Estimating the Global Demand of Photovoltaic System

Estimating the Global Demand of Photovoltaic System

Yi-Fen Chen, Bi-Chu Chen, Chia-Wen Tsai, Wen-Yu Chen, Lee-Wei Wei
Copyright: © 2012 |Pages: 9
DOI: 10.4018/jsds.2012010105
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The purpose of this research is to predict the total market demand of photovoltaic (PV) system of the world. By using the Grey forecasting model, the results were precise and valid. Then, the sensitivity analysis was conducted to select the most appropriate horizontal adjusting factor (HAF) and to determine the growth type of PV industry. The result showed the HAF was 0.4, which indicated the growth speed is in a low speed but very close to normal speed. The average residual error was 10.5% from 1995 to 2007 compared to the actual value in the same period. Then, the forecasted value from 2008 to 2011 showed an increasing shape and would reach 8554.9 MW in 2011. This research found the growth type of PV industry of the world, offering meaningful information for firms to decide the strategy in the future. For government, the result could also help to implement adequate policies to support the development of PV industry in the future.
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2. Methodology

Prediction is a kind of technique in management which can reduce the uncertainty in the future. The administrator can use it to make an appropriate decision. Thus, a precise prediction method is needed. Deng (1982) submitted the Grey system theory in 1982, constructing a Grey forecasting model to do forecasting and make strategic decisions. Grey forecasting model is the basis and also the core of grey system theory (Tien, 2005; Chang et al., 2005; Chan, 2006; Lin et al., 2009; Lin & Yang, 2004). The Grey system theory treats all variables as a grey capacity within a certain range (Hsu & Chen, 2003; Tseng et al., 2001; Lin et al., 2011). In the grey procedure, the variables are related to time. The Grey capacity is not achieved by formulating the statistical regulation. It looks at the nature of internal regularity to manage the disorganized primitive data. Another characteristic of Grey forecasting model is it only requires few amounts of observations and can be very precise, while traditional method need large observations (Wang & Hung, 2003). In Grey forecasting model, only four observations can construct a forecasting model.

So far, Grey forecasting model has been widely used. Liu, Huang, and Lin (2008) applied GM (1,1) model to find the relaxation law of autofrettaged residual stresses in stable temperature and under pressure fluctuation in order to prove the efficiency of autofrettaged treatment. Chuang, Hsu, Wang, and Wang (2004) applied GM (1,1) model to forecast the stock price index in Taiwan. Lin and Yang (2003) applied the Grey forecasting model to predict the output value of Taiwan’s opto-electronics industry. Jiang, Yao, Deng, and Ma (2004) applied the Grey forecasting model to predict the operating energy performance for an air cooled water chiller (ACWC) units so as to install the Heating Ventilation and Air Conditioning (HVAC). Chang (2005) adopted Grey forecasting model to predict the production of TFT-LCD industry in Taiwan. Moreover, Hsu (2011) used improved grey forecasting models to forecast the output of opto-electronics industry.

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