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Renewable energy aims to generate more ecological and non-pollutant energy and for that, they are essential for sustainable development of the society.Generation with renewable energies must take into account the deficiencies and limitations that arise because of the intermittent capacity of power supply. Hybrid renewable energy systems based on solar and wind resources are the most promising energy generation power systems due to their complementarity to exploit natural resources; however, the installation of a hybrid solar-wind system is dependent of the weather conditions and that makes the dimension of the system complex.An optimal design of the hybrid system to be implemented becomes critical in order to have the best cost-reliability ratio. Applying intelligent optimization techniques makes possible to find an optimal design in a suitable time. The literature shows that the most used optimization technique is genetic algorithms. This master thesis, proposes to use intelligent technologies to optimally size a hybrid renewable energy system using solar and wind power. The proposed approach requires the system configuration, providing or not grid connection, To God for His infinite blessings. To my parents for their unconditional support, motivation and care. To my husband for making this process simpler with his love, patience and human warmth. Special thanks to my advisor Christian G. Quintero M. for his guidance, constant support, impartial corrections and confidence; for encouraging me to continue at all times and give me his friendship.My teachers Luis Torres, Juan Velez, Ingrid Oliveros, Cesar Orozco for allowing me to grow as a master's student. To Jamer Jimenez for his supervision, friendship and care.To my companions for sharing this path full of challenges with me and make it easier and his unconditional help. To my relatives and those whom I did not name, thank you. vi To my God, parents, family and friends vii General Contents Part I: Introduction and Related Work Motivation, objectives, main contributions and an overview of general concepts used in this thesis dissertation. A review of relevant related work used as reference and inspiration to develop the proposed approach. Part II: Proposed Approach General considerations and implementation of the proposed system for sizing hybrid renewable energy systems approach that seeks to provide broad decisions when designing optimal systems with intelligence techniques Part III: Experimental Results and Conclusions Analysis and discussion of the experimental results, conclusions and future research related to hybrid renewable energy systems based on intelligent systems. viii Detailed Contents CHAPTER 1 .

This paper considers the problem of diagnosing people's driving skills under real driving conditions using GPS data and video records. For this real environment implementation, a brand new intelligent driving diagnosis system based on fuzzy logic was developed. This system seeks to propose an abstraction of expert driving criteria for driving assessment. The analysis takes into account GPS signals such as: position, velocity, accelerations and vehicle yaw angle; because of its relation with drivers' maneuvers.In that sense, this work presents in the first place, the proposed scheme for the intelligent driving diagnosis agent in terms of its own characteristics properties, which explain important considerations about how an intelligent agent must be conceived. Secondly, it attempts to explain the scheme for the implementation of the intelligent driving diagnosis agent based on its fuzzy logic algorithm, which takes into account the analysis of real-time telemetry signals and proposed set of driving diagnosis rules for the intelligent driving diagnosis, based on a quantitative abstraction of some traffic laws and some secure driving techniques.Experimental testing has been performed in driving conditions. All tested drivers performed the driving task on real streets. The testing results show that our intelligent driving diagnosis system allows quantitative qualifications of driving performance with a high degree of reliability.

In this paper we consider the problem of searching an unknown number of targets in static environment by a team of robots. As the targets positions and distribution are uncertain; the goal is to minimize the overall exploration time. Using cell maps, the key problem can be solved choosing the suitable cell for the individual robots so that they simultaneously explore different regions of the environment. We present an intelligent approach for the coordination of multiple robots, in which contrast to previous approaches, able to perform task allocations taking into account the trade-off between the costs of reaching the cell and its utility. This utility function has been modeled using neural networks and optimized with genetic algorithms. Besides, if the task produces some conflict between robots, a negotiation algorithm is used to collision avoidance. The proposed approach has been implemented in real-world experiments and its performance tested in simulation runs. The results given in this paper demonstrate that our coordination mechanism significantly reduces the exploration time and increase the effectiveness compared to previous approaches.