Popoola, Olawale and Ramokone, Agnes and Awelewa, A. A. (2024) Adept Domestic Energy Load Profile Development Using Computational Intelligence-Based Modelling. International Transactions on Electrical Energy Systems.
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Abstract
Most studies undertaken on energy usage in buildings have shown that energy utilization is widely in0uenced by occupancy presence and occupants’ activities relative to the indoor environment, which may be widely dependent on weather conditions and user behaviors. However, the core drawback that has negated the pro3cient estimation of energy is the modelling of occupant behavior relative to energy use. Occupants’ behavior is a complex phenomenon and has a dynamic nature in0uenced by numerous internal, individual, and circumstantial factors. ,is research proposes a computational intelligence-based model for household electricity usage pro3le development as impacted by core input variables—household activities, household 3nancial status, and occupancy presence. ,e incorporation of these variables and their adaptiveness is expected to address and resolve unpredictability or nonlinearity concerns, thus allowing for adept energy usage estimation. ,e model addresses issues unresolved in many other studies, such as occupancy determination (deduction) and the impact on energy consumption. ,e performance precision of this approach has been demonstrated by trend series analysis, demand analysis, and correlation analysis. Based on the performance indicators including mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE), the model has shown pro3cient predictive output with respect to the metered (actual) energy usage data. ,e proposed model, compared to actual data, showed that average MAPE values for the respective day standard, morning peak, and night peak demand period (TOUs) are 2.8%, 1.88%, and 0.31% for all income groups, respectively. ,e aptitude to improve on energy prediction and evaluation accuracy, especially in these periods, makes it a highly suited tool for demand-side management, power generation, and distribution planning activity. ,is will translate into power system reliability, reduce operation cost (lowest cost), and reduce greenhouse emissions (environmental pollution), thereby cumulating into sustainable cities.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Patricia Nwokealisi |
| Date Deposited: | 27 Nov 2024 16:22 |
| Last Modified: | 27 Nov 2024 16:22 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/18625 |
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