Analysis of the Impact of Clustering Techniques and Parameters on Evolutionary-Based Hybrid Models for Forecasting Electricity Consumption
Electricity is undeniably one of the most crucial building blocks of high-quality life all over the world. Like many other African countries, Nigeria is still grappling with the challenge of the energy crisis. However, accurate prediction of electricity consumption is vital for the operation of elec...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10209173/ |
| _version_ | 1797742962339217408 |
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| author | Stephen Oyewumi Oladipo Yanxia Sun Abraham Olatide Amole |
| author_facet | Stephen Oyewumi Oladipo Yanxia Sun Abraham Olatide Amole |
| author_sort | Stephen Oyewumi Oladipo |
| collection | DOAJ |
| description | Electricity is undeniably one of the most crucial building blocks of high-quality life all over the world. Like many other African countries, Nigeria is still grappling with the challenge of the energy crisis. However, accurate prediction of electricity consumption is vital for the operation of electric utility companies and policymakers. In response, this study underlines the application of hybrid modelling techniques for the accurate prediction of electricity consumption, using Lagos districts, Nigeria, as a case study. To begin with, this research investigates the performance of three evolutionary algorithms — Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) — to optimize the parameters of adaptive network-based fuzzy inference systems (ANFIS). In addition, the impact of renowned clustering techniques such as grid partitioning (GP), fuzzy c-means (FCM), and subtractive clustering (SC) on other pivotal key hyperparameters of the ANFIS was examined and analyzed. Furthermore, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models that are based on six different variants of PSO. The efficacy of the proposed model was evaluated using four standard statistical measures. Finally, the results showed that the combination of the ANFIS approach and PSO under an SC approach and clustering radius of 0.6 delivered the best forecast scheme with the highest accuracy of the MAPE (8.8418%), the MAE (872.1784), the CVRMSE (10.7895), and the RMSE (1.0945E+03). The simulation results were analyzed and compared to other approaches, revealing that the suggested model is better. |
| first_indexed | 2024-03-12T14:47:44Z |
| format | Article |
| id | doaj.art-4d92775f3ce946a4a5a10942e0176f69 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:47:44Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4d92775f3ce946a4a5a10942e0176f692023-08-15T23:01:32ZengIEEEIEEE Access2169-35362023-01-0111828388285610.1109/ACCESS.2023.330225210209173Analysis of the Impact of Clustering Techniques and Parameters on Evolutionary-Based Hybrid Models for Forecasting Electricity ConsumptionStephen Oyewumi Oladipo0https://orcid.org/0000-0003-0444-7929Yanxia Sun1https://orcid.org/0000-0002-3455-9625Abraham Olatide Amole2Department of Electrical and Electronic Engineering, University of Johannesburg, Johannesburg, South AfricaDepartment of Electrical and Electronic Engineering, University of Johannesburg, Johannesburg, South AfricaDepartment of Electrical, Electronics and Telecommunication Engineering, College of Engineering, Bells University of Technology, Ota, NigeriaElectricity is undeniably one of the most crucial building blocks of high-quality life all over the world. Like many other African countries, Nigeria is still grappling with the challenge of the energy crisis. However, accurate prediction of electricity consumption is vital for the operation of electric utility companies and policymakers. In response, this study underlines the application of hybrid modelling techniques for the accurate prediction of electricity consumption, using Lagos districts, Nigeria, as a case study. To begin with, this research investigates the performance of three evolutionary algorithms — Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) — to optimize the parameters of adaptive network-based fuzzy inference systems (ANFIS). In addition, the impact of renowned clustering techniques such as grid partitioning (GP), fuzzy c-means (FCM), and subtractive clustering (SC) on other pivotal key hyperparameters of the ANFIS was examined and analyzed. Furthermore, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models that are based on six different variants of PSO. The efficacy of the proposed model was evaluated using four standard statistical measures. Finally, the results showed that the combination of the ANFIS approach and PSO under an SC approach and clustering radius of 0.6 delivered the best forecast scheme with the highest accuracy of the MAPE (8.8418%), the MAE (872.1784), the CVRMSE (10.7895), and the RMSE (1.0945E+03). The simulation results were analyzed and compared to other approaches, revealing that the suggested model is better.https://ieeexplore.ieee.org/document/10209173/Particle swarm optimization (PSO)genetic algorithm (GA)differential evolution (DE)adaptive network-based fuzzy inference systems (ANFIS)clustering technique |
| spellingShingle | Stephen Oyewumi Oladipo Yanxia Sun Abraham Olatide Amole Analysis of the Impact of Clustering Techniques and Parameters on Evolutionary-Based Hybrid Models for Forecasting Electricity Consumption IEEE Access Particle swarm optimization (PSO) genetic algorithm (GA) differential evolution (DE) adaptive network-based fuzzy inference systems (ANFIS) clustering technique |
| title | Analysis of the Impact of Clustering Techniques and Parameters on Evolutionary-Based Hybrid Models for Forecasting Electricity Consumption |
| title_full | Analysis of the Impact of Clustering Techniques and Parameters on Evolutionary-Based Hybrid Models for Forecasting Electricity Consumption |
| title_fullStr | Analysis of the Impact of Clustering Techniques and Parameters on Evolutionary-Based Hybrid Models for Forecasting Electricity Consumption |
| title_full_unstemmed | Analysis of the Impact of Clustering Techniques and Parameters on Evolutionary-Based Hybrid Models for Forecasting Electricity Consumption |
| title_short | Analysis of the Impact of Clustering Techniques and Parameters on Evolutionary-Based Hybrid Models for Forecasting Electricity Consumption |
| title_sort | analysis of the impact of clustering techniques and parameters on evolutionary based hybrid models for forecasting electricity consumption |
| topic | Particle swarm optimization (PSO) genetic algorithm (GA) differential evolution (DE) adaptive network-based fuzzy inference systems (ANFIS) clustering technique |
| url | https://ieeexplore.ieee.org/document/10209173/ |
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