Update README.md

README.md

@@ -1,8 +1,54 @@
 ---
 license: mit
+tags:
+- Decision Transformer
+- Heating-Ventilation-Air Conditionion(HVAC)
+- Docker
+- Energy Plus
+- Generalisation, General Models
+- Transfer Learning and Zero Shot
 ---
+# One for All: LLM guided zero-shot HVAC control
+## Abstract
 
+HVAC controllers are widely deployed across buildings with dif-
+ferent layouts, sensing configurations, climates, and occupancy
+patterns. In practice, controllers tuned for one building often de-
+grade when applied to another, leading to inconsistent energy effi-
+ciency and occupant comfort. Many learning-based HVAC control
+methods rely on building-specific training, retraining, or expert
+intervention, which is often impractical or costly at scale.
+To address these challenges, we present Gen-HVAC, an LLM-
+guided, zero-shot HVAC control platform for multi-zone buildings
+that is trained once and deployed across diverse buildings without
+retraining. We design a transformer-based HVAC controller that
+is trained on historical building operation data collected across
+multiple buildings and generates control actions by conditioning
+on recent system behavior rather than building-specific models.
+By using recent temperature measurements, past control actions,
+and observed system responses, the controller generates HVAC
+control actions that transfer across buildings and climates with-
+out retraining, enabling the same model to scale to new buildings.
+To further improve occupant comfort, we integrate a lightweight
+language model that allows users to specify comfort preferences
+directly, without requiring human expertise, manual rule design,
+or paid external APIs. The system translates these preferences into
+control objectives that guide the controller without interfering with
+system dynamics or real-time control. By conditioning on these
+objectives, the controller switches between operating modes, such
+as energy-focused or comfort-focused behavior.
+We evaluate Gen-HVAC across multiple climates and building
+scenarios using EnergyPlus and validate the system in a real build-
+ing deployment. Results show consistent improvements over rule-
+based control, achieving 36.8% energy savings with 28% comfort
+performance under zero-shot deployment. We also release our plat-
+form to support reproducibility and enable future research on scal-
+able, data-driven HVAC control.
 
+----
+![SolarSys Hierarchical Architecture](assets/solarSys.png)
+
+## System Architecture, Training and Implementation
 We devided this entire project into 4 phases. Please go through each step to use our system.
 
 1) Energy Plus Setup and Trajectory Data generation