Travieso-González Carlos M, Celada-Bernal Sergio, Lomoschitz Alejandro, Cabrera-Quintero Fidel
Institute for Technological Development and Innovation in Communications, IDeTIC, University of Las Palmas de Gran Caanria, ULPGC, Las Palmas de Gran Canaria, E35017, Spain.
Signals and Communications Departament, University of Las Palmas de Gran Caanria, Las Palmas de Gran Canaria, ULPGC, E35017, Spain.
Heliyon. 2024 Apr 21;10(9):e30002. doi: 10.1016/j.heliyon.2024.e30002. eCollection 2024 May 15.
Forecasting is of great importance in the field of renewable energies because it allows us to know the quantity of energy that can be produced, and thus, to have an efficient management of energy sources. However, determining which prediction system is more adequate is very complex, as each energy infrastructure is different. This work studies the influence of some variables when making predictions using ensemble methods for different locations. In particular, the proposal analyzes the influence of the aspects: the variation of the sampling frequency of solar panel systems, the influence of the type of neural network architecture and the number of ensemble method blocks for each model. Following comprehensive experimentation across multiple locations, our study has identified the most effective solar energy prediction model tailored to the specific conditions of each energy infrastructure. The results offer a decisive framework for selecting the optimal system for accurate and efficient energy forecasting. The key point is the use of short time intervals, which is independent of type of prediction model and of their ensemble method.
预测在可再生能源领域非常重要，因为它能让我们了解可生产的能源量，从而实现能源的高效管理。然而，确定哪种预测系统更合适非常复杂，因为每个能源基础设施都不同。这项工作研究了在不同地点使用集成方法进行预测时一些变量的影响。具体而言，该提议分析了以下方面的影响：太阳能板系统采样频率的变化、神经网络架构类型的影响以及每个模型的集成方法块数量。通过在多个地点进行全面实验，我们的研究确定了针对每个能源基础设施特定条件的最有效太阳能预测模型。研究结果为选择准确高效的能源预测最优系统提供了决定性框架。关键在于使用短时间间隔，这与预测模型类型及其集成方法无关。
