Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq
This study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neu...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/6/2919 |
| _version_ | 1797612054290366464 |
|---|---|
| author | Ahmed Mazin Majid AL-Qaysi Altug Bozkurt Yavuz Ates |
| author_facet | Ahmed Mazin Majid AL-Qaysi Altug Bozkurt Yavuz Ates |
| author_sort | Ahmed Mazin Majid AL-Qaysi |
| collection | DOAJ |
| description | This study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neural network (ANN) model with advanced prediction techniques such as genetic algorithms (GA) and adaptive neuro-fuzzy inference systems (ANFIS). This article stands out with a comprehensive compilation of many features and methodologies previously presented in other studies. This study uses a long-term pattern in the prediction process and achieves the lowest relative error values by using hourly divided annual data for testing and training. Data samples were applied to different algorithms, and we examined their effects on load predictions to understand the relationship between various factors and electrical load. This study shows that the ANN–GA model has good accuracy and low error rates for load predictions compared to other models, resulting in the best performance for our system. |
| first_indexed | 2024-03-11T06:35:44Z |
| format | Article |
| id | doaj.art-48b70181cf6e454290ae5e04d3ba3f05 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T06:35:44Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-48b70181cf6e454290ae5e04d3ba3f052023-11-17T10:52:47ZengMDPI AGEnergies1996-10732023-03-01166291910.3390/en16062919Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in IraqAhmed Mazin Majid AL-Qaysi0Altug Bozkurt1Yavuz Ates2Department of Electrical Engineering, Yildiz Technical University, 34220 Istanbul, TurkeyDepartment of Electrical Engineering, Yildiz Technical University, 34220 Istanbul, TurkeyDepartment of Electrical Electronics Engineering, Manisa Celal Bayar University, 45140 Manisa, TurkeyThis study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neural network (ANN) model with advanced prediction techniques such as genetic algorithms (GA) and adaptive neuro-fuzzy inference systems (ANFIS). This article stands out with a comprehensive compilation of many features and methodologies previously presented in other studies. This study uses a long-term pattern in the prediction process and achieves the lowest relative error values by using hourly divided annual data for testing and training. Data samples were applied to different algorithms, and we examined their effects on load predictions to understand the relationship between various factors and electrical load. This study shows that the ANN–GA model has good accuracy and low error rates for load predictions compared to other models, resulting in the best performance for our system.https://www.mdpi.com/1996-1073/16/6/2919artificial neural networkadaptive neuro-based fuzzy inference systemelectrical load forecastinggenetic algorithms |
| spellingShingle | Ahmed Mazin Majid AL-Qaysi Altug Bozkurt Yavuz Ates Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq Energies artificial neural network adaptive neuro-based fuzzy inference system electrical load forecasting genetic algorithms |
| title | Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq |
| title_full | Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq |
| title_fullStr | Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq |
| title_full_unstemmed | Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq |
| title_short | Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq |
| title_sort | load forecasting based on genetic algorithm artificial neural network adaptive neuro fuzzy inference systems a case study in iraq |
| topic | artificial neural network adaptive neuro-based fuzzy inference system electrical load forecasting genetic algorithms |
| url | https://www.mdpi.com/1996-1073/16/6/2919 |
| work_keys_str_mv | AT ahmedmazinmajidalqaysi loadforecastingbasedongeneticalgorithmartificialneuralnetworkadaptiveneurofuzzyinferencesystemsacasestudyiniraq AT altugbozkurt loadforecastingbasedongeneticalgorithmartificialneuralnetworkadaptiveneurofuzzyinferencesystemsacasestudyiniraq AT yavuzates loadforecastingbasedongeneticalgorithmartificialneuralnetworkadaptiveneurofuzzyinferencesystemsacasestudyiniraq |