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nakedved/genai-capstone

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Solar Power Generation Forecast Model

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.

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
  • 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

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://solar-power-prediction-81xp.onrender.com
  • GitHub: https://github.com/vedpawar2254/Solar-power-prediction