Abstract
The forecast error characteristic analysis of short-term photovoltaic power generation can provide a reliable reference for power system optimal dispatching. In this paper, the total in-day error level was stratified by fuzzy C-means algorithm. Then the historical PV output data based on the numerical characteristics of point prediction output were classified. A General Gauss Mixed Model was proposed to fit the forecast error distribution of various photovoltaic output forecast error distribution. The impact of meteorological factors together with numerical characteristics on the forecast error was taken into full consideration in this analysis method. The predicted point output with high volatility can be accurately captured, and the reliable confidence interval is given. The proposed method is independent of the point prediction algorithm and has strong applicability. The General Gauss Mixed Model can meet the peak diversity, bias, and multimodal properties of the error distribution, and the fitting effect is superior to the normal distribution, the Laplace distribution, and the t Location-Scale distribution model. The error model has a flexible shape, a concise expression, and high practical value for engineering.
Highlights
Facing the double pressure of energy crisis and environmental pollution, people pay more and more attention to the new energy generation technology with clean and environmental protection characteristics
Ere are only a few literatures on the forecast error of PV power generation at home and abroad, and the description of the prediction error of PV output in some literature is based on the assumption that it obeys normal distribution. e PV output uncertainty needs to be considered when studying the optimal scheduling of power systems, and most of the literature uses the actual output value in the form of the sum of the predicted output and the forecast error
Based on the assumption that the forecast error obeys normal distribution, the results obtained in [19,20,21] are different from the actual statistical results. e research in [22] shows that weather factors have great influence on the forecast error, and the forecast error of solar volts in sunny days is close to normal distribution. e feasibility of using t Location-Scale model to describe the forecast error of PV output is proposed and verified in [23]. e statistical results show that the PV output forecast error distribution has multiple peaks, while the existing research using single distribution model is weak in describing the multipeaks
Summary
Facing the double pressure of energy crisis and environmental pollution, people pay more and more attention to the new energy generation technology with clean and environmental protection characteristics. Ere are only a few literatures on the forecast error of PV power generation at home and abroad, and the description of the prediction error of PV output in some literature is based on the assumption that it obeys normal distribution. Because of the random characteristics of meteorological factors such as solar irradiation, temperature, and wind speed, the forecast error of photovoltaic output does not have a certain distribution characteristic, and it is difficult for the established forecast error model to achieve ideal accuracy. Compared with the traditional Gaussian model, this model can describe the error distribution of different kurtosis and shape more accurately This method is universal and is not affected by photovoltaic power prediction algorithm and the geographical location of photovoltaic power stations
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