Saglam, Mustafa;
Spataru, Catalina;
Karaman, Omer Ali;
(2022)
Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island.
Energies
, 15
(16)
, Article 5950. 10.3390/en15165950.
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Abstract
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 R2, 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.
| Type: | Article |
|---|---|
| Title: | Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/en15165950 |
| Publisher version: | https://doi.org/10.3390/en15165950 |
| Language: | English |
| Additional information: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). |
| Keywords: | Science & Technology, Technology, Energy & Fuels, electricity demand forecast, particle swarm optimization, multi linear regression, artificial neural networks, PARTICLE SWARM OPTIMIZATION, NATURAL-GAS CONSUMPTION, ENERGY-CONSUMPTION, NEURAL-NETWORKS, PREDICTION, MACHINE, TURKEY |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10157081 |
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