I developed a predictive model to estimate solar energy production using a time series dataset, leveraging the ARIMA (AutoRegressive Integrated Moving Average) model. The project aimed to accurately forecast solar energy output by analyzing historical solar irradiance data and temporal patterns. By applying ARIMA, I was able to capture and model the underlying trends and seasonality in the time series data, leading to more precise predictions of solar energy generation. The approach involved preprocessing the dataset to handle missing values and anomalies, followed by parameter tuning of the ARIMA model to optimize its performance. The predictive model provides valuable insights into energy production patterns, which can be used to enhance energy management and optimize solar panel deployment. This project contributes to more efficient utilization of solar energy resources, supporting sustainable energy practices.

Python (Time Series Analysis, Data Processing)

ARIMA Model for Time Series Forecasting

Data Preprocessing and Cleaning

Model Evaluation and Parameter Tuning

Visualization of Forecast Results

Accurate forecasting of solar energy production using historical data

Improved understanding of energy generation patterns and trends

Enhanced capability for energy management and optimization of solar resources

Contribution to sustainable energy practices by providing actionable insights into solar energy production