The dynamic development of wind power in recent years has generated the demand for production forecasting tools in wind farms. The data obtained from mathematical models is useful both for wind farm owners and distribution and transmission system operators. The predictions of production allow the wind farm operator to control the operation of the turbine in real time or plan future repairs and maintenance work in the long run. In turn, the results of the forecasting model allow the transmission system operator to plan the operation of the power system and to decide whether to reduce the load of conventional power plants or to start the reserve units. The presented article is a review of the currently applied methods of wind power generation forecasting. Due to the nature of the input data, physical and statistical methods are distinguished. The physical approach is based on the use of data related to atmospheric conditions, terrain, and wind farm characteristics. It is usually based on numerical weather prediction models (NWP). In turn, the statistical approach uses historical data sets to determine the dependence of output variables on input parameters. However, the most favorable, from the point of view of the quality of the results, are models that use hybrid approaches. Determining the best model turns out to be a complicated task, because its usefulness depends on many factors. The applied model may be highly accurate under given conditions, but it may be completely unsuitable for another wind farm.
Taking the importance of time and risk into account has a significant impact on the value of investment projects. Investments in the energy sector are long-term projects and, as such, are burdened with uncertainty associated with the long-term freezing of capital and obtaining the expected return. In the power industry, this uncertainty is increased by factors specific to the sector, including in particular changes in the political and legal environment and the rapid technological development. In the case of discounted cash flow analysis (DCF), commonly used for assessing the economic efficiency of investments, the only parameter expressing investor uncertainty regarding investment opportunities is the discount rate, which increases with the increasing risk of the project. It determines the value of the current project, thus becoming an important criterion affecting investors’ decisions. For this reason, it is of great importance for the assessment of investment effectiveness. This rate, usually in the form of the weighted average cost of capital (WACC), generally includes two elements: the cost of equity capital and borrowed capital. Due to the fluctuant relationship between these two parameters in project financing, performing a WACC analysis in order to compare the risks associated with the different technologies is not completely justified. A good solution to the problem is to use the cost of equity. This article focuses on the analysis of this cost as a measure of risk related to energy investments in the United States, Europe and worldwide.