یک روش پیش‌بینی بلندمدت بار الکتریکی مبتنی بر استخراج ویژگی برای کاهش اثر داده های خارج از محدوده

محورهای موضوعی : انرژی های تجدیدپذیر

محمد داود سعیدی ¹ , مجید معظمی ²

1 - دانشکده مهندسی برق- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران
2 - مرکز تحقیقات ریزشبکه‌های هوشمند- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران

تاریخ دریافت : 1400/08/28 تاریخ پذیرش : 1400/12/16 تاریخ انتشار : 1402/12/01

کلید واژه: ماشین یادگیری شدید, پیش‌بینی بلند‌مدت بار, میانگین درصد خطای مطلق, بهبود دقت پیش‌بینی, پیش‌پردازش, تبدیل‌ موجک,

چکیده مقاله :

پیش‌بینی میان-‌مدت بار الکتریکی اغلب برای برنامه‌ریزی عملیات نیروگاه‌های حرارتی و آبی، زمان‌بندی بهینه برای بازرسی و تعمیرات و نگهداری نیروگاه‌ها و شبکه برق استفاده می‌شود. در این مقاله یک روش ترکیبی با استفاده از تبدیل موجک و ماشین یادگیری شدید مقاوم به داده‌های خارج از محدوده، برای پیش‌بینی بلند‌مدت بار ارائه ‌شده است. داده‌های بار و دمای ساعتی، از پایگاه داده GEFCOM 2014 استخراج‌ شده و به دو دسته آموزش و آزمایش تقسیم شده است. از تبدیل موجک یک سطحی برای تجزیه داده‌ها به‌منظور استخراج ویژگی‌ها و کاهش ابعاد ماتریس داده‌ها استفاده می‌شود. دو دسته مقادیر مؤلفه‌های فرکانس پایین (تقریب) و مقادیر مؤلفه‌های فرکانس بالا (جزئیات) حاصل از تجزیه جهت آموزش و پیش‌بینی به مدل‌ وارد شده و خروجی مقادیر پایین با خروجی مقادیر بالای مدل‌ جمع می شود تا پیش بینی نهایی را تشکیل دهد. جهت سنجش و مقایسه دقت و کارایی روش پیشنهادی، اعمال تبدیل موجک روی داده‌ها، برای سه مدل‌ دیگر ماشین یادگیری شدید انجام گردیده است. همچنین داده‌ها بدون اعمال تبدیل موجک به چهار مدل پیش بینی دیگر نیز وارد شده و نتایج پیش‌بینی حاصل با روش پیشنهادی مورد مقایسه قرار گرفته است. نتایج ارزیابی فوق نشان می‌دهد که تبدیل موجک و ماشین یادگیری شدید مقاوم به داده‌های خارج محدوده باعث بهبود دقت پیش‌بینی می‌گردد و مقدار میانگین درصد خطای مطلق به عدد ۰۹۶۶/۳ کاهش یافته است. مقدار خطای کلی محاسبه شده روش پیشنهادی بهترین نتیجه در بین سایر مدل‌های ماشین یادگیری شدید و روش‌های بدون پیش‌پردازش بوده است. خطای فوق بر مبنای مقدار میانگین درصد خطای مطلق به ترتیب ۴۲۰۸/۰ نسبت به مدل ماشین یادگیری شدید اصلی، ۱۱۹۴/۰ نسبت به مدل تنظیم‌شده و ۱۳۵۳/۰ نسبت به مدل تنظیم‌شده و وزن‌دار، کاهش یافته است.

چکیده انگلیسی:

Electrical load forecasting is the prediction of future demands based on various data and factors containing different consumptions on weekdays, electricity prices and weather conditions that are different for societies and places. Generally, medium-term electrical load forecasting is often used for the operation of thermal and hydropower plants, optimal time planning for maintenance of power plants and the power grids. However, long-term electrical load forecasting is used to manage on-time future demands and generation, transmission and distribution expansion planning. In this paper, a hybrid long-term load forecasting approach using wavelet transform and an outlier robust extreme learning machine is proposed. Hourly load and temperature data were extracted from the GEFCOM 2014 database and divided into two classes of training and test. The one-level wavelet transform is used to decompose data to extract properties and reduce the dimensions of the data matrix. Decomposed low-frequency component (approximations) and high-frequency component values (details) from wavelet analysis are entered into the model for training and forecasting. For comparison accuracy of the proposed method, wavelet transform is applied to the data for the other three extreme learning machines. Also data without wavelet transform entered into four other forecasting models and the load forecasting results are compared with the proposed method. The results of the above mentioned evaluation show that electrical load forecasting by using wavelet transform and outlier robust extreme learning machine improves forecasting accuracy and the MAPE reduces to 3.0966. The overall calculated error by the proposed method was the best result obtained between the three several models of extreme learning machines and without preprocessing model. The MAPE is 0.4208 less than the ELM, 0.944 less than the RELM, and 0.1353 less than the WRELM model, respectively.

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