Power demand is increasing in all sectors of the developing nations; however

the inadequate and disproportionate generation and distribution resources create a burden for the authorities to efficiently manage the power grid. This results in network overloading and consequent blackouts. To efficiently manage the resources

short-term consumption modeling (using physical or data-based models)is mandatory. Physical models

in contrast to the data-based models

are generally complex and difficult to be used for automatic control strategies for the grid. However

for the latter

there is generally a lack of data availability in the developing nations

as energy measurements are taken just once a month for billing purposes. A recent study conducted consumption measurements

at a high sampling rate

of 42 residential houses from different neighborhoods in Lahore

Pakistan. As mentioned earlier

such a dataset is rare in the developing nations

therefore

leverage the availability of this data set and perform SARIMA (seasonal autoregressive integrated moving average)and ARIMA (autoregressive integrated moving average) based time series modelling of the consumption. Furthermore

for a wider applicability of the results

this work also includes clustering of the houses based on the parameters such as power consumption in different seasons

number of occupants

structural attributes

demographics

and household appliances.Hierarchical clustering with complete linkage was used to derive household groups

Euclidean distance was applied as the similarity metric

and silhouette analysis validated cluster compactness and separation.We report models of different groups of residential houses

categorized with respect to the aforementioned parameters

to facilitate the operators for efficient power management.The findings in this paper are supported by a variety of simulation results

across 41 households

the dataset was split into training and testing sets

corresponding to 93-7% for the 24-h horizon and 82-18% for the 72-h horizon

which showed that SARIMA consistently outperformed ARIMA

reducing errors for 24-h (MAE: 0.30 →0.23 kW

RMSE: 0.40 →0.32 kW

MAPE: 36.9 →28.3%) and 72-h horizons (MAE: 0.39 →0.32 kW

RMSE: 0.51 →0.41 kW

MAPE:49.1 →39.54%) and yielding lower AICc values. Clustering validation using silhouette widths (0.13-0.25) confirmed the robustness of identified household groups.