Integrasi IndoBERT dan NSGA-II untuk Penjadwalan HEMS Berbasis Preferensi Pengguna
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1309Keywords:
HEMS; IndoBERT; NSGA-II; Natural Language Processing; Energy Scheduling; Solar Panels; Pre-trained Feature ExtractorAbstract
The increasing use of Internet of Things (IoT) based devices in smart homes is driving the need for a Home Energy Management System (HEMS) capable of optimal and adaptive energy scheduling. However, most HEMS systems still use statistical user preference parameters, thus lacking the ability to understand natural language user instructions. This research proposes the integration of IndoBERT and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate HEMS device scheduling based on Indonesian language user preferences. The research method utilizes user preferences using the IndoBERT indobenchmark/indobert-base-p1 model as a pre-trained feature extractor to identify intents and entities such as devices, operating times, priorities, reminders, and environmental conditions. The extracted results are then converted into preference vectors, comfort weights, and dynamic constraints as a modification of the NSGA-II optimization. To mitigate the impact of friction in the NLP extraction results, the IndoBERT output is processed using a confidence-based weighting mechanism before being used in optimization. The IndoBERT model evaluation results showed Accuracy, Precision, Recall, and F1-Score values ??of 70.49%, with user-level validation reaching 93%. The IndoBERT–NSGA-II integration was able to produce HEMS scheduling that considers energy efficiency, photovoltaic energy utilization, and user comforts.
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