Photovoltaic Power Forecasting: Basic Approaches and Features

The article is devoted to the tasks of forecasting the productivity of solar power plants (SPP) and PV-unit. A review of modern forecasting methods and the actual basis for its implementation is made. Accepted classifications of methods are presented, the basis of which is the “direct forecasts - indirect forecasts” approach. The basis of the second approach is the forecast of solar radiation with subsequent conversion to the productivity of solar power plants. Accordingly, in the first case, a series of data on plants productivity over long periods in the past are of great importance and mainly statistical and machine learning methods are used. The second approach is based on a numerical weather forecast, which provides, among other things, a solar radiation forecast. A significant influence on the choice of forecasting methods is provided by the required spatial and temporal horizon. The latter is determined by the electricity market rules in a particular country or region. The problem of spatial resolution is important for power plants occupying large areas, as well as in forecasting the productivity of a network of solar plants. The article presents the currently adopted forecast metrics which allow estimating errors, as well as comparing the effectiveness of various forecasting methods. The prospects of developing probabilistic forecasts as an alternative to deterministic approaches are shown. Probabilistic forecasts are more likely to meet network requirements. The requirements of grid operators in some countries for forecasting the productivity of SPP and the penalties in case of a high forecast error are considered in the article. Examples of financial losses from forecast errors are shown on the example of solar power plants in the USA and China. The requirements of the Russian Federation wholesale energy and capacity market are discussed. The market sets the maximum permissible deviations from the declared energy production by stations (including SPP) and fines for these deviations. The article presents the financial  losses estimates from errors in productivity forecasts for solar energy plants in the Russian Federation.

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