Predicting Photovoltaic System Performance with Real-World Data: A Comprehensive Guide

To predict the performance of a photovoltaic (PV) system using real-world data, we can employ various techniques. Here is a general approach that can be followed:
\

Data collection: Gather real-world data related to the PV system's performance. This may include historical solar irradiance data, temperature data, power output measurements from the PV system, and any other relevant variables. Ensure that the data covers a significant time period to capture different weather conditions and seasonal variations.
\
Data preprocessing: Clean the collected data by removing any outliers, missing values, or errors. Normalize the data if necessary to bring all variables to a similar scale. Also, consider transforming the data if required, such as converting daily solar irradiance to average monthly values.
\
Feature engineering: Create additional features that might influence the PV system's performance. For example, calculate the solar zenith angle based on the latitude and longitude of the PV system's location. Consider including factors like shading, azimuth angle, and tilt angle if available.
\
Splitting the dataset: Divide the dataset into training and testing sets. Typically, use around 70-80% of the data for training and the remaining 20-30% for testing/validation.
\
Model selection: Choose an appropriate machine learning model for prediction. Some commonly used models for PV system performance prediction include linear regression, support vector regression, random forest regression, and neural networks. Consider the complexity of the model, interpretability, and computational requirements while selecting.
\
Model training: Train the chosen model using the training dataset. Adjust the model's hyperparameters, if applicable, through techniques like grid search or random search to optimize its performance.
\
Model evaluation: Evaluate the trained model's performance using the testing dataset. Common evaluation metrics include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). Compare the predicted power output with the actual measurements.
\
Model refinement: If the model's performance is not satisfactory, consider refining the model. This could involve adjusting hyperparameters, incorporating additional features, or trying different models.
\
Prediction: Once the model is deemed satisfactory, use it to predict the performance of the PV system using new or unseen data. Ensure that the new data follows the same preprocessing steps as the training dataset.
\
Monitoring and updating: Continuously monitor the PV system's performance and update the model periodically to account for any changes or drifts in system behavior.

Remember that the accuracy of the predictions heavily relies on the quality and representativeness of the collected real-world data.