Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence
The most important feature of load forecasting is enabling the building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has increased during the COVID-19 period, as compared to normal times. Therefore, the perfo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1292183/full |
| _version_ | 1827387764522352640 |
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| author | S. M. Mahfuz Alam Mohd. Hasan Ali |
| author_facet | S. M. Mahfuz Alam Mohd. Hasan Ali |
| author_sort | S. M. Mahfuz Alam |
| collection | DOAJ |
| description | The most important feature of load forecasting is enabling the building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has increased during the COVID-19 period, as compared to normal times. Therefore, the performance of forecasting methods is impacted, and further tuning of parameters is required to cope with energy consumption changes due to COVID-19. This paper proposes a new adaptive neuro-fuzzy 2 inference system (ANFIS2) for energy usage forecasting in residential buildings for both normal and COVID-19 periods. The particle swarm optimization (PSO) method has been implemented for parameter optimization, and subtractive clustering is used for data training for the proposed ANFIS2 system. Two modifications in terms of input and parameters of ANFIS2 are made to cope with the change in the consumption pattern and reduce the prediction errors during the COVID-19 period. Simulation results obtained by MATLAB software validate the efficacy of the proposed ANFIS2 in residential load forecasting during both normal and COVID-19 periods. Moreover, the performance of the proposed method is better than that of the existing adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), and random forest (RF) approaches. |
| first_indexed | 2024-03-08T16:08:01Z |
| format | Article |
| id | doaj.art-0a32a3f688894caeae0d280979a56c6e |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-08T16:08:01Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-0a32a3f688894caeae0d280979a56c6e2024-01-08T04:32:29ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-01-011110.3389/fenrg.2023.12921831292183Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influenceS. M. Mahfuz Alam0Mohd. Hasan Ali1Department of EEE, Dhaka University of Engineering and Technology, Gazipur, BangladeshDepartment of ECE, The University of Memphis, Memphis, TN, United StatesThe most important feature of load forecasting is enabling the building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has increased during the COVID-19 period, as compared to normal times. Therefore, the performance of forecasting methods is impacted, and further tuning of parameters is required to cope with energy consumption changes due to COVID-19. This paper proposes a new adaptive neuro-fuzzy 2 inference system (ANFIS2) for energy usage forecasting in residential buildings for both normal and COVID-19 periods. The particle swarm optimization (PSO) method has been implemented for parameter optimization, and subtractive clustering is used for data training for the proposed ANFIS2 system. Two modifications in terms of input and parameters of ANFIS2 are made to cope with the change in the consumption pattern and reduce the prediction errors during the COVID-19 period. Simulation results obtained by MATLAB software validate the efficacy of the proposed ANFIS2 in residential load forecasting during both normal and COVID-19 periods. Moreover, the performance of the proposed method is better than that of the existing adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), and random forest (RF) approaches.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1292183/fullAdaptive neuro-fuzzy 2 inference systemCOVID-19load forecastingresidential loadparticle swarm optimization |
| spellingShingle | S. M. Mahfuz Alam Mohd. Hasan Ali Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence Frontiers in Energy Research Adaptive neuro-fuzzy 2 inference system COVID-19 load forecasting residential load particle swarm optimization |
| title | Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence |
| title_full | Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence |
| title_fullStr | Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence |
| title_full_unstemmed | Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence |
| title_short | Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence |
| title_sort | residential load forecasting by a pso tuned anfis2 method considering the covid 19 influence |
| topic | Adaptive neuro-fuzzy 2 inference system COVID-19 load forecasting residential load particle swarm optimization |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1292183/full |
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