Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM

Mao Yang,Xin Su,Tian Peng,Miaomiao Ma

doi:10.3389/fenrg.2021.757385

Abstract

The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method.

Highlights

In recent years, the depletion of fossil fuels and the widespread environmental pollution have become global issues that must be urgently solved
The wavelet packet (WP)-least squares support vector machine (LSSVM) PV power station output power point prediction method was selected because it has a higher prediction accuracy rate, and accurate point prediction provides a good basis for probability prediction
This study compared multiple probability density fittings on the point prediction error obtained by the WP-LSSVM method

Summary

Introduction

The depletion of fossil fuels and the widespread environmental pollution have become global issues that must be urgently solved. More countries and regions are searching for new energy sources to replace fossil fuels. Renewable energy sources, such as solar energy and wind energy, have attracted more attention worldwide owing to their advantages of being abundant, safe, and clean. In the first half of 2020, China’s newly installed photovoltaic power generation capacity reached 11.52 million kilowatts, including 7.082 million kilowatts of centralized photovoltaic and 4.435 million kilowatts of distributed photovoltaic. By the end of June, the cumulative installed capacity of photovoltaic power generation had reached 216 million kW, including 149 million kilowatts of centralized photovoltaic power and 67.07 million kilowatts of distributed photovoltaic power. The randomness, fluctuation, and intermittent nature of PV power impose enormous obstacles to the integration of solar energy into the power grid (Ueda et al, 2008; Armstrong, 2014; Europe, 2014)

Methods

Results

Conclusion