The efficient and robust design of hybrid energy systems (HES) for rural electrification depends greatly on high-precision electricity load forecasting. The traditional methods seldom detect the high volatility in residential loads, and there is a large gap in acquiring high-precision load profiles for industry-standard design software like HOMER Pro. This research addresses this gap by developing and testing a high-fidelity load forecasting model based on the XGBoost algorithm to generate realistic residential load profiles of a rural town, “Brikcha.” Drawing on fine-grained meteorological and electric consumption data for the Brikcha region, the model utilizes temporal feature engineering and is hyperparameter-tuned using automatic hyperparameter tuning with Optuna. The model, which was obtained after all the runs and experiments, predicted very well; R2 was 0.9919 and MAE was 0.8088 with RMSE being 1.0632. Visual diagnostics further showed that this model was able to track sharp peaks and dynamic fluctuations in energy demands reasonably well. The most significant contribution of this work is a robust, data-driven framework that produces tested, high-accuracy load inputs, providing a building block for the design of more efficient and economically viable hybrid energy systems and advancement in sustainable rural electrification programs.
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