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Soleimani, Paria, Yaghobi, Zohreh. (1397). Forecasting of IRAN Power Demand Network by hybrid of Support Vector Regression model and Fruit fly Optimization Algorithm. , 52(3), 405-420. doi: 10.22059/jieng.2019.249233.1509
Paria Soleimani; Zohreh Yaghobi. "Forecasting of IRAN Power Demand Network by hybrid of Support Vector Regression model and Fruit fly Optimization Algorithm". , 52, 3, 1397, 405-420. doi: 10.22059/jieng.2019.249233.1509
Soleimani, Paria, Yaghobi, Zohreh. (1397). 'Forecasting of IRAN Power Demand Network by hybrid of Support Vector Regression model and Fruit fly Optimization Algorithm', , 52(3), pp. 405-420. doi: 10.22059/jieng.2019.249233.1509
Soleimani, Paria, Yaghobi, Zohreh. Forecasting of IRAN Power Demand Network by hybrid of Support Vector Regression model and Fruit fly Optimization Algorithm. , 1397; 52(3): 405-420. doi: 10.22059/jieng.2019.249233.1509

Forecasting of IRAN Power Demand Network by hybrid of Support Vector Regression model and Fruit fly Optimization Algorithm

Advances in Industrial Engineering
مقاله 8، دوره 52، شماره 3، دی 2018، صفحه 405-420    اصل مقاله (1.26 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jieng.2019.249233.1509
نویسندگان
Paria Soleimani* 1؛ Zohreh Yaghobi2
1Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2Department of Industrial engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
چکیده
Accurate monthly power demand network forecasting can help to plan the energy and it can handle the correct management of the power consumption. It has been found that the monthly electricity consumption demonstrates a complex nonlinear characteristic and has an obvious seasonal tendency. One of the models that is widely used to predict the nonlinear time series is the support vector regression model (SVR) in which the selection of key parameters and the effect of seasonal changes could be considered. The important issues in this research are to determine the parameters of the support vector regression model optimally, as well as the adjustment of the nonlinear and seasonal trends of the electricity data. The method that is proposed by this study is to hybrid the support vector regression model (SVR) with Fruit fly optimization Algorithm (FOA) and the seasonal index adjustment to forecast the monthly power demand. In addition, in order to evaluate the performance of the hybrid predictive model a small sample of the monthly power demand from Iran and a large sample of Iran monthly electricity production has been used to demonstrate the predictive model performance. This study also evaluates the superiority of the SFOASVR model to the other known predictive methods. In terms of the prediction accuracy, we used the evaluation criteria such as Root Mean Square Error (RMSE) and mean absolute percentage error (MAPE) as well as Wilcoxon's nonparametric statistical test. The results show that the SFOASVR model has less error than the other forecasting models and is superior to the most other models in terms of Wilcoxon test. Therefore, SFOASVR method is an appropriate option for prediction of the power demand.
کلیدواژه‌ها
Forecast؛ Power demand network؛ Seasonal changes؛ Support Vector Regression (SVR)؛ Fruit fly Optimization Algorithm (FOA)
عنوان مقاله [English]
پیش‌بینی نیاز مصرف فصلی شبکۀ برق ایران با استفاده از روش ترکیبی رگرسیون بردار پشتیبان و الگوریتم بهینه‌سازی مگس میوه
نویسندگان [English]
پریا سلیمانی1؛ زهره یعقوبی2
1استادیار مهندسی صنایع، دانشگاه آزاد اسلامی، واحد تهران جنوب
2کارشناس ارشد مهندسی صنایع، دانشگاه آزاد اسلامی، واحد تهران جنوب
چکیده [English]
پیش‌بینی دقیق نیاز مصرف شبکة برق ماهانه می‌تواند در برنامه‌ریزی انرژی مؤثر باشد و مدیریت صحیح‌تر مصرف برق را امکان‌پذیر کند. نیاز مصرف برق ماهانه نشان‌دهندة گرایش فصلی پیچیده و غیرخطی است یکی از مدل‌هایی که به‌طور گسترده برای پیش‌بینی سری‌های زمانی غیرخطی استفاده می‌شود، رگرسیون بردار پشتیبان (SVR) است که در آن باید انتخاب پارامترهای کلیدی و تأثیر تغییرات فصلی درنظر گرفته شود؛ بنابراین ضروری است پارامترهای مدل رگرسیون بردار پشتیبان به‌صورت مناسب انتخاب شوند و گرایش‌های غیرخطی و فصلی داده‌های نیاز مصرف برق تعدیل شوند. روشی که در پژوهش حاضر پیشنهاد می‌شود، پیوندزدن مدل رگرسیون بردار پشتیبان (SVR) با الگوریتم بهینه‌سازی مگس میوه (FOA) و تنظیم شاخص فصلی برای پیش‌بینی نیاز مصرف برق ماهانه است. علاوه‌براین، به‌منظور ارزیابی جامع عملکرد پیش‌بینی مدل ترکیبی، نمونه‌ای کوچک از نیاز مصرف برق ماهانة ایران و نمونة بزرگی از تولید برق ماهانة ایران برای نشان‌دادن عملکرد پیش‌بینی بررسی شده است. همچنین در این پژوهش برتری «مدل ترکیبی رگرسیون بردار پشتیبان با الگوریتم بهینه‌سازی مگس میوه با تعدیل گرایش‌های فصلی (SFOASVR)» در مقایسه با سایر مدل‌های شناخته‌شده پیش‌بینی از نظر دقت پیش‌بینی و کم‌بودن خطای پیش‌بینی بررسی شده است. برای این منظور معیارهای ارزیابی ریشة میانگین مربعات خطا (RMSE) و میانگین درصد خطای مطلق (MAPE)، همچنین آزمون ناپارامتری ویلکاکسون صورت می‌گیرد. براساس نتایج، مدل SFOASVR از سایر مدل‌های پیش‌بینی خطای کمتری دارد و درنتیجه گزینه‌ای مناسب برای کاربردهای پیش‌بینی نیاز مصرف برق است.
کلیدواژه‌ها [English]
الگوریتم بهینه‌سازی مگس میوه (FOA), تغییرات فصلی, پیش‌بینی, رگرسیون بردار پشتیبان (SVR), نیاز مصرف شبکة برق
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