تجزیه و تحلیل پیشگویانه در عرضه و تقاضای برق در چین
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|9319||2007||10 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 3604 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
- تولید محتوا با مقالات ISI برای سایت یا وبلاگ شما
- تولید محتوا با مقالات ISI برای کتاب شما
- تولید محتوا با مقالات ISI برای نشریه یا رسانه شما
پیشنهاد می کنیم کیفیت محتوای سایت خود را با استفاده از منابع علمی، افزایش دهید.
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Renewable Energy, Volume 32, Issue 7, June 2007, Pages 1165–1174
In order to analyze the electric-power demand and supply in China efficiently, this paper presents a Grey–Markov forecasting model to forecast the electric-power demand in China. This method takes into account the general trend series and random fluctuations about original time-series data. It has the merits of both simplicity of application and high forecasting precision. This paper was based on historical data of the electric-power requirement from 1985 to 2001 in China, and forecasted and analyzed the electric-power supply and demand in China by the Grey–Markov forecasting model. The forecasting precision of Grey-Markov forecasting model from 2002 to 2004 is 99.42%, 98.05% and 97.56% respectively, and in GM(1,1) Grey forecasting model, it is 98.53%, 94.02% and 88.48%, respectively. It shows that the Grey–Markov forecasting models has higher precision than GM(1,1) Grey forecasting model. The forecast values from 2002 to 2013 were as follows: 16106.7, 18541.3, 20575.7, 23940.5, 24498.0, 26785.1, 27977.2, 29032.2, 31247.5, 33428.8, 35865.4, and 38399.3 TW h. The results provide scientific basis for the planned development of the electric-power supply in China.
Along with economic growth of nearly 10% per year over the last two decades, electric-power use has been rapidly increasing in China. During the 1980s, because the developing speed of the economy was quicker than that of the electric-power industry, China had a scarcity of electric-power. To deal with the situation, China first innovated the investment system. In 1981, the Longkou power plant was constructed with joint venture by the central government and local units in Shandong province. In 1987, State Department advanced a “twenty-words policy” about power system innovation, which translated to English was “government and corporation should be separated, a province is an entity, unite the electric-power network, attemp the system united, raise funds to build power plants”. In 1984, a Japanese company constructed a new hydropower station in the Yunnan province, which was called as Lubuge hydropower station. Because it was the first power plant constructed by foreign capital, it was also called as “Lubuge shock wave” which influenced the investment system of electric-utility industry very much. From 1985, State Department set up Huaneng Power International Corporation and other power corporations to raise foreign capital. With the new policies, Chinese electric-utility industry developed very quickly. After the total electric-power equipped capacity exceeded 1 TW in 1987, the total electric-power equipped capacity exceeded 2 TW in 1995 and exceeded 3 TW in 2000. In 2004, the additional electric-power equipped capacity was 0.51 TW, which was at the first place of the world. The total electric-power equipped capacity was 4.47 TW. Chinese electric-power demand and supply began to keep a balance from 1996 . The electric generation production of 1985 was 4117.6 TW h, and that of 2004 was 20418.08 TW h. It means that there was 4.98 times increase from 1985. The electric generation production had grown at 8.82% per year between 1985 and 2004. The total electric-power equipped capacity and the generation production of China were at the second place of the world, from 1996 untill now. While great development was accessed in Chinese electric-utility industry, there were many problems which influenced the development of the society badly. Electric-power was a resource that could not be stored. The supply quantity had to equal the consumption. So neither the shortage of supply nor the short of consumption would influence the electric-utility industry seriously. Perhaps, the whole occupation would be deficient due to lack work, and maybe insufficient supply would affect the function of different fields in society. In late 1980s, consumption of electricity in our country kept increasing between 9% and 10%. It reached 16% and became the top in history in 1991, while in 1992 and 1993, it kept above 11%. This period was called as over-economic. The lack of electricity appeared in the whole society. The power cut appeared even in Beijing. The government started to adjust the quo-status in 1993, which made the economy in China come into “soft-land”. The consumption of the whole country was decreasing, and reached the bottom of 2.8%. It led to the free of electric equipments and the whole coal industry fell into deficit. The government made a stipulation on prohibiting building new electric factory during three years. However, the increase of consumption in 1999 recovered 6.2% and 9% in 2000. Since 2000, the lack of electricity pervaded from the delta in Changjiang River to Zhujiang River even the whole country. There were 12 provinces to cut power and limit the electric use. The lack of electric equipment was 20.35 GW. In 2003, the power of 22 provinces cut, the lack