Ciencias Exactas y Ciencias de la Salud

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Tailored gamification platform based on artificial intelligence. Connected thermostats as a case study for saving energy in connected homes
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12) Méndez Garduño, Juana Isabel; MENDEZ GARDUÑO, JUANA ISABEL; 686197; Ponce Cruz, Pedro; emipsanchez; McDaniel, Troy; López Caudana, Edgar Omar; Molina Gutiérrez, Arturo; School of Engineering and Sciences; Campus Monterrey; Peffer, Therese
The product platforms are a set of system components that are interdependent with other system components. Furthermore, platforms are the basis for all technology-based products and allow collaborations for multi-product systems. Traditionally, products were created without third-party collaboration. Thus, the same owner's product upgraded, modified, or updated the product falling in limited customization, lack of integration, and modularity. Evolving products into product platforms creates value, but it is complex to implement. The relevance of transitioning into product platforms relies on companies entering global markets. Therefore, platforms are cost-effective for global competition. For instance, around 60~\% of technological companies value investing in platforms. Furthermore, the tendency shows that companies aspire to turn the business into a fully integrated digital technology company. On the other hand, customers prefer a tailored service, platform, or product over generic products. Nevertheless, the adoption of these product platforms fails due to usability and behavioral problems. Hence, it is complex to measure individuals’ satisfaction because their behavior is related to perception and other context-specific factors, such as age, gender, income, cultural aspects, specific needs, personality traits, and other preferences. To achieve the adoption of product platforms, this thesis proposes to tailor user solutions by profiling the consumer through personality traits to propose strategies that allow them to adapt more easily to product usage. Thus, appealing ludic interfaces engage end-users to interact better with platforms. Therefore, social interaction (social platform) plays a primary role in understanding and knowing better the users’ patterns and profiles them. In addition, it is feasible to understand consumers' habits by sending stimuli through gamification or serious game strategies. Gamification enhances a platform with affordances for gameful experiences to support the user’s overall value creation. Besides, Artificial Intelligence decision systems link the type of consumer and gamification for deploying user-oriented product platforms. Hence, this thesis proposed a four-step methodology for deploying tailored platforms and validating the methodology in a case study. Furthermore, this methodology was used in the context of smart homes, smart communities, and smart cities.
Enhanced maximum Power Point tracking algorithm and DC-DC converters optimal design methodology powered by the earthquake optimization algorithm
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11) Méndez Flores, Efraín; MENDEZ FLORES, EFRAIN; 859994; Macías Hidalgo, Israel; puelquio, emipsanchez; Vargas Martínez, Adriana; Ponce Cruz, Pedro; Sánchez García, Juan Carlos; Soriano Avendaño, Luis Arturo; School of Engineering and Sciences; Campus Ciudad de México
Nowadays, owing to the growing interest in cleaner energy systems, energy harvesting from Photovoltaic (PV) sources has gained greater relevance due to their worldwide suitability. PV systems are responsible for supplying more than 500,000 [GW] of the electrical energy consumed worldwide. Therefore, different power converters topologies, control algorithms and techniques have been developed to maximize the energy harvested by PV sources. Among the research topics related to PV applications, Maximum Power Point Tracking (MPPT) methods are usually employed together with DC/DC converters to control the impedance at the output of PV arrays, which allows changing the current and voltage supplied by the PV source to achieve a dynamic optimization of the energy transferred. Classical MPPT algorithms such as, Perturb and Observe (P&O) guarantee correct tracking behavior with low calibration parameter dependence but with a compromised relationship between the settling time and steady-state oscillations. Thus, methods like Particle Swarm Optimization (PSO) based techniques have improved the settling time and the steady-state oscillations, but the performance of the PSO-MPPT is highly susceptible to a correct and precise parameter calibration, which may not always ensure the expected behavior. Therefore, this work presents a novel alternative for MPPT applications, based on the Earthquake Optimization Algorithm (EA), which contributes a solution with an easy parameters calibration and improved dynamic behavior. Hence, results show that the contributed MPPT can be easily suited to different power applications and converter topologies, where the proposed solution reduced between 12% and 36% of the energy wasted compared to the P&O and PSO-based proposals. Yet, aggressive dynamic changes may cause the metaheuristic MPPTs to get stuck in local minimum solutions due to their convergence properties, which is why this work also presents an Artificial Neural Networks (ANN) contribution as a reliable reinitialization signal for metaheuristic MPPT algorithms, whose results show that the solar irradiation changes detection through the ANN achieved over 99\% of accuracy. Still, the experimental validation of the contributed MPPT control structure requires an efficient and reliable testbed for the tests, which is why MPPTs are usually implemented through DC-DC converters. Yet, components selection and precise estimation of circuit parameters are issues that can improve the converter’s performance; which is why, metaheuristic optimization algorithms can be applied using the mathematical model of DC-DC converters in order to optimize their performance through an optimal components selection. Therefore, this work also contributes a novel optimal design methodology for DC-DC converters, where the validation designs are optimized to enable an optimal dynamic behavior regarding the validating application. Henceforth, The experimental results validate the design methodology, showing ripple improvements and operating power range extension, which are key features to have an efficient performance in DC-DC converters. Thereby, the contributions of this work were completely validated through an integration case study, which will be later addressed in this work. The technology developed through the EA contributions in this work, reached a Technology Readiness Level (TRL) 5.

Ciencias Exactas y Ciencias de la Salud

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