🧠 AI with Python – ☀️ Solar Energy Output Prediction

Solar energy generation depends heavily on environmental conditions such as sunlight intensity, temperature, humidity, and time of day.

Accurately predicting solar energy output helps power providers optimize grid planning, manage storage, and reduce energy waste.

In this project, we build a machine learning regression model to predict solar energy output using weather and time-based features — a practical real-world application of ML in renewable energy.

Understanding the Problem

Solar energy output is not constant. It varies due to:

• changing sunlight intensity throughout the day
• weather conditions like humidity and wind
• seasonal and environmental factors

The challenge is to learn the relationship between these factors and the actual energy produced, making this a regression problem rather than classification.

1. Loading the Solar Energy Dataset

We begin with a dataset containing environmental measurements and corresponding solar energy output.

import pandas as pd

df = pd.read_csv("solar_energy.csv")
df.head()

Each row represents a time snapshot with features such as temperature, humidity, solar irradiance, wind speed, and hour of the day.

2. Inspecting Data Quality

Before modeling, it’s important to verify data types and check for missing values.

print(df.info())
print(df.isnull().sum())

Solar datasets often originate from sensors, which may occasionally produce missing or noisy readings.

3. Preparing Features and Target

We separate input features from the target variable.

X = df.drop("energy_output", axis=1)
y = df["energy_output"]

The target variable represents energy generated (in kWh), making this a continuous prediction task.

4. Train/Test Split

We split the data to evaluate how well the model generalizes to unseen conditions.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y,
    test_size=0.3,
    random_state=42
)

5. Training a Regression Model

Solar energy output often has non-linear relationships with weather features.

We use a Random Forest Regressor to capture these patterns.

from sklearn.ensemble import RandomForestRegressor

model = RandomForestRegressor(
    n_estimators=200,
    max_depth=10,
    random_state=42
)

model.fit(X_train, y_train)

Random Forest models work well with mixed feature interactions and noisy data.

6. Evaluating Model Performance

We evaluate predictions using regression-specific metrics.

from sklearn.metrics import mean_absolute_error, r2_score

y_pred = model.predict(X_test)

print("MAE:", mean_absolute_error(y_test, y_pred))
print("R²:", r2_score(y_test, y_pred))

• MAE shows average prediction error
• R² measures how much variance the model explains

7. Comparing Actual vs Predicted Output

Visualizing predictions helps assess model behavior.

import matplotlib.pyplot as plt

plt.scatter(y_test, y_pred)
plt.xlabel("Actual Energy Output")
plt.ylabel("Predicted Energy Output")
plt.title("Solar Energy Output: Actual vs Predicted")
plt.grid(True)
plt.show()

A tight diagonal pattern indicates strong predictive performance.

Key Takeaways

1. Solar energy prediction is a practical real-world regression problem.
2. Environmental and time-based features strongly influence energy output.
3. Random Forest models capture non-linear relationships effectively.
4. MAE and R² are essential metrics for evaluating regression models.
5. ML plays a vital role in renewable energy optimization and planning.

Conclusion

Predicting solar energy output demonstrates how machine learning can contribute to sustainable energy solutions.

By learning patterns from weather and environmental data, ML models help improve forecasting accuracy and support smarter energy management.

This project highlights a complete end-to-end regression workflow and fits naturally into the AI with Python – Real-World Mini Projects (Advanced) series — bridging machine learning with real-world environmental impact.