Duplicate from lavanya251104/AI_BATTERY_OPTIMIZER

Co-authored-by: Lavanya Arora <lavanya251104@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+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:**

ai_battery_optimizer.csv

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+timestamp,battery_percentage,power_usage_mw,time_remaining_min,predicted_app,confidence,brightness,fps
+2025-08-28 12:30:00,85,839,272,Chrome,0.65,60,60
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+2025-08-28 13:45:00,75,923,220,Chrome,0.84,55,60
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+2025-08-28 15:00:00,67,911,159,Gaming,0.95,70,60
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+2025-08-29 00:45:00,9,833,-295,Netflix,0.84,60,30

dataset_info.json

@@ -0,0 +1,53 @@
+{
+  "description": "The AI Battery Optimizer Dataset is a synthetic smartphone battery usage dataset created for research in energy optimization and ML-based app usage prediction.",
+  "citation": "@dataset{teamneuralbattery2025,\n  author = {Aishwarya Singh and Lavanya Arora and Shreya Kathuria and Navya Jain},\n  title = {AI Battery Optimizer Dataset},\n  year = {2025},\n  publisher = {Hugging Face},\n  license = {CC-BY-4.0}\n}",
+  "homepage": "https://huggingface.co/datasets/teamneuralbattery/AI-Battery-Optimizer",
+  "license": "CC-BY-4.0",
+  "features": {
+    "timestamp": {
+      "dtype": "string",
+      "description": "UTC timestamp of the log entry"
+    },
+    "battery_percentage": {
+      "dtype": "int32",
+      "description": "Battery percentage remaining"
+    },
+    "power_usage_mw": {
+      "dtype": "int32",
+      "description": "Power usage in milliwatts"
+    },
+    "time_remaining_min": {
+      "dtype": "int32",
+      "description": "Estimated time remaining in minutes"
+    },
+    "predicted_app": {
+      "dtype": "string",
+      "description": "Predicted app to be opened next"
+    },
+    "confidence": {
+      "dtype": "float32",
+      "description": "Confidence score of the prediction (0\u20131)"
+    },
+    "brightness": {
+      "dtype": "int32",
+      "description": "Screen brightness percentage"
+    },
+    "fps": {
+      "dtype": "int32",
+      "description": "Frame rate setting (30 or 60 FPS)"
+    }
+  },
+  "splits": {
+    "train": {
+      "num_examples": 40,
+      "name": "train"
+    },
+    "test": {
+      "num_examples": 10,
+      "name": "test"
+    }
+  },
+  "size_categories": [
+    "n<1K"
+  ]
+}

test.csv

@@ -0,0 +1,11 @@
+timestamp,battery_percentage,power_usage_mw,time_remaining_min,predicted_app,confidence,brightness,fps
+2025-08-28 14:15:00,72,893,191,Spotify,0.9,65,60
+2025-08-28 15:00:00,67,911,159,Gaming,0.95,70,60
+2025-08-28 16:00:00,61,902,105,Spotify,0.77,50,30
+2025-08-28 17:00:00,56,851,60,Spotify,0.86,60,60
+2025-08-28 17:30:00,53,836,31,Netflix,0.76,65,60
+2025-08-28 18:00:00,50,872,15,Chrome,0.67,60,30
+2025-08-28 19:30:00,39,872,-57,Netflix,0.89,65,30
+2025-08-28 22:00:00,25,909,-173,Netflix,0.81,60,60
+2025-08-28 23:00:00,18,922,-216,Chrome,0.84,65,60
+2025-08-29 00:45:00,9,833,-295,Netflix,0.84,60,30

train (1).csv

@@ -0,0 +1,41 @@
+timestamp,battery_percentage,power_usage_mw,time_remaining_min,predicted_app,confidence,brightness,fps
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+2025-08-28 22:15:00,23,948,-182,Gaming,0.86,55,60
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+2025-08-28 18:45:00,45,859,-21,WhatsApp,0.88,60,30
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+2025-08-28 17:15:00,55,860,45,YouTube,0.87,55,30
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