Model Card for Model ID
Model Details
Model Description
This model is a Generative AI-powered energy optimization system designed to provide personalized recommendations for household electricity usage. It integrates IoT-based real-time monitoring, machine learning forecasting, and generative AI (LLMs) to deliver actionable suggestions through a web application.
- Developed by: Welikalage R.Y.W., Sri Lanka Institute of Information Technology
- Funded by [optional]: Self-funded (with hardware + AWS cloud expenses)
- Shared by [optional]: Fine-tuned Transformer-based Large Language Model (LLM)
- Model type: Fine-tuned Transformer-based Large Language Model (LLM)
- Language(s) (NLP): English
- License: [More Information Needed]
- Finetuned from model [optional]: Pre-trained Hugging Face Transformer LLM (domain-specific fine-tuning)
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: An Intelligent Electricity Management Unit: AI-Driven Power Forecasting and Personalized Consumption Insights with Application Integration (Project Proposal, 2025)
- Demo [optional]: [More Information Needed]
Uses
Personalized energy-saving suggestions for household appliances
IoT-based real-time electricity consumption tracking
Forecasting energy usage with time-series ML models
Gamified dashboards to visualize energy savings
Direct Use
Integration with smart home systems and utility providers
Extension into sustainability-focused apps
Used in research for energy efficiency and behavioral insights
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
