Update README.md

README.md

@@ -1,89 +1,91 @@
 ---
 license: cc-by-4.0
+
 task_categories:
 - time-series-forecasting
 - tabular-regression
+
 language:
 - en
+
 tags:
 - solar-energy
 - renewable-energy
-- time-series
 - forecasting
 - machine-learning
-- india
+- time-series
 - climate
 - weather
+- india
+
 size_categories:
 - 1M<n<10M
+
 pretty_name: India Solar Benchmark Dataset
 ---
 
 ![India Solar Benchmark Dataset](https://images.pexels.com/photos/27863809/pexels-photo-27863809.jpeg)
+
 # India Solar Benchmark Dataset
 
-A large-scale solar irradiance forecasting benchmark dataset for India built from NASA POWER meteorological observations and enhanced with temporal, geographical, solar-geometry, physics-informed, lag, and rolling statistical features.
+A large-scale benchmark dataset for solar irradiance forecasting and renewable energy research built from NASA POWER meteorological observations across **50 major Indian cities**.
 
-## Links
+The benchmark contains **10 years of hourly observations (2016–2025)** and is distributed as two complementary datasets:
 
-GitHub Repository:
-https://github.com/Narendersingh007/india-solar-benchmark-dataset
+- **india_multicity_raw.parquet** – cleaned and standardized observations after preprocessing, intended for custom feature engineering and research.
+- **india_multicity_ml_ready.parquet** – fully engineered benchmark containing temporal, geographical, solar geometry, physics-informed, lag, and rolling statistical features.
 
-Kaggle Dataset:
-https://www.kaggle.com/datasets/narendersingh007/india-solar-benchmark-dataset
+The benchmark is designed for reproducible research in renewable energy forecasting, time-series machine learning, feature engineering, and physics-informed AI.
 
-## Dataset Details
+---
 
-### Dataset Description
+# Links
 
-The India Solar Benchmark Dataset contains hourly solar irradiance and meteorological observations collected across 50 major Indian cities from 2016 to 2025.
+| Resource | Link |
+|----------|------|
+| GitHub Repository | https://github.com/Narendersingh007/india-solar-benchmark-dataset |
+| Kaggle Dataset | https://www.kaggle.com/datasets/narendersingh007/india-solar-benchmark-dataset |
 
-The dataset is designed to support:
+---
 
-- Solar irradiance forecasting
-- Renewable energy prediction
-- Time-series forecasting research
-- Deep learning models (LSTM, GRU, Transformer)
-- Tree-based models (XGBoost, LightGBM, CatBoost)
-- Physics-informed machine learning
+# Dataset Details
 
-### Dataset Statistics
+## Dataset Statistics
 
 | Metric | Value |
-|----------|----------|
+|---------|------|
 | Cities | 50 |
 | Years | 2016–2025 |
-| Rows | 4,383,600 |
-| Features | 60 |
 | Frequency | Hourly |
+| Total Records | 4,383,600 |
+| Features (ML Ready) | 60 |
+| Raw Source Files | 500 |
 | Target Variable | ALLSKY_SFC_SW_DWN |
+| Storage Format | Parquet |
 | Source | NASA POWER API |
 
-### Included Files
-
-- `india_multicity_ml_ready.parquet`
-- `train.parquet`
-- `val.parquet`
-- `test.parquet`
-- `sample_preview.csv`
-
-### Curated By
-
-Narender Singh
+---
 
-### License
+## Included Files
 
-CC BY 4.0
+| File | Description |
+|------|-------------|
+| **india_multicity_raw.parquet** | Cleaned benchmark dataset before feature engineering |
+| **india_multicity_ml_ready.parquet** | Fully engineered benchmark dataset |
+| **train.parquet** | Chronological training split |
+| **val.parquet** | Validation split |
+| **test.parquet** | Holdout test split |
+| **00_sample_preview.csv** | Lightweight preview for schema exploration |
 
-### Dataset Sources
+---
 
-**NASA POWER API**
+## Target Variable
 
-https://power.larc.nasa.gov/
+**ALLSKY_SFC_SW_DWN**
 
-**GitHub Repository**
+Hourly **Global Horizontal Irradiance (GHI)** measured in **W/m²**.
 
-https://github.com/Narendersingh007/india-solar-benchmark-dataset
+This represents the total incoming shortwave solar radiation reaching a horizontal surface and serves as the primary prediction target.
 
 ---
 
@@ -91,31 +93,35 @@ https://github.com/Narendersingh007/india-solar-benchmark-dataset
 
 ## Direct Use
 
-This dataset is intended for:
+This benchmark is intended for
 
 - Solar irradiance forecasting
-- Renewable energy generation prediction
-- Time-series forecasting benchmarks
-- Renewable energy research
-- Climate and weather analytics
-- Physics-informed machine learning research
-- Educational and academic projects
+- Renewable energy prediction
+- Photovoltaic power estimation
+- Time-series forecasting research
+- Deep learning (LSTM, GRU, Transformer)
+- Gradient boosting (XGBoost, LightGBM, CatBoost)
+- Feature engineering research
+- Physics-informed machine learning
+- Renewable energy analytics
+
+---
 
 ## Out-of-Scope Use
 
-This dataset should not be used for:
+The benchmark should not be used for
 
-- Safety-critical energy infrastructure decisions
-- Real-time operational forecasting without additional validation
-- Applications requiring precise ground-station measurements
+- Safety-critical operational decisions
+- Grid control without additional validation
+- Applications requiring ground-station precision
 
-NASA POWER data represents modeled and satellite-derived observations and may differ from local sensor measurements.
+NASA POWER observations are satellite-derived and modeled products and may differ from local sensor measurements.
 
