Using Fractional Order Grey Seasonal Model to Predict the Power Generation in China
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Using Fractional Order Grey Seasonal Model to Predict the Power Generation in China Kai Zhang 1 & Lifeng Wu 1 Received: 27 May 2020 / Accepted: 20 October 2020/ # Springer Nature Switzerland AG 2020
Abstract
To accurately predict the amount of power generation, the particle swarm optimization grey season model with fractional order accumulation (PSO-FGSM(1,1) model) is proposed. Seasonal indices are introduced into the new model to enhance its seasonality, and particle swarm algorithm is used to find the optimal order. In order to evaluate the performance of the proposed model, the calculation results of the Holt-Winters model are used for comparison. The experimental results show that the prediction errors of the proposed model and Holt-Winters model are 2.4% and 3.93% respectively. It is proved that the new model has better predictive performance. Finally, the new model is discussed in two specific cases, which further reflect the prediction ability of the proposed model to predict seasonal data. The accurate prediction results can provide reference for the allocation of power resources. Keywords Power generation . PSO-FGSM(1,1) model . GWO-Holt-Winters model . Power resource allocation
1 Introduction As one of the main energy sources, electricity is seen as a key role in the process of human civilization. The development of the power industry is related to the security and prosperity of a country. With the development of social economy and the increase of population, energy consumption is also increasing. Electricity generation is the main form of energy consumption. But Electricity production has caused a series of environmental pollution and climate change problems (Bartos and Chester 2015). Accurate prediction of power generation can reduce the cost of power generation and the impact on the environment. Changes in power generation have seasonal characteristics because of the influence of social production activities. In
* Lifeng Wu [email protected]
1
College of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China
Zhang K., Wu L.
addition, the use of clean energy enhances the seasonal electricity generation. Therefore, it is of great significance to capture the seasonal characteristics for accurately predicting power generation. Clean energy generation reduces the environmental impact of electricity generation. However, the seasonal characteristics of electricity generation are enhanced, making it more difficult to predict electricity generation. In recent years, new energy has been concerned and developed rapidly all over the word (Hosenuzzaman et al. 2015; Clò et al. 2015). In the Korean power industry, new energy and traditional energy are optimally allocated as sustainable development measures (Ahn et al. 2015). Britain has abundant wind resources, and wind power generation under extreme wind conditions has been studied (Cannon et al. 2015). China has a huge new energy market, including solar power, hydropower, wind power and other new energy generation g
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