Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

• Autor(a) principal: Casteleiro-Roca, José-Luis
• Data de Publicação: 2020
• Outros Autores: Chamoso, Pablo, Jove, Esteban, González-Briones, Alfonso, Quintián, Héctor, Fernández-Ibáñez, María-Isabel, Vega Vega, Rafael Alejandro, Piñón Pazos, Andrés-José, López Vázquez, José Antonio, Torres-Álvarez, Santiago, Pinto, Tiago, Calvo-Rolle, Jose Luis
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.22/19400
• topic: Clustering; Prediction; Regression; Solar thermal collector; Hybrid model

Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.