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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Diseño de un sistema de navegación avanzado para el nanosatélite GWSat
Proyecto de Graduación (Licenciatura en Ingeniería Mecatrónica) Instituto Tecnológico de Costa Rica. Área Académica de Ingeniería Mecatrónica, 2020Este documento es un informe para optar por el título de licenciado en ingeniería en mecatrónica. En este se presenta la metodología para el diseño de un controlador clásico y un controlador inteligente para el nanosatélite GWSat, esto con el objetivo de en un futuro diseñar un sistema híbrido que integre ambas tecnologías. El primer controlador que se diseñó es un controlador LQR. El segundo se diseñó por medio del algoritmo de entrenamiento TD3, el cual se basa en aprendizaje por refuerzo profundo para su optimización. En el caso del controlador LQR se logró un tiempo de estabilización de 950s, un ángulo de error de 0.15° y un error en la velocidad angular de 3x10-5 rad/s. Para el caso del TD3 se obtuvo un tiempo de estabilización de 700 s, un ángulo de error de 1.4° y un error en la velocidad angular de 7.3x10-6 rad/s. Los aportes principales de este proyecto son dos controladores implementados en MATLAB los cuales son fáciles de utilizar y modificar para cualquier caso que se quiera probar, además de ambientes programados en los que se pueden ajustar los diferentes parámetros de los controladores en caso de que se desee un desempeño distinto.This document is a report to opt for a bachelor's degree in mechatronics engineering. This presents the methodology for the design of a classic controller and an intelligent controller for the GWSat nanosatellite, with the aim of designing a hybrid system that integrates both technologies, in the foreseeable future. The first controller that was designed is an LQR controller. The second was designed using the TD3 training algorithm, which is based on deep reinforcement learning for optimization. The LQR controller achieved a stabilization time of 950s, an error angle of 0.15° and an error in the angular velocity of 3x10-5 rad/s. For the TD3 case, it achieved a 700 s stabilization time, an 1.4° error angle, and an 7.3x10-6 rad/s angular velocity error. The main contributions of this project are two controllers implemented in MATLAB which are easy to use and modify for any case you want to test, also a series of programmed environments where you can modify the controllers´ different parameters in order to obtain a different performance