| --- |
| 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 |
| - **Repository:** Hugging Face Dataset Repo – AI Battery Optimizer |
| - **Related Project:** [AI Battery Optimizer App](https://huggingface.co/) |
|
|
| --- |
|
|
| ## 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:** |
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|
