A Wavelet Transform-Based Hybrid Short Term Load Forecasting Method for Managing the Costs of EV Charging Stations and Parking Lots

  • سال انتشار: 1402
  • محل انتشار: فصلنامه پردازش سیگنال و انرژیهای تجدیدپذیر، دوره: 7، شماره: 4
  • کد COI اختصاصی: JR_SPRE-7-4_006
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 117
دانلود فایل این مقاله

نویسندگان

Yashar Khanchoupani

Department of electrical engineering, Islamic Azad University, Urmia Branch, Urmia, Iran

Mojtaba Beiraghi

Department of electrical engineering, Islamic Azad University, Urmia Branch, Urmia, Iran

Reza Ghanizadeh

Department of electrical engineering, Islamic Azad University, Urmia Branch, Urmia, Iran

چکیده

Advanced data processing methods are emerging on a daily basis and they can be used in many tasks. Short-term load forecasting (STLF) is an essential task for power distribution systems. Specially, for the electric vehicle (EV) charging stations and parking lots the STLF can give precise time tables in which the electrical power demand is at the lowest and the electric prices are low too. In this paper, we developed a STLF system that works based on wavelet transform of the load data collected from the previous ۳ months. Next, we perform the feature selection using the Gram-Schmidt (GS) procedure. The selected features are then fed to the Radial basis function (RBF) network in order to predict the power usage one day ahead. The learning algorithm that is used for the RBF network is the hybrid "k-means, RLS" algorithm. It is indicated that the proposed RBF network works more accurately or at least equal to the previously presented support vector machine (SVM) predictor. Also, the computational complexity of the RBF network is much less than SVM and consequently, the time consumption of the presented system is far less than recently proposed methods. The numerical simulation results based on the real-world load data of the Urmia city showed that the proposed method based on the wavelet transform features and the hybrid RBF is more efficient than the previous SVM both in accuracy (more than ۲۹%) and computational complexity (more than ۳۰%).

کلیدواژه ها

RBF network, Short Term Load Forecasting, RLS Algorithm, hybrid learning, performance

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.