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Datasets:
lavanya251104
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AI_BATTERY_OPTIMIZER

Tasks:
Time Series Forecasting
Modalities:
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Text
Formats:
csv
Languages:
English
Size:
< 1K
Tags:
battery
smartphone
energy
optimization
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metadata
license: cc-by-4.0
task_categories:
  - time-series-forecasting
  - classification
language:
  - en
tags:
  - battery
  - smartphone
  - energy
  - optimization
pretty_name: AI Battery Optimizer Dataset
size_categories:
  - n<1K

Dataset Card for AI Battery Optimizer

The AI Battery Optimizer Dataset contains synthetic smartphone battery usage logs created during the development of the AI Battery Optimizer App.
It is intended for research and experimentation on battery prediction, app usage forecasting, and adaptive resource management.

Dataset Details

Dataset Description

  • Curated by (Team):

    • Aishwarya Singh
    • Lavanya Arora
    • Shreya Kathuria
    • Navya Jain

  • Funded by: Self / Academic Project

  • Shared by: Team NeuralBattery

  • Language(s): English (column headers, labels)

  • License: Creative Commons Attribution 4.0 (CC BY 4.0)

This dataset logs:

  • Battery percentage over time
  • Power usage (mW)
  • Estimated time remaining
  • Predicted app usage with confidence score
  • Screen brightness level
  • Frame rate (FPS)

Dataset Sources

Uses

Direct Use

  • Training time-series models (Chronos, TBATS, PatchTSMixer) for predicting battery drain
  • Evaluating ML-based app usage predictions
  • Research on energy optimization in smartphones
  • Simulating adaptive energy-saving systems

Out-of-Scope Use

  • Real-world personal battery health monitoring
  • Any application requiring sensitive/private user data (dataset is synthetic)

Dataset Structure

Format: CSV / JSON

Fields:

  • timestamp → Log time (UTC)
  • battery_percentage → Battery level (%)
  • power_usage_mw → Power consumption in milliwatts
  • time_remaining_min → Estimated time left (minutes)
  • predicted_app → Next app predicted (e.g. Instagram, YouTube)
  • confidence → ML prediction confidence score (0–1)
  • brightness → Screen brightness (%)
  • fps → Frame rate setting

Example Row:

timestamp battery_percentage power_usage_mw time_remaining_min predicted_app confidence brightness fps
2025-08-28 12:30:00 85 850 272 Instagram 0.87 75 60

Dataset Creation

Curation Rationale

Battery drain is influenced by app usage, FPS, brightness, and background processes.
This dataset was created to simulate realistic smartphone usage patterns for developing an ML-driven energy optimization system.

Source Data

  • Synthetic logs generated during AI Battery Optimizer app simulations
  • Inspired by real smartphone usage, but fully anonymized

Data Collection and Processing

  • Battery drain simulated every 30s via backend API
  • App predictions generated every 15s with probabilistic ML logic
  • Logs normalized into CSV format for training

Annotations

  • Predictions contain confidence scores
  • Users can validate predictions inside the app (feedback loop)
  • Dataset can be extended with these feedback labels

Personal and Sensitive Information

  • Dataset is synthetic
  • No personal or sensitive user data included

Bias, Risks, and Limitations

  • Synthetic dataset may not capture all real-world battery usage variability
  • Predictions are approximations, not exact reflections of real device usage
  • Should be treated as a benchmark/simulation dataset

Recommendations

  • Use this dataset for prototyping and model training
  • Fine-tune with real anonymized battery logs for production apps

Citation

BibTeX: