Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

Abstract

In recent years, photovoltaic energy has become one of the most implemented electricity generation options to help reduce environmental pollution suffered by the planet. Accuracy in this photovoltaic energy forecasting is essential to increase the amount of renewable energy that can be introduced to existing electrical grid systems. The objective of this work is based on developing various computational models capable of making short-term forecasting about the generation of photovoltaic energy that is generated in a solar plant. For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. RNN models have a particular configuration that makes them efficient for processing ordered data in time series. The results of this work have been obtained through controlled experiments with different configurations of its hyperparameters for hybrid RNN-ANN models. From these, the three models with the best performance are selected, and after a comparative analysis between them, the forecasting of photovoltaic energy production for the next few hours can be determined with a determination coefficient of 0.97 and root mean square error (RMSE) of 0.17. It is concluded that the proposed and implemented models are functional and capable of predicting with a high level of accuracy the photovoltaic energy production of the solar plant, based on historical data on photovoltaic energy production.

Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks

“…Subsequently, forecast models were generated using the RNN-ANN hybrid architecture designed and explained in [11]. These models were created using various hyperparameter configurations and trained, validated, and evaluated using five performance metrics.…”

“…Based on the previous research described in [11], this study generated forecast models using records of EAE production combined with meteorological variables obtained from meteorological stations installed inside a solar plant. The meteorological variables included sun radiation (IRRAD), temperature (TEMP), wind speed (WS), wind angle (WANG), and a timeline (date and time).…”

“…In this article, the focus is on the development of forecast models for the generation of photovoltaic energy. It uses a base architecture described in a previous research work, referred to as [11]. This hybrid architecture combines RNN and ANN components in two hidden layers:…”

“…The main innovation of this study compared to the analyzed works is its utilization of the RNN-ANN hybrid architecture designed and described in the previous work described in [11]. This architecture serves as the foundation for generating forecast models of photovoltaic energy production.…”

“…In the previous work [11], the hybridization of this RNN-ANN architecture was introduced to improve the models' convergence. The results of the tests demonstrate that using solely layers with recurrent neurons does not yield superior performance when compared to incorporating an additional layer of shallow neurons.…”