Transformer based time series prediction of the maximum power point for solar photovoltaic cells

TL;DR

This paper introduces a transformer-based deep learning model for maximum power point tracking in solar PV cells, leveraging comprehensive environmental and time series data to improve prediction accuracy and robustness across diverse atmospheric conditions.

Contribution

It presents a novel transformer architecture that models cyclic atmospheric patterns using multidimensional time series data for enhanced MPPT in solar PV systems.

Findings

Achieved 0.47% mean average percentage error in power prediction.

Attained 99.54% average power efficiency in tests.

Validated robustness through real-time simulations.

Abstract

This paper proposes an improved deep learning based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series based environmental inputs. Generally, artificial neural network based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions.