This exploratory case study presents a high-resolution, data-driven comparison of appliance usage behaviors between a rural (off-grid solar) and an urban (grid-connected) Malaysian household. Integrating minute-level logger data with Bottom-Up Load Modeling (BULM), state-based probability analysis, and Statistical Parameter Index (SPI), this multi-layered framework captures the temporal and probabilistic dimensions of residential energy demand. Results demonstrate that rural households prioritize essential appliances with lower operational variability to prevent solar battery depletion. Notably, rural fan utilization exhibits a significantly higher frequency (3.77 times/day) and energy consumption per cycle than the urban counterpart due to the absence of air conditioning. Conversely, urban households operate a diverse array of high-power devices; air conditioning alone accounts for 32.71% of total daily energy, driving intense peak periods. These quantified behavioral disparities directly inform targeted energy planning: optimizing solar-battery sizing and demand-side management for rural constraints, and implementing peak demand-shifting strategies for urban thermal loads. Ultimately, this framework provides preliminary insights for equitable residential energy policy, accurate load forecasting, and precise Life Cycle Assessments in diverse infrastructural contexts.
Xu et al. (Thu,) studied this question.