Update README.md

README.md

@@ -10,7 +10,7 @@ tags:
 ---
 # SolarSys: Scalable Hierarchical Coordination for Distributed Solar Energy
 
-SolarSys is a novel **Hierarchical Multi-Agent Reinforcement Learning (HRL)** system designed to manage energy storage and peer-to-peer (P2P) trading across large communities of solar-equipped residences[cite: 10]. This repository contains the full source code for the SolarSys system, including the trained policies, the custom Gym environment, and the hierarchical diffusion model used for data augmentation.
+SolarSys is a novel **Hierarchical Multi-Agent Reinforcement Learning (HRL)** system designed to manage energy storage and peer-to-peer (P2P) trading across large communities of solar-equipped residences. This repository contains the full source code for the SolarSys system, including the trained policies, the custom Gym environment, and the hierarchical diffusion model used for data augmentation.
 
 ---
 
@@ -19,8 +19,8 @@ SolarSys is a novel **Hierarchical Multi-Agent Reinforcement Learning (HRL)** sy
 
 The core of SolarSys is a two-level decision hierarchy:
 
-1.  [cite_start]**Low-Level (Intra-Cluster):** Individual households use a **MAPPO** agent to make instantaneous decisions (charge, discharge, local P2P trade, grid trade) based on local meter readings and price signals[cite: 13, 313].
-2.  [cite_start]**High-Level (Inter-Cluster):** Cluster Managers use a **Mean-Field** policy to coordinate bulk energy transfers between clusters, ensuring the overall system remains balanced against grid constraints[cite: 14, 314].
+1.  **Low-Level (Intra-Cluster):** Individual households use a **MAPPO** agent to make instantaneous decisions (charge, discharge, local P2P trade, grid trade) based on local meter readings and price signals.
+2.  **High-Level (Inter-Cluster):** Cluster Managers use a **Mean-Field** policy to coordinate bulk energy transfers between clusters, ensuring the overall system remains balanced against grid constraints.
 
 
 
@@ -28,10 +28,10 @@ The core of SolarSys is a two-level decision hierarchy:
 
 ##  Data Generation Framework
 
-[cite_start]To enable large-scale simulation with realistic temporal dynamics, SolarSys includes a **Hierarchical Diffusion Model** for generating synthetic, long-duration energy profiles that maintain both long-term (seasonal/monthly) and short-term (daily/hourly) characteristics[cite: 254, 255].
+To enable large-scale simulation with realistic temporal dynamics, SolarSys includes a **Hierarchical Diffusion Model** for generating synthetic, long-duration energy profiles that maintain both long-term (seasonal/monthly) and short-term (daily/hourly) characteristics.
 
-* [cite_start]**Model:** Hierarchical Diffusion U-Net [cite: 254, 255]
-* [cite_start]**Input:** Household ID and Day-of-Year conditioning [cite: 256]
+* **Model:** Hierarchical Diffusion U-Net
+* **Input:** Location based housing dataset(eg. Ausgrid dataset, newyork dataset)
 * **Output:** High-resolution time series for Grid Usage and Solar Generation (kWh).