTing methods, which utilize different types of input to FORECAST the output of WPT, are the Meteorology FORECASTing Method (MFM) and the Observational FORECASTing Method (OFM). The MFM determines the unit output from the FORECASTed wind speed at the WPT installation site, using the input from a composite data set created from the original annual-hourly weather data. Three different techniques can be used in MFM to FORECAST the wind speed, and the best result is selected for conversion calculation of the output of WPT. OFM, however, FORECASTs the unit output based on five observed annual-hourly data obtained from the operation of target WPT. Two different techniques can be used in the OFM simulation. The results from these techniques for each method are compared and the best one will be used for the final FORECAST of the WPT outputs. This paper presents and compares the FORECASTing results of WPT output obtained from MFM and OFM. Furthermore, in order to increase the result precision and decrease the FORECAST error, a new composite data system is also developed and proposed.The methodologies proposed in this paper will be very useful for designers, planners and operators of the wind power turbines

Aware of the economic performance of the business in the future will help potential investors and Creditors in economic decisions. Investment decision taken on the basis of FORECASTed earnings. Therefore the accuracy of these predictions is important. Prior research has shown that cost FORECAST error is more than sales FORECAST error and has more effect on the earning FORECAST error and claimed that this error is due to ignoring the cost behavior. This research was investigated cost behavior FORECAST accuracy by studying 104 company from 2010-2015. Samples were separated to companies with symmetrical (equal) favorable and unfavorable sales FORECAST error and were compared. T test and Mann-Whitney tests were used for this purpose. The results showed that managers are not predicting the cost variability correctly; however, due to the symmetry of the earning FORECAST error in firms with equal sales FORECASTing error, it can be concluded that managers generally pay attention to cost behavior in their predictions. In other words, according to the proportion of sales FORECAST error and cost FORECAST error, the main reason for the earning FORECAST error is the error in predicting sales.

The aim of this study is to present two time-series FORECASTing models and combine these models to provide a short-term prediction for hourly electricity demand, using daily electricity consumption data for the period 2006-2011. The first model is based on the decomposition of the electricity load into deterministic and stochastic components and the second model is based on the assumption that the electricity load is a stochastic time series. Once the hourly demand for electricity load is predicted using the above-mentioned models, the performance of the combined model is compared with the two time-series models and also with the dispatching unit model (a multi-variable model in which the weather variable is also included). The results show that the use of the combined model leads to an increase in prediction accuracy over the two time-series models. Moreover, the accuracy of the combined model is as good as the dispatching unit model for most of the time during the day, and even better during the consumption peak hours.

Under the history of research there is a direct relationship between the audit quality and financial reporting reliability. We investigate whether audit quality is associated with the earninig FORECAST.The statistical population is the listed companies in Tehran Stock Exchange during 1381 to 1385. The evidence shows that earning FORECAST accuracy is higher and the FORECAST dispersion is smaller for firms audited by industry specialist auditors. In addition, there is an indirect relationship between auditor size and earning FORECAST dispersion.Our results suggest that high quality audit provided by the only big) i.e. Audit Organization (and industry specialist auditors is associated with more accurate earning FORECAST.

The purpose of this study is to investigate the relation between investor’, s sentiment and bias in management earnings FORECAST. To do this, three hypotheses are formulated. The sample consists of 165 firms listed in Tehran Stock Exchange during 2010-2010. The research method is the multivariate regression model using the panel data method. The results of research hypotheses show that investor sentiment has a negative and significant relationship with bias in the management earnings FORECASTs,In addition, according to the research results, managers' annual earnings FORECASTs are not more optimistic when investors are optimistic, nevertheless, it is optimistic when investors are pessimistic. However, there is no significant difference between investor sentiment and managers' predictions in firms with higher uncertainty.

In this paper, various analytical methods of 5G technology penetration rate is investigated and then 5G penetration rate is calculated for Iran based on Pareto method. The 5G technology penetration rate is important to predict the time and the way to migrate to the 5G network. It is assumed that the 5G technology penetration rate in the first year of the launch based on the Pareto method depends on the 4G penetration rate in the same year. The 5G technology penetration rate in Iran in the first year of the launch is predicted to be about 0. 95% based on the Pareto method. The number of 5G unique subscribers in the years after the launch is also FORECASTed from the number of 4G technology unique subscribers which is predicated by the Gompertz method. The best time for the widespread investment of the operator in the 5G is 2025. It is also estimated that the best time to replace 5G technology with 4G is 2029.