Castillo-Rojas, W.; Medina Quispe, F.; Hernández, C. Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks. Energies2023, 16, 5093.
Castillo-Rojas, W.; Medina Quispe, F.; Hernández, C. Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks. Energies 2023, 16, 5093.
Castillo-Rojas, W.; Medina Quispe, F.; Hernández, C. Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks. Energies2023, 16, 5093.
Castillo-Rojas, W.; Medina Quispe, F.; Hernández, C. Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks. Energies 2023, 16, 5093.
Abstract
This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
