---
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  

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

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## Personal and Sensitive Information
- Dataset is **synthetic**  
- No personal or sensitive user data included  

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## 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:**