Multi-Label Forecasting of Power Output in BAPV Plants Using Machine Learning

Introduction

In recent years, the demand for renewable energy sources has skyrocketed, and photovoltaic (PV) systems have emerged as a leading solution. However, accurately predicting the power output of PV systems is crucial for optimizing their efficiency and managing energy supply. This study explores the use of machine learning algorithms (MLAs) to achieve multi-label forecasts of both PV and alternating current (AC) power outputs in Building Applied Photovoltaics (BAPV) plants. This approach leverages various ML techniques to model complex relationships between environmental variables and energy outputs.

Overview of Machine Learning Algorithms

The study employs several distinct groups of MLAs, categorized into:

1. Neural-based Methods:

   • Neural Networks (NN)
   • Deep Learning (DL)

2. Regression-based Methods:

   • Linear Regression (LR)
   • Tree-based Methods: Including Gradient-Boosted Trees (GBT), Random Forests (RF), and Decision Trees (DT)

3. Lazy-based Methods:

   • K-Nearest Neighbors (K-NN)
4. Support Vector Machines (SVM)

In implementing these algorithms, the study utilizes RapidMiner Studio, an advanced data analytics software that provides a user-friendly graphical interface for data manipulation, model building, and evaluation.

Data Collection and Pre-processing

Data collection is foundational to the study, focusing on environmental parameters such as solar irradiance, ambient temperature, wind speed, and cell temperature, alongside recorded PV and AC power outputs. The dataset spans one year, from October 2022 to September 2023, capturing all seasonal variations.

Pre-processing involves:

1. Data Filtering: Removing outliers and erroneous readings.
2. Feature Selection: Identifying significant input features via techniques like feature importance ranking and Pearson correlation.
3. Data Splitting: Dividing the dataset into training and validation subsets with a 70:30 ratio to prepare for model training.

This structured workflow ensures a clean and balanced dataset for accurate forecasting.

Machine Learning Implementation

The core of the study revolves around the iterative training and optimization of ML models. Each MLA is trained on historical data to recognize patterns and predict future outputs. The process can be summarized in a series of steps:

1. Input Phase: Gathering all relevant data, including meteorological data.

2. Training Phase: Using the collected data to train various MLAs iteratively.

3. Forecasting Phase: Applying trained models to predict power generation based on new data.

Models are subjected to hyper-parameter tuning to optimize performance, using a validation dataset to adjust aspects like the number of hidden layers in NN or kernel selection for SVM.

Feature Selection Techniques

Feature selection plays a pivotal role in model accuracy. By analyzing correlations between input features (like solar irradiance and temperature) and output variables (PV and AC power), models can prioritize the most relevant variables. The methodology employs statistical correlation metrics, specifically the Pearson correlation coefficient, to determine which meteorological factors most effectively influence energy output.

Meteorological Influence on PV Output

The environmental parameters significantly impact the performance of forecasting models. Solar irradiance and ambient temperature are two major contributors, with their relationship affecting power generation levels. As cloud movement can result in abrupt changes in solar energy capture, comprehensive input features are critical for accurate model predictions.

The chosen dataset, recorded at high resolution (5-minute intervals), includes over 105,000 samples, enabling fine-grained seasonal analysis and training of ML models.

Algorithms and Their Applications

Regression Algorithms

Linear Regression (LR) and Polynomial Regression (PR) are essential for quantifying relationships between dependent and independent variables. Their mathematical formulations capture the impact of various meteorological inputs on the PV output.

Neural Networks

Artificial Neural Networks (ANNs) and Deep Learning models are capable of capturing complex relationships in data, transforming inputs through layered architectures. Using sigmoid or linear activation functions depending on the output type, these models have shown promise in improving prediction accuracy.

Tree-based Methods

Tree-based models, including Decision Trees and Random Forests, utilize hierarchical structures to derive predictions. They effectively model interactions between variables and can manage nonlinear relationships, making them suitable for capturing the complexities present in PV forecasting.

Lazy Learning

The K-Nearest Neighbors (K-NN) algorithm provides a straightforward, instance-based approach to predictions. By averaging the outputs of similar instances, K-NN enables quick forecasting without extensive training, albeit at the cost of increased memory and computation during the prediction phase.

Support Vector Machines (SVM)

SVMs leverage linear kernel functions for compact models that minimize computational overhead. They are particularly effective in high-dimensional feature spaces, making them advantageous for datasets rich in meteorological data.

Model Evaluation and Performance Measurement

To assess the predictive success of each MLA, various error metrics are computed. These include the Absolute Error (AE), Root Mean Square Error (RMSE), Normalized Absolute Error (NAE), Relative Error (RE), Relative Root Square Error (RRSE), and Correlation Coefficient (R). These metrics provide a comprehensive picture of each model’s efficacy and reliability.

Conclusion (for Transition)

The study meticulously documents the workflow from data collection through model training to predictive evaluation, showcasing a robust multi-label forecasting approach for PV and AC power outputs. Utilizing an array of machine learning techniques ensures a nuanced view of influencing parameters while aiming for reliable predictions tailored to the unique characteristics of the BAPV system.