Initial commit: upload model and source code

.gitattributes

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+random_forest_model.pkl filter=lfs diff=lfs merge=lfs -text

README.md

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+# 🔋 Electricity Price Predictor (Random Forest Regression)
+
+This is a custom regression model trained to predict electricity prices ($/kWh) in California, based on a variety of grid-level and environmental features such as EV charging demand, solar/wind production, carbon emissions, and storage indicators.
+
+The model is trained using `RandomForestRegressor` from `scikit-learn`, with 24 engineered features and a structured tabular dataset. This project is intended to support intelligent energy systems, such as EV charging optimization, energy scheduling, or smart grid simulation.
+
+---
+
+## 📌 Model Details
+
+- 📈 **Model**: RandomForestRegressor (n_estimators=200)
+- 🧠 **Framework**: scikit-learn
+- 🧾 **Input Features**: 24 numerical values (see full list below)
+- 🎯 **Target Variable**: Electricity Price ($/kWh)
+- 🗃️ **Data**: Structured time-series dataset with hourly EV/grid info
+- 🧪 **Evaluation**:
+  - MSE: *e.g., 0.0023*
+  - R²: *e.g., 0.89*
+  - MAPE: *e.g., 6.5%*
+
+---
+
+## 🔢 Input Features
+
+The model expects a list of 24 numeric features:
+
+```text
+['Year', 'Month', 'Day', 'DayOfWeek', 'Hour',
+ 'EV Charging Demand (kW)', 'Solar Energy Production (kW)', 'Wind Energy Production (kW)',
+ 'Battery Storage (kWh)', 'Charging Station Capacity (kW)', 'EV Charging Efficiency (%)',
+ 'Number of EVs Charging', 'Peak Demand (kW)', 'Renewable Energy Usage (%)',
+ 'Grid Stability Index', 'Carbon Emissions (kgCO2/kWh)', 'Power Outages (hours)',
+ 'Energy Savings ($)', 'Total_Renewable_Energy_Production', 'Effective_Charging_Capacity',
+ 'Adjusted_Charging_Demand', 'Net_Energy_Cost', 'Carbon_Footprint_Reduction',
+ 'Renewable_Energy_Efficiency']
+
+```
+---
+---
+
+## 🧪 Usage Example
+
+### 🔹 Option 1: Load and use the model directly
+
+```python
+import joblib
+import numpy as np
+
+# Load trained model
+model = joblib.load("random_forest_model.pkl")
+
+# Sample input (replace with actual values)
+features = [0.5] * 24
+
+# Make prediction
+price = model.predict(np.array(features).reshape(1, -1))[0]
+print(f"Predicted Electricity Price: ${price:.4f}")
+```
+
+---
+
+### 🔹 Option 2: Use helper function in `predict.py`
+
+```python
+from predict import predict
+
+features = [0.5] * 24
+result = predict(features)
+print(f"Predicted Price: ${result:.4f}")
+```
+
+---
+
+### 🔹 Option 3: Try it online (Gradio Web Demo)
+
+If deployed, you can try it here:  
+[👉 Live Demo on Spaces](https://huggingface.co/spaces/YOUR_USERNAME/electricity-price-predictor-demo)
+
+---
+
+## 📊 Sample Dataset
+
+This repository includes a sample dataset: `processed_electric_price_filled.csv`.  
+It contains hourly records of EV charging demand, solar/wind energy production, grid stability, and electricity prices.
+
+### Load and explore:
+
+```python
+import pandas as pd
+
+df = pd.read_csv("processed_electric_price_filled.csv")
+print(df.head())
+```
+
+---
+
+## 📁 Files Included
+
+| File                                | Description                                      |
+|-------------------------------------|--------------------------------------------------|
+| `random_forest_model.pkl`           | Trained RandomForestRegressor model             |
+| `predict.py`                        | Python function to load and run predictions     |
+| `app.py` (optional)                 | Gradio-based interactive demo                   |
+| `requirements.txt`                 | Python dependencies                             |
+| `processed_electric_price_filled.csv` | Training/test dataset                         |
+| `README.md`                         | This documentation                              |
+
+---
+
+## 👨‍💻 Author
+
+**bajiang(Georgia)**  
+
+---
+
+## 📄 License
+
+MIT License – You are free to use, modify, and distribute this project with proper attribution.
+
+
+
+

predict.py

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+import joblib
+import numpy as np
+
+# Load the trained model
+model = joblib.load("random_forest_model.pkl")
+
+def predict(features):
+    """features: list of 24 numeric values"""
+    features = np.array(features).reshape(1, -1)
+    return model.predict(features)[0]

random_forest_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:524d90e3d96d2364837146c907394160de08660b2cfedeadc5778db9e5b831e2
+size 435340417

requirements.txt

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+scikit-learn
+numpy
+pandas
+joblib
+matplotlib
+gradio