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README.md

@@ -1,28 +1,164 @@
 ---
 license: mit
+language:
+  - en
 tags:
   - sklearn
   - solar-energy
   - time-series
   - regression
+  - random-forest
+  - energy-forecasting
+  - photovoltaic
+library_name: sklearn
+pipeline_tag: tabular-regression
+metrics:
+  - r2
+  - mae
+  - rmse
 ---
 
-# Solar Power Forecast Model
+# Solar Power Generation Forecast Model
 
-RandomForestRegressor trained to predict plant-level DC power output
-15 minutes ahead using weather sensor data and lag features.
+A `RandomForestRegressor` (scikit-learn) trained on real solar plant operational data
+to predict **plant-level DC power output 15 minutes into the future**. Part of a
+GenAI capstone project that extends this forecasting model with an agentic grid
+optimisation assistant built on LangGraph, FAISS RAG, and Llama 3.1 via Groq.
 
-**Dataset**: Kaggle Solar Power Generation Data (Plant 1, 34 days, 15-min intervals)
-**Features**: irradiation, ambient temp, module temp, hour, day_of_year, month, lag_1, lag_4, rolling_mean_4
-**R² (daytime)**: 0.9905
-**R² (full dataset)**: 0.9323
+---
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| **Model type** | RandomForestRegressor (scikit-learn 1.8.0) |
+| **Task** | Tabular regression — 15-minute ahead solar power forecasting |
+| **n_estimators** | 200 |
+| **max_depth** | 12 |
+| **random_state** | 42 |
+| **n_jobs** | -1 (parallelised) |
+| **Input features** | 9 |
+| **Target** | DC_POWER at t+1 (Watts, plant-level aggregate) |
+
+---
+
+## Dataset
+
+- **Source**: [Kaggle Solar Power Generation Data](https://www.kaggle.com/datasets/anikannal/solar-power-generation-data)
+- **Plant**: Plant 1 — two 15-minute aligned CSV files (generation + weather sensor)
+- **Period**: 34 days (May–June 2020), 15-minute intervals
+- **Raw records**: 68,778 inverter-level rows → 3,157 plant-level timestamps after aggregation
+- **Train/test split**: 80/20 chronological (2,521 train / 631 test) — no shuffling to prevent leakage
+
+### Data Preprocessing
+
+1. Inverter-level `DC_POWER` summed per timestamp to plant-level aggregate
+2. Merged with weather sensor table on `DATE_TIME`
+3. Chronological sort, null rows dropped after feature construction
+
+---
+
+## Features
+
+| Feature | Type | Construction |
+|---|---|---|
+| `AMBIENT_TEMPERATURE` | Weather | Raw sensor reading (°C) |
+| `MODULE_TEMPERATURE` | Weather | Raw sensor reading (°C) |
+| `IRRADIATION` | Weather | Raw sensor reading (kW/m²) |
+| `hour` | Time | `DATE_TIME.dt.hour` |
+| `day_of_year` | Time | `DATE_TIME.dt.dayofyear` |
+| `month` | Time | `DATE_TIME.dt.month` |
+| `lag_1` | Autoregressive | `DC_POWER` at t−1 (15 min prior) |
+| `lag_4` | Autoregressive | `DC_POWER` at t−4 (1 hour prior) |
+| `rolling_mean_4` | Autoregressive | Rolling mean of `DC_POWER` over 4 intervals |
+
+**Feature importances** (mean decrease in impurity, approximate):
+
+| Feature | Importance |
+|---|---|
+| `IRRADIATION` | ~0.88 |
+| `hour` | ~0.04 |
+| `rolling_mean_4` | ~0.03 |
+| `lag_4` | ~0.02 |
+| `lag_1` | ~0.01 |
+| Others | < 0.01 each |
+
+Irradiation dominates by a wide margin. Temporal lag features carry independent predictive
+signal for transition periods where irradiance changes rapidly.
+
+---
+
+## Performance
+
+| Evaluation Split | MAE (W) | RMSE (W) | R² |
+|---|---|---|---|
+| **Daytime only** (irradiation > 0) | 4,646.83 | 7,397.92 | **0.9905** |
+| **Full dataset** (24-hour) | 10,573.81 | 21,207.71 | **0.9323** |
+
+The gap between splits reflects sunrise/sunset transition periods where steep power ramps
+are structurally harder to predict with autoregressive lag features calibrated on
+steady-state production.
+
+---
 
 ## Usage
 
 ```python
 import joblib
+import numpy as np
+import pandas as pd
 from huggingface_hub import hf_hub_download
 
+# Load model
 path = hf_hub_download(repo_id="nakedved/genai-capstone", filename="solar_forecast_model.pkl")
 model = joblib.load(path)
+
+# Input must have exactly these 9 columns in this order:
+# AMBIENT_TEMPERATURE, MODULE_TEMPERATURE, IRRADIATION,
+# hour, dayofyear, month, lag_1, lag_4, rolling_mean_4
+
+sample = pd.DataFrame([{
+    "AMBIENT_TEMPERATURE": 28.5,
+    "MODULE_TEMPERATURE": 42.1,
+    "IRRADIATION": 0.65,
+    "hour": 12,
+    "dayofyear": 155,
+    "month": 6,
+    "lag_1": 85000.0,
+    "lag_4": 78000.0,
+    "rolling_mean_4": 81500.0,
+}])
+
+prediction = model.predict(sample)
+print(f"Predicted DC Power (next 15 min): {prediction[0]:,.0f} W")
+```
+
+---
+
+## Limitations
+
+- Trained on a single plant (Plant 1) over 34 days. Performance on other plants or
+  seasonal conditions outside May–June may degrade.
+- Batch inference only — not designed for streaming real-time input.
+- RandomForest has no explicit temporal memory; long-range dependencies (multi-hour
+  trends, weather fronts) are not captured.
+- Sunrise/sunset RMSE is significantly higher than daytime-only RMSE due to steep
+  power ramps that lag features partially miss.
+
+---
+
+## Citation
+
+If you use this model, please reference the source dataset:
+
 ```
+Anikannal (2020). Solar Power Generation Data.
+Kaggle. https://www.kaggle.com/datasets/anikannal/solar-power-generation-data
+```
+
+---
+
+## Related
+
+- **Deployed app**: https://solarpowerpredictionmodel.streamlit.app/
+- **GitHub**: https://github.com/Aviral02git/Solar-power-prediction