التنبؤ بإستهلاک الطاقة الشمسية الکهروضوئية في الصين باستخدام عشرة حدوديات

نوع المستند : المقالة الأصلية

المؤلفون

1 باحثة في الإقتصاد بکلية الدراسات الآسيوية العليا – جامعة الزقازيق - مصر

2 أستاذ دکتور بقسم الإقتصاد – کلية الإقتصاد والعلوم السياسية – جامعة القاهرة – مصر

المستخلص

The problem of this paper is to forecast China’s consumption of PV-solar energy up to the year 2030. To solve this problem, ten polynomial models are built with degrees from first to tenth.  Computing the model’s parameters is based on the actual consumption in the period from 1989 to 2018 and applying the method of least-squares with the aid of  Excel, xuru, and php  software.  A comparison between the ten models is performed based on four measures; the root mean Square Residuals (RMSE), the complement of coefficient of determination (1-R2), the Bayesian Information Criterion (BIC) , and the absolute difference between the actual and forecasted consumption in 2019.  The best model is the fifth-degree polynomial. This model is tested for sensitivity and significance and it passes the tests. The model is used for forecasting the consumptions for the period from 2019 to 2030. The forecasted consumption is 3788 TWh in the year 2030.

الكلمات الرئيسية

المراجع

[1] Peng et al., Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm, Energies, Vol. 12, No. 7, 2019.
[2] Sicheng Wang, Current Status of  PV in China and Its Future Forecast, CSEE Journal of Power and Energy Systems, Vol. 6, No. 1,  2020.
[3] Shuxia Yang et al., Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory, Energies, Vol. 13, 2020.
[4] P.S. Bodger and Tay H. S. , Trend Extrapolation in Long-Term Forecasting: An Investigation Using New Zealand Electricity Consumption Data, Technological Forecasting and Social Change, Vol. 30, 1986.
[5] Dinesh W. Gawatre et al., Comparative Study of  Population Forecasting Methods,  IOSR Journal of Mechanical and Civil Engineering, Vol. 13, No. 4, 2016.
[6] عثمان علي شلبي ، مبادئ الرياضيات للتجاريين، الجزء الأول، کلية التجارة ، الزقازيق، مصر 2001 .
[7] BP,  “Statistical Review of World Energy 2019”,   68th edition.
[8] http://www.xuru.org/rt/TOC.asp
[9] http://polynomialregression.drque.net/online.php
[10]                     Douglas C. Montgomery et al., Introduction to Time Series Analysis and Forecasting, John Wiley & Sons, Inc., 2015.
[11]                    عبدالرحمن إسماعيل الصالحي ، مقدمة في علم الإحصاء ،    کلية التجارة ، الزقازيق ، مصر 1997 .
[12]                     Joe Chong, Powerful Forecasting with MS Excel, Published by XLPert Enterprise, 2010.
[13]                    عزام عبدالرحمن صبري، الإحصاء التطبيقي بنظام SPSS ، الدار المنهجية للنشر والتوزيع، عمان، الأردن، 2015.
[14]                     Emanuele Borgonovo, Sensitivity Analysis: An Introduction for the Management Scientist,  Springer International Publishing,  2017.

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