of electric equipment was 44.85 GW, which doubling compared to last year. The power of 24 provinces cut till 2004, and the largest lack of electricity reached 30 GW. It diffused in the whole country, including the undeveloped areas such as Guangxi, Yunnan, Guizhou, Shanxi, Neimenggu, Gansu, Qinghai, Ningxia et al. The summer of 2004, was considered to have experienced as the most serious lack of power since more than twenty years. The consumption of electricity was 19,458.4 TW h from January to November in this year. It increased 15.13% more than that in last year at the same time approaching to the top value. There are many factors that cause lack of electricity, such as insufficient investment in electricity, the distinction in the speed of new electrical product and the demand increase, the rapid increase of the industry and electrical industry with high energy consumption, the low ability of electrical transport and the accidents, the quick increase in the load of air-conditioning, the overwrought supply caused by the controversy in the price of electricity and coal, the shortage of water, etc. Because of many undermining factors, including economical development, the structure of industry, the incoming lever of citizen, climate and the national policy, etc., fluctuation in the consumption of electricity and the increase of load appeared obvious. Efficient methods to predict the electrical demand in middle and long term are scarce. We attempt to study a reasonable approach for development of electricity using scientific methods. Thus, according to the data of the total consumption of electricity, this paper proposed a Grey–Markov forecasting model to forecast and analyze the electric-power supply and demand in China. Deng initially presented the Grey system theory in 1982 . The Grey forecasting model adopts the essential part of the Grey system theory. The GM(1,1) Grey forecasting model can be used in circumstances with relatively little data and it can use a first-order differential equation to characterize an unknown system. So the GM(1,1) Grey forecasting model is suitable for forecasting the competitive environment where decision makers can refer only to a limited historical data . But the forecasting precision for data sequences with large random fluctuation is low. The Markov-chain forecasting model can be used to forecast a system with randomly varying time series. It is a dynamic system which forecasts the development of the system according to transition probabilities between states which reflect the influence of all random factors. So the Markov-chain forecasting model is applicable to problems with random variation, which could improve the GM(1,1) model . The disadvantage of the model is that it demands that the process be a stationary one, that is, the trend curve is a horizontal line. If the trend curve is nonhorizontal, the forecast is less accurate because the states of the ordinary Markov probability matrix forecast are stationary. To improve the Markov-chain forecasting model, this paper builds a GM(1,1) Grey forecasting model to describe the historical data of the electric-power demand in China to calculate the fluctuating trend curve first . The rationale of Grey–Markov forecasting model is as follows: first a GM(1,1) Grey forecasting model is built to calculate the fluctuating trend curve of the historical data series, then specify some states around the trend curve, a Markov transition matrix can be built to find out the transition probability, finally these two models should be combined to forecast accurately by the historical time-series data. This forecasting method can make full use of the information given by historical data, and increase greatly the forecasting precision of random fluctuating sequences.
نتیجه گیری انگلیسی
The Grey–Markov forecasting model can fully utilize the information of the historical time-series data with large random fluctuation. As the case study shows that the accuracy of Grey–Markov forecasting model in forecast value from 2002 to 2004 is higher than those of GM(1,1) Grey forecasting model in this paper. Based on the above analysis, the Grey–Markov forecasting model appeals to be intrinsically better because it has merits of both simplicity of application and high forecasting precision. This model has integrated the advantages of GM(1,1) Grey forecasting model and Markov-chain forecasting model. The forecasting result of this forecasting method greatly depends on state-intervals partitioning. There is no standard rule to divide the states intervals. Generally speaking, the number of states should be decided according to the data and the demands of the problem. If the historical data are not abundant, the number of states should be fewer, so that the transition between the states can be clearly revealed. If the data are abundant, the number of states should be increased and the forecasting precision would be increased correspondingly. Because the electric-power demand in China is influenced by many factors such as the economy, industry framework, people's income level, weather and government's policy, there is a certain development trend for the historical time-series data, and the data fluctuate randomly. So it is suitable to forecast the electric-power demand by the Grey–Markov forecasting model. The Grey–Markov forecasting model could be applied to forecast other time series problems with large random fluctuation.