 ---
 
 # Dataset Structure
 
-## Train / Validation / Test Split
+## Chronological Split
 
 | Split | Years |
 |---------|---------|
@@ -123,122 +129,77 @@ NASA POWER data represents modeled and satellite-derived observations and may di
 | Validation | 2023 |
 | Test | 2024–2025 |
 
-Chronological splitting is used to prevent temporal leakage and simulate real-world forecasting scenarios.
-
-## Feature Groups
-
-### Weather Features
-Examples:
-- T2M
-- RH2M
-- WS10M
-- PRECTOTCORR
-- PS
-- WD10M
-
-### Temporal Features
-Examples:
-- YEAR
-- MO
-- DY
-- HR
-- WEEKDAY
-- QUARTER
-
-### Geographic Features
-Examples:
-- CITY
-- LATITUDE
-- LONGITUDE
-
-### Solar Geometry Features
-Examples:
-- SOLAR_ZENITH
-- SOLAR_ELEVATION
-- SOLAR_AZIMUTH
-- CLEARSKY_GHI
-- CLEARSKY_DNI
-- CLEARSKY_DHI
-
-### Physics-Informed Features
-Examples:
-- TEMP_HUMIDITY
-- DEWPOINT_SPREAD
-- WIND_POWER
-- PRESSURE_TEMP
-
-### Lag Features
-Examples:
-- lag_1
-- lag_3
-- lag_6
-- lag_12
-- lag_24
-- lag_168
-
-### Rolling Features
-Examples:
-- rolling_mean_24
-- rolling_std_24
-- rolling_mean_168
-- rolling_max_24
-- rolling_min_24
+Chronological splitting prevents temporal leakage and reflects real-world forecasting workflows.
+
+---
+
+## Feature Categories
+
+The ML-ready benchmark contains engineered features grouped into
+
+- Weather variables
+- Temporal features
+- Cyclical encodings
+- Geographic metadata
+- Solar geometry (PVLIB)
+- Physics-informed variables
+- Lag features
+- Rolling statistical features
 
 ---
 
 # Dataset Creation
-## Curation Rationale
 
-India lacks publicly available large-scale benchmark datasets for solar irradiance forecasting covering multiple cities and long time horizons.
-This dataset was created to provide a standardized benchmark for machine learning and renewable energy research in India.
+## Motivation
 
-## Source Data
-The dataset is derived from NASA POWER meteorological and solar radiation observations.
+Public benchmark datasets for large-scale solar irradiance forecasting across India are limited.
 
-### Data Collection and Processing
-Pipeline:
+This benchmark was created to provide a standardized, reproducible dataset for machine learning, renewable energy, and forecasting research.
 
-NASA POWER API
+---
 
-→ Hourly data collection
-→ Multi-city aggregation
-→ Data cleaning
-→ Feature engineering
-→ Solar geometry computation (PVLIB)
-→ Physics-informed feature generation
-→ Lag and rolling feature creation
-→ Chronological train/validation/test splits
-### Source Data Producers
+## Source Data
 
-Original observations are provided by the NASA POWER project.
-https://power.larc.nasa.gov/
+Meteorological and solar radiation observations were collected from the **NASA POWER API**.
+
+Solar geometry and clear-sky irradiance variables were generated using **PVLIB**.
 
 ---
 
-# Personal and Sensitive Information
+## Data Processing Pipeline
+
+The benchmark generation pipeline performs
 
-This dataset contains no personal, private, or sensitive information.
-The dataset consists solely of environmental, meteorological, geographic, and solar radiation measurements.
+1. NASA POWER data collection
+2. Multi-city aggregation
+3. Data cleaning and validation
+4. Benchmark raw dataset generation
+5. Temporal feature engineering
+6. Solar geometry computation
+7. Physics-informed feature generation
+8. Lag feature creation
+9. Rolling statistics computation
+10. ML-ready benchmark generation
+11. Train / Validation / Test splitting
+12. Metadata and validation report generation
 
 ---
 
-# Bias, Risks, and Limitations
+# Bias, Risks and Limitations
 
-Potential limitations include:
+Potential limitations include
 
-- Dependence on NASA POWER modeled observations
-- Geographic representation limited to selected Indian cities
-- Possible discrepancies relative to local weather stations
-- Forecasting performance may vary across climatic regions
+- NASA POWER modeled observations rather than ground-station measurements
+- Coverage limited to selected Indian cities
+- Regional climate variability
+- Forecast performance depending on downstream modeling choices
 
-Users should validate models independently before operational deployment.
+Users should independently validate models before operational deployment.
 
 ---
 
 # Citation
 
-## BibTeX
-
 ```bibtex
 @dataset{india_solar_benchmark_dataset,
   title={India Solar Benchmark Dataset},
@@ -249,16 +210,13 @@ Users should validate models independently before operational deployment.
 }
 ```
 
-## APA
-
-Singh, N. (2026). India Solar Benchmark Dataset. Hugging Face.
-
 ---
 
 # Acknowledgements
 
-This dataset is built using meteorological and solar radiation data provided by NASA POWER.
-Solar geometry and clear-sky calculations were generated using PVLIB.
+This benchmark is built using meteorological and solar radiation observations provided by the NASA POWER project.
+
+Solar geometry and clear-sky irradiance variables were generated using PVLIB.
 
 - NASA POWER: https://power.larc.nasa.gov/
 - PVLIB: https://pvlib-python.readthedocs.io/
@@ -270,8 +228,12 @@ Solar geometry and clear-sky calculations were generated using PVLIB.
 ```python
 import pandas as pd
 
+# ML-ready benchmark
 df = pd.read_parquet("india_multicity_ml_ready.parquet")
 
+# Raw benchmark
+raw_df = pd.read_parquet("india_multicity_raw.parquet")
+
 print(df.shape)
-print(df.head())
+print(raw_df.shape)
 ```