Paper Info
Title
A Real-Time Electrical Load Forecasting In Jordan Using An Enhanced Evolutionary Feedforward Neural Network
Abstract
Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. Two Well-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work's main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan's current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results.
Year
DOI
Venue
2021
10.3390/s21186240
SENSORS
Keywords
DocType
Volume
artificial neural network, hourly demand, load forecasting, maximum demand, total demand
Journal
21
Issue
ISSN
Citations 
18
1424-8220
1
PageRank 
References 
Authors
0.41
0
4
Name
Order
Citations
PageRank
Lina Alhmoud110.75
Ruba Abu Khurma221.10
Ala' M. Al-Zoubi32219.83
Ibrahim Aljarah470333.62