Analysis of New Strategies to Reach Nearly Zero Energy Buildings (nZEBs)

The need to reduce CO2 emissions makes it necessary to review most areas of human activity because we are the main source of these emissions. In particular, the building sector is one of its main responsible. Through a State of the Art, several papers published in various scientific journals about nearly Zero Energy Building (nZEB) studies are reviewed. After carrying out an analysis of said documentation, it is considered that a new way of study can be developed with a multidisciplinary approach. For this, another series of articles are analyzed, where different advances in the field of building construction have been developed and are summarized. It is concluded that the Model Predictive Control (MPC) can also be a useful tool to optimize energy consumption in buildings, especially for public use, in order to achieve the goal of achieving nZEB, through the use of Renewable Energy Sources (RES)

Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes

Dynamical models are essential for model-based control methodologies which allow smart buildings to operate autonomously in an energy and cost efficient manner. However, buildings have complex thermal dynamics which are affected externally by the environment and internally by thermal loads such as equipment and occupancy. Moreover, the physical parameters of buildings may change over time as the buildings age or due to changes in the buildings’ configuration or structure. In this paper, we introduce an online model learning methodology to identify a nonparametric dynamical model for buildings when the thermal load is latent (i.e., the thermal load cannot be measured). The proposed model is based on stochastic hybrid systems, where the discrete state describes the level of the thermal load and the continuous dynamics represented by Gaussian processes describe the thermal dynamics of the air temperature. We demonstrate the evaluation of the proposed model using two-zone and five-zone buildings. The data for both experiments are generated using the EnergyPlus software. Experimental results show that the proposed model estimates the thermal load level correctly and predicts the thermal behavior with good performance

Modelizado y simulación energética/térmica de un edificio de oficinas, desarrollado a través de redes neuronales artificiales.

202 p.La tesis se encuentra estructurada en cinco capítulos, donde se expone una primera parte teórica y posteriormente se pone en práctica con objeto de justificar y demostrar dicha teoría. El primer capítulo explica las inquietudes que nos llevan a analizar la situación actual y la necesidad de hacer frente al problema para mejorar la eficiencia energética en edificios públicos. En el segundo capítulo se expone un Estado del Arte basado en distintas publicaciones relacionadas sobre el tema que han sido sintetizadas y analizadas. El tercer capítulo se centra en explicar el procedimiento de investigación e hipótesis planteada, justificando la metodología aplicada para optimizar el consumo energético en un edificio público tipo. El cuarto capítulo se dedica a explicar el proceso de modelizado y simulación, con todos los pasos realizados para llevar a cabo la hipótesis expuesta, sobre un edificio real que se ha elegido como referencia. En el capítulo quinto se analizan las estimaciones obtenidas a través de un modelo ANN en base a dicho edificio tipo, se muestran los resultados obtenidos y las correspondientes conclusiones de esta tesis, para la mejora de la eficiencia energética en los edificios públicos de oficinas