Error analysis of solar energy characteristics research

What is the prediction error of solar energy?

The overall prediction error of solar energy is smaller than that of wind energy, ranging from 3.9 to 10.0%, and the largest provincial prediction error is observed in Shanghai (SH), while the smallest provincial prediction error comes from Xinjiang (XJ).

Are error analysis benchmarks useful for wind and solar output forecasting?

There are research gaps in terms of error analysis benchmarks that consider long-term, high-granularity, and nationwide scales of wind and solar output prediction, particularly within the context of China. Error-analysis benchmarks for wind and solar output forecasting are of great value in academic research and industry.

How does the prediction error affect wind and solar energy uncertainty?

The results reveal the nationwide spatial distribution of the wind and solar energy uncertainty through the prediction error. The first-order difference and peak ratio of output data are determined as primary factors of the prediction error.

Why do wind and solar prediction errors vary across spatiotemporal distributions?

We reveal diversified spatiotemporal distribution patterns of prediction errors, indicating that over 60% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. The first-order difference and peak ratio of generation series are two primary indicators explaining the uncertainty distribution.

Why is prediction error analysis important in the decision-making of intermittent renewables?

Third, the prediction error of renewable power determines the revenue risk of power generation companies, especially in markets with deviation punishment. In this regard, prediction error analysis can provide an important reference for the decision-making of intermittent renewables.

Does daily fluctuation affect solar prediction error?

In contrast, the solar prediction error is affected by daily fluctuations since solar generation exhibits daily periodicity. Our results reveal the provincial distribution of the uncertainty of wind and solar generation, indicating different priorities for renewable energy development in different areas.