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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

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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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.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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