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A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

1
Department of Electrical Power Engineering, Faculty of Engineering Technology, Yarmouk University, Irbid 21163, Jordan
2
King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
3
School of Science, Technology and Engineering, University of Granada, 18011 Granada, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Antonino Laudani
Sensors 2021, 21(18), 6240; https://doi.org/10.3390/s21186240
Received: 16 July 2021 / Revised: 3 September 2021 / Accepted: 6 September 2021 / Published: 17 September 2021
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: artificial neural network; hourly demand; load forecasting; maximum demand; total demand artificial neural network; hourly demand; load forecasting; maximum demand; total demand
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MDPI and ACS Style

Alhmoud, L.; Abu Khurma, R.; Al-Zoubi, A.M.; Aljarah, I. A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network. Sensors 2021, 21, 6240. https://doi.org/10.3390/s21186240

AMA Style

Alhmoud L, Abu Khurma R, Al-Zoubi AM, Aljarah I. A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network. Sensors. 2021; 21(18):6240. https://doi.org/10.3390/s21186240

Chicago/Turabian Style

Alhmoud, Lina, Ruba Abu Khurma, Ala’ M. Al-Zoubi, and Ibrahim Aljarah. 2021. "A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network" Sensors 21, no. 18: 6240. https://doi.org/10.3390/s21186240

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