The Research on the Construction of Confidence Interval Model for Solar, Hydropower and Load Demand

The renewable energy output and load demand are accompanied by volatility and randomness, which will affect the safe and reliable operation of the power system. Since the traditional power point prediction method cannot describe their uncertainty well, interval prediction can provide more comprehensive and valuable decision information for the operation of the power system. Based on this, a non-parametric kernel density estimation method without pre-assumption model is used to analyze the probability distribution characteristics of renewable energy output and load demand. The probability distribution curves are also fitted by this method, and then confidence intervals at different confidence levels are derived. Using the photovoltaic (PV) data as an example, the PV probability density function is calculated and the probability distribution curve is fitted to obtain the output prediction intervals at different confidence levels. The prediction interval coverage probability is used as an evaluation index of interval estimation to analyze and measure the effect of modeling estimation of renewable energy output and load demand.