Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Amjady, N. ; Dept. of Electr. Eng., Semnan Univ., Semnan, Iran ; Daraeepour, A.

Prediction of daily peak load for next month is an important type of medium-term load forecast (MTLF) for electrical power systems, which provides useful information for maintenance scheduling, adequacy assessment, scheduling of fuel supplies and limited energy resources, etc. However, the exclusive characteristics of daily peak load signal, such as its nonstationary, nonlinear and volatile behavior, present a number of challenges for this task. In this paper, a new hybrid forecast engine is proposed for this purpose. The proposed engine has an iterative training mechanism composed of a novel stochastic search technique and Levenberg-Marquardt (LM) learning algorithm. The effectiveness of the proposed forecast strategy is extensively evaluated based on several benchmark datasets.

Published in:

Power Systems, IEEE Transactions on  (Volume:26 ,  Issue: 2 )