Adept Domestic Energy Load Profile Development Using Computational Intelligence-Based Modelling

Abstract

Most studies undertaken on energy usage in buildings have shown that energy utilization is widely influenced 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 proficient estimation of energy is the modelling of occupant behavior relative to energy use. Occupants’ behavior is a complex phenomenon and has a dynamic nature influenced by numerous internal, individual, and circumstantial factors. This research proposes a computational intelligence-based model for household electricity usage profile development as impacted by core input variables—household activities, household financial status, and occupancy presence. The incorporation of these variables and their adaptiveness is expected to address and resolve unpredictability or nonlinearity concerns, thus allowing for adept energy usage estimation. The model addresses issues unresolved in many other studies, such as occupancy determination (deduction) and the impact on energy consumption. The 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 proficient predictive output with respect to the metered (actual) energy usage data. The 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. The 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. This will translate into power system reliability, reduce operation cost (lowest cost), and reduce greenhouse emissions (environmental pollution), thereby cumulating into sustainable cities.