Smart Building Recommendations with LLMs: A Semantic Comparison Approach
The increasing need for sustainable energy management in smart buildings calls for cost-effective solutions that balance energy efficiency and occupant comfort. This article presents a Large Language Model (LLM)-based recommendation system capable of generating proactive, context-aware suggestions f...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/13/2303 |
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| Summary: | The increasing need for sustainable energy management in smart buildings calls for cost-effective solutions that balance energy efficiency and occupant comfort. This article presents a Large Language Model (LLM)-based recommendation system capable of generating proactive, context-aware suggestions from dynamic building conditions. The system was trained on a combination of real-world data and Sinergym simulations, capturing inputs such as weather conditions, forecasts, energy usage, electricity prices, and detailed zone parameters. Five models were fine-tuned and evaluated: GPT-2-Small, GPT-2-Medium, DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, and GPT-4. To enhance evaluation precision, a novel metric, the Zone-Aware Semantic Reward (ZASR), was developed, combining Sentence-BERT with zone-level scoring and complemented by F1-Score metrics. While GPT-4 demonstrated strong performance with minimal data, its high inference cost limits scalability. In contrast, open-access models like DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, and GPT-2-Medium required larger datasets but matched or exceeded GPT-4’s performance at significantly lower cost. The system demonstrated adaptability across diverse building types, supported by heterogeneous datasets and parameter normalization. Importantly, the system was also deployed in a real-world multi-zone residential building in Thessaloniki, Greece. During a two-week operational period under near-identical weather and occupancy conditions, the model-assisted recommendations contributed to an estimated 10% reduction in electricity consumption, showcasing the practical potential of LLM-based recommendations in live building environments. |
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| ISSN: | 2075-5309 |