Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island
This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression method...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/16/5950 |
| _version_ | 1827628350147919872 |
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| author | Mustafa Saglam Catalina Spataru Omer Ali Karaman |
| author_facet | Mustafa Saglam Catalina Spataru Omer Ali Karaman |
| author_sort | Mustafa Saglam |
| collection | DOAJ |
| description | This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as <i>R</i><sup>2</sup>, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data. |
| first_indexed | 2024-03-09T13:30:23Z |
| format | Article |
| id | doaj.art-a55b397351af41deabee80bfef45152a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T13:30:23Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a55b397351af41deabee80bfef45152a2023-11-30T21:18:39ZengMDPI AGEnergies1996-10732022-08-011516595010.3390/en15165950Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada IslandMustafa Saglam0Catalina Spataru1Omer Ali Karaman2Energy Institute, Bartlett School Environment, Energy and Resources, University College London, London WC1E 6BT, UKEnergy Institute, Bartlett School Environment, Energy and Resources, University College London, London WC1E 6BT, UKDepartment of Electronic and Automation, Vocational School, Batman University, Batman 72100, TurkeyThis study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as <i>R</i><sup>2</sup>, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data.https://www.mdpi.com/1996-1073/15/16/5950electricity demand forecastparticle swarm optimizationmulti linear regressionartificial neural networks |
| spellingShingle | Mustafa Saglam Catalina Spataru Omer Ali Karaman Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island Energies electricity demand forecast particle swarm optimization multi linear regression artificial neural networks |
| title | Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island |
| title_full | Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island |
| title_fullStr | Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island |
| title_full_unstemmed | Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island |
| title_short | Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island |
| title_sort | electricity demand forecasting with use of artificial intelligence the case of gokceada island |
| topic | electricity demand forecast particle swarm optimization multi linear regression artificial neural networks |
| url | https://www.mdpi.com/1996-1073/15/16/5950 |
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