Abstract
Prediction of solar irradiation and wind speed are essential for enhancing the renewable energy integration into the existing power system grids. However, the deficiencies caused to the network operations provided by their intermittent effects need to be investigated. Regarding reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator. This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.

Abstract
This paper presents a cost-effective approach of a mecanum wheel robotic platform for educational propose on the development of an autonomous or remote controlled mobile robot with a four-wheel mecanum drive train. The main structure of the mobile robot was developed in Solidworks and it was built using additive manufacturing to validate in a real scenario. The main objective of developing this type of mobile platform was the ability to transport different types of cargo or robotic arm on industrial spaces or on rough terrain, since the implemented suspension mechanism allows the wheels contact to the floor. Another important objective is the maneuverability and the capacity to be guided in various environments, a great advantage in this type of mobile platform. An additional advantage of the developed mobile robot is the easy way to reconfigure the structure for new acquired parts.

Abstract
This research proposes positioning obstacle detection sensors by multirotor unmanned aerial vehicles (UAVs) dedicated to detailed inspections in high voltage towers. Different obstacle detection sensors are analyzed to compose a multisensory architecture in a multirotor UAV. The representation of the beam pattern of the sensors is modeled in the CoppeliaSim simulator to analyze the sensors' coverage and detection performance in simulation. A multirotor UAV is designed to carry the same sensor architecture modeled in the simulation. The aircraft is used to perform flights over a deactivated electrical tower, aiming to evaluate the detection performance of the sensory architecture embedded in the aircraft. The results obtained in the simulation were compared with those obtained in a real scenario of electrical inspections. The proposed method achieved its goals as a mechanism to early evaluate the detection capability of different previously characterized sensor architectures used in multirotor UAV for electrical inspections.

Abstract
Increasing population in cities combined with efforts to obtain more sustainable living spaces will require a smarter Solid Waste Management System (SWMS). A critical step in SWMS is the collection of wastes, generally associated with expensive costs faced by companies or municipalities in this sector. Some studies are being developed for the optimization of waste collection routes, but few consider inland cities as model regions. Here, the model region considered for the route optimization using Guided Local Search (GLS) algorithm was Bragança, a city in the northeast region of Portugal. The algorithm used in this work is available in open-source Google OR-tools. Results show that waste collection efficiency is affected by the upper limit of waste in dumpsters. Additionally, it is demonstrated the importance of dynamic selection of dumpsters. For instance, efficiency decreased 10.67% for the best upper limit compared to the traditional collection in the regular selection of dumpsters (levels only). However, an improvement of 50.45% compared to traditional collection was observed using dynamic selection of dumpsters to be collected. In other words, collection cannot be improved only by letting dumpsters reach 90% of waste level. In fact, strategies such as the dynamic selection here presented, can play an important role to save resources in a SWMS.

Abstract
Unmanned Aerial Vehicles (UAVs) are being deployed in different applications due to their reduced time execution to perform tasks, more extensive coverage area, and more risk minimization to humans. In the Oil & Gas industry, its use for inspection activities is even more attractive due to the large structures in these facilities. Therefore, this research proposes deploying an autonomous UAV system to inspect unburied pipelines of onshore O&G facilities. The proposed UAV guiding system is based on image processing techniques Canny edge detection and Hough Transform to detect the line and on a path follower algorithm to generate the trajectory. The proposed strategy was developed in Robot Operating System (ROS) and tested in a simulated environment considering the practical oper-ational. The same controller was tested on a physical UAV to validate the results obtained in previous simulations. The results demonstrated the effectiveness and feasibility of deploying the proposed strategy for this specific task and the cost reduction potential for real-life operations, as well as reduced potential risks to the physical integrity of the workers.

Abstract
Monitoring and controlling the energy consumption of electrical appliances brings significant benefits to both consumers and the energy utility. This work presents a system for monitoring and controlling energy consumption by residence loads connected to smart plugs. The user will have a tool to view consumption information and remotely turn loads on and off, as well as control the power level at which certain appliances will operate. In addition, it is intended to give the system the ability to make decisions regarding the operation of electrical devices based on the electrical energy available. This decision-making can occur either through priorities established by the user or, possibly, through Machine Learning applied to the system, based on the consumption pattern. Solutions like these can even be applied in situations where the user produces his own energy and would like to use the surplus produced to meet certain loads.