| --- |
| base_model: Qwen/Qwen2.5-7B-Instruct |
| library_name: peft |
| license: apache-2.0 |
| tags: |
| - text-generation |
| - timeseriesdatabase |
| - energy |
| - qwen |
| --- |
| |
| Energy-Intelligence: The Autonomous Energy Analyst |
| ================================================== |
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| <Gallery /> |
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| Model Overview |
| -------------- |
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| **Energy-Intelligence** is a hyper-specialized, fine-tuned large language model engineered to serve as the "Cognitive Core" for industrial electrical monitoring systems. Unlike general-purpose AI, this model is natively fluent in the physics, economics, and regulatory frameworks of the Energy & Utilities sector. |
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| It functions as an **Expert Energy Auditor**, capable of processing massive streams of time-series data to provide high-level behavioral insights, stability reports, and compliance audits with zero human intervention. |
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| 🚀 Key Intelligence Features |
| ---------------------------- |
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| ### 1\. Autonomous Energy Analytics & Pattern Recognition |
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| The model doesn't just process numbers; it interprets the "heartbeat" of an electrical system. |
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| - **Behavioral Profiling:** Identifies operational signatures across Main and Sub-meter hierarchies. |
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| - **Load Analysis:** Dynamically calculates consumption patterns and differentiates between base-load and peak-demand fluctuations. |
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| - **Thermal Correlation:** Maps environmental temperature data against electrical performance to detect equipment stress and cooling inefficiencies. |
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| ### 2\. Deep Domain Expertise & Regulatory Logic |
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| The engine is pre-loaded with a comprehensive "Knowledge Vault" of electrical standards: |
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| - **Power Quality Auditing:** Native assessment of Voltage stability against **IS12360 standards** (±6% fluctuation logic). |
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| - **Phase Symmetry:** Monitors R-Y-B phase balance to ensure distribution efficiency and prevent neutral current overloads. |
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| - **CIM Standard Integration:** Operates using the **Common Information Model (CIM)**, ensuring seamless integration with modern Smart Grid architectures. |
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| ### 3\. Precision Reporting & Peak Demand Intelligence |
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| The model is specifically tuned for the Indian Energy Market and global industrial standards: |
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| - **Peak Hour Optimization:** Automatically identifies and highlights inefficiencies occurring during Morning (07:30--09:30) and Evening (17:30--19:30) IST peak windows. |
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| - **Expert Insights:** Transforms raw electrical metrics into "Actionable Intelligence," such as identifying power factor degradation or potential insulation failures before they become critical. |
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| 📊 Structural Understanding: The SLD Hierarchy |
| ---------------------------------------------- |
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| The model possesses a built-in mental map of industrial electrical hierarchies, allowing it to navigate complex infrastructures like a lead engineer: |
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| Plaintext |
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| ``` |
| Cotspun Textile Private Limited |
| | |
| +-------------------------------------+ |
| | EquipmentRoom |--- Environmental Monitoring (Temp) |
| | +-------------------------------+ | |
| | | Main Meter (ID: 1.1) | | |
| | | |- AC Sub-Meter (ID: 1.2) | | |
| | | |- UPS 1 Sub-Meter (ID: 1.3) | | |
| | | |- UPS 2 Sub-Meter (ID: 1.4) | | |
| | +-------------------------------+ | |
| +-------------------------------------+ |
| |
| ``` |
| ## Methodology of Training |
| ---------------------------------------------------- |
| To achieve high-fidelity reasoning in a compact 7B parameter footprint, Energy-Intelligence was developed through a **Distillation & RLHF Architecture**: |
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| 1. **RLHF (Reinforcement Learning from Human Feedback):** |
| Human evaluators review multiple responses generated by the model and select the better one. The model improves based on these preferences, making it more accurate, helpful, and aligned with real-world expectations. |
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| 2. **Synthetic Data Generation:** |
| We utilized synthetic data generated by the Teacher model to capture domain knowledge and real-world scenarios, enabling scalable training with improved accuracy and coverage of complex use cases. |
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| 3. **Distillation:** |
| - **The Oracle (Teacher):** We utilized Gemini Pro as a high-parameter teacher model, providing it with domain knowledge, business logic, and complex system understanding to generate high-quality learning data. |
| - **The Specialist (Student):** The Qwen2.5-7B-Instruct base model was fine-tuned on this curated dataset, effectively capturing the Teacher’s advanced reasoning in a more efficient form. |
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| 4. **The Result:** |
| A model that possesses the intelligence of a much larger AI system while operating with the speed and cost-efficiency required for real-time industrial monitoring and analytics. |
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| ---------------------------------------------------- |
| ### Why Adding RLHF Matters for the Model Card |
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| - **Precision:** The model is refined using human feedback, improving the quality and reliability of responses. |
| - **Domain Safety:** Reduces the risk of incorrect outputs that could impact critical energy operations. |
| - **Human Alignment:** Ensures the model behaves in a helpful, consistent, and context-aware manner aligned with human expectations. |
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| Our methodology focuses on embedding the intelligence of large-scale systems into a compact and efficient architecture: |
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| - By distilling knowledge from a high-parameter Teacher into a 7B model, we significantly reduce computational requirements without sacrificing reasoning capability. |
| - The approach captures the **brains of domain experts**, built upon decades of domain expertise and engineering practices. |
| - Optimized training and alignment ensure that the model delivers high accuracy with minimal resource consumption. |
| - This enables deployment on cost-efficient infrastructure, including edge environments, while maintaining enterprise-grade performance. |
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| 📥 Getting Started |
| ------------------ |
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| The weights for the **Energy-Intelligence** engine are available in the **Files & versions** tab. This model is ready for deployment in RAG pipelines, automated energy reporting dashboards, and real-time anomaly detection systems. |
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| ## Training Code Repository |
| ---------------------------------------------------- |
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| The complete training pipeline, including data preparation, fine-tuning, and optimization workflows, is available in the following GitHub repository: |
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| 🔗 https://github.com/Savaliya03/Architecting-an-Energy-Intelligence-LLM-via-PEFT-Optimization |
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| This repository provides implementation details of the model architecture, showcasing how Parameter Efficient Fine-Tuning (PEFT) techniques are used to reduce computational cost while maintaining high performance. |
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