Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

Xiaoshuang Luo,Wenhao Zhou,Hui Li,Bo Zeng,Lifeng Wu

doi:10.1155/2021/9200560

Xiaoshuang Luo, Wenhao Zhou + Show 3 more

Open Access

https://doi.org/10.1155/2021/9200560

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Abstract

The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

Highlights

With the development of China’s manufacturing, the rapid growth of energy consumption has become a major bottleneck affecting China’s sustainable development of the economy
Wind turbines use installed capacity to describe how much electricity may be generated by a turbine in optimal wind conditions, describing how many watts of electricity the turbine hardware can possibly produce—generally measured in megawatts or kilowatts. e US Energy Information Administration (EIA) refers to capacity as the maximum output of electricity that a generator can produce under ideal conditions
We focus on forecasting wind energy consumption from a macro perspective

Summary

Introduction

With the development of China’s manufacturing, the rapid growth of energy consumption has become a major bottleneck affecting China’s sustainable development of the economy. Volatility, and intermittence of wind power systems, the data characteristics of wind power prediction and installed capacity are consistent with the grey model of “small samples, poor information.” e techniques based on large samples, such as the SVM and neural network, are difficult to apply to the prediction of WPIC and the ARMA model is difficult to detect the complex nonlinear dynamic processes of power system and wind energy, so it can not accurately describe the real changes of a wind speed or wind farm power, which affects the performance of the prediction results. Is paper intends to expand the structure and optimize the parameters of the conventional grey model, on the basis of which a more reasonable prediction model of wind power installed capacity is constructed. In equation (3), we can estimate the parameter a and b using the least-square method, which is as follows: p􏽢

Section 3. Error test method

Findings

Conclusion