A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/18/6240 |
| _version_ | 1827681276864233472 |
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| author | Lina Alhmoud Ruba Abu Khurma Ala’ M. Al-Zoubi Ibrahim Aljarah |
| author_facet | Lina Alhmoud Ruba Abu Khurma Ala’ M. Al-Zoubi Ibrahim Aljarah |
| author_sort | Lina Alhmoud |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:13:55Z |
| format | Article |
| id | doaj.art-ded99605246941d48d0c187461ee4059 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:55Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ded99605246941d48d0c187461ee40592023-11-22T15:13:50ZengMDPI AGSensors1424-82202021-09-012118624010.3390/s21186240A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural NetworkLina Alhmoud0Ruba Abu Khurma1Ala’ M. Al-Zoubi2Ibrahim Aljarah3Department of Electrical Power Engineering, Faculty of Engineering Technology, Yarmouk University, Irbid 21163, JordanKing Abdullah II School for Information Technology, The University of Jordan, Amman 11942, JordanKing Abdullah II School for Information Technology, The University of Jordan, Amman 11942, JordanKing Abdullah II School for Information Technology, The University of Jordan, Amman 11942, JordanPower 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.https://www.mdpi.com/1424-8220/21/18/6240artificial neural networkhourly demandload forecastingmaximum demandtotal demand |
| spellingShingle | Lina Alhmoud Ruba Abu Khurma Ala’ M. Al-Zoubi Ibrahim Aljarah A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network Sensors artificial neural network hourly demand load forecasting maximum demand total demand |
| title | A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network |
| title_full | A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network |
| title_fullStr | A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network |
| title_full_unstemmed | A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network |
| title_short | A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network |
| title_sort | real time electrical load forecasting in jordan using an enhanced evolutionary feedforward neural network |
| topic | artificial neural network hourly demand load forecasting maximum demand total demand |
| url | https://www.mdpi.com/1424-8220/21/18/6240 |
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