Wind Power Interval Forecasting Based on Confidence Interval Optimization

Abstract

Most of the current wind power interval forecast methods are based on the assumption the point forecast error is subject to a known distribution (such as a normal distribution, beta distribution, etc.). The interval forecast of wind power is obtained after solving the confidence interval of the known distribution. However, this assumption does not reflect the truth because the distribution of error is random and does not necessary obey any known distribution. Moreover, the current method for calculating the confidence interval is only good for a known distribution. Therefore, those interval forecast methods cannot be applied generally, and the forecast quality is not good. In this paper, a general method is proposed to determine the optimal interval forecast of wind power. Firstly, the distribution of the point forecast error is found by using the non-parametric Parzen window estimation method which is suitable for the distribution of an arbitrary shape. Secondly, an optimal method is used to find the minimum confidence interval of arbitrary distribution. Finally the optimal forecast interval is obtained. Simulation results indicate that this method is not only generally applicable, but also has a better comprehensive evaluation index.

Optimization of Wind Farm Self-Discipline Interval and Energy Storage System Configuration

“…A non-parametric kernel density estimation method is introduced to estimate the error distribution in this paper [12]. The non-parametric kernel density estimation method is suitable for arbitrary shape error distribution.…”

“…The index PICP (the prediction interval coverage probability) can be used to evaluate the self-discipline interval accuracy. PICP is the percentage of actual values falling into the confidence interval, as shown in formula (12 ).…”

“…The authors of [11] reduce the impacts of wind power forecast errors while prolonging the lifetime of ESS. In [12], a mathematical model and an optimization method is proposed to compensate the forecast error of wind power and reduce the uncertainty of wind power output. In [13], a direct control strategy is proposed to track the deviation of the wind power plan, the control strategy in this paper can change the charge and discharge power of energy storage in real-time according to the deviation of wind power and the state of charge.…”

Adaptive Damping Control Strategy of Wind Integrated Power System

“…The stability and control of the power system is determined for a given set of circumstances [1]. With the rapid growth of wind power resources, the stochastic flotation of wind power has become more and more significant and cannot be neglected [2,3]. In most cases, lack of robustness of the controllers designed for the deterministic operating point when the wind power output changes a lot has become a dominant problem [4].…”

The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece

“…Note that MIS evaluates prediction intervals taking into consideration both their coverage, i.e., the percentage of times when the true values lie inside the prediction intervals, and their spread, i.e., the distance between the upper and lower bounds [51]. Thus, in order for a prediction interval to be effective, it must provide the nominal coverage with the minimum possible width [52]. The experimental setup can be summarized as follows:…”