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
Workplace accidents are a concern for companies nowadays and can occur due to internal and external factors of the company. Thereby, several strategies are developed to predict and minimize the hazards in this environment. Companies resort to intelligent solutions, such as predictive analytics, aiming to increase productivity while ensuring safety in the work environment. In terms of accident prediction analysis, different input data are needed to ensure the accuracy of a predictive model. Therefore, this study aims to automatic collect and pre-process data from holidays for subsequent implementation in an accident-oriented predictive model to demonstrate its relevance in predicting accidents in the workplace.

Abstract

Abstract
It is known that electrical energy consumption is higher during the day than at night.This is a challenge to balance the consumption levels because when the consumption is high at night, it does not have energy production to supply and the tariff usage is cheaper. Aspiring to avoid the users consuming too much electrical energy and work on this usage control during the night, the present work aims to develop smart plug modules that could self-manage power in residence utilizing the minimum of grid energy. In this sense, the modules may use the overproduction of energy coming from generator systems (such as photovoltaic panels), eliminating the necessity of battery usage. Sometimes, the power supply could provide different values of current, consequently, the use of this electric energy needs to adapt according to the production. Therefore, the final objective is to build an intelligent electrical management system that works on energy efficiency.

Abstract
In the current energy context, alternatives are sought that provide a more conscious use of energy and the development of technology aimed at efficiently meeting the needs of energy consumers and the utility company. In this scenario, smart systems for monitoring and controlling the energy consumption of residential loads stand out. In [1], the authors worked on a system from which the user could monitor their energy consumption in real time. Through a website, the consumer accessed their information using visualizations in graphics, for example. Consumption data was obtained by a smart plug. Furthermore, the option to remotely turn devices on and off has been included in the system so that the user has the ease of controlling their devices.

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.