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Generation of electric energy through wind turbines is one of the practically inexhaustible alternatives of generation. It is considered a source of clean energy, but still needs a lot of research for the development of science and technologies that ensures uniformity in generation, providing a greater participation of this source in the energy matrix, since the wind presents abrupt variations in speed, density and other important variables. In wind-based electrical systems, it is essential to predict at least one day in advance the future values of wind behavior, in order to evaluate the availability of energy for the next period, which is relevant information in the dispatch of the generating units and in the control of the electrical system. This paper develops ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using artificial neural network models, Autoregressive Integrated Moving Average (ARIMA) and hybrid models including forecasting using wavelets. For the application of the methodology, the meteorological variables of the database of the national organization system of environmental data (SONDA), Petrolina station, from 1 January 2004 to 31 March 2017, were used. A comparison among results by different used approaches is also done and it is also predicted the possibility of power and energy generation using a certain kind of wind generator.

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The aim of this article is to present a systematic review of the literature on urban logistics and its stakeholders and identify future research directions. In order to discuss the main contributions and trends of this theme, a combination of bibliometric analysis techniques, semantic and content and a technological prospection were performed. Through the evaluation of a sample group of seventy one articles, it was possible to trace paths and understand content approaches, identifying advantages, limitations and conditions for the good development of city logistics and its agents. Based on these findings, the low evidence of how effectively each of the stakeholders influencing the activities of city logistics stands out, since your heterogeneity and conflict of interest are singled out as the cause of the difficulty of this deployment. It is important to note that from the technological prospection was possible to identify the state of innovation on the world stage in the area studied.

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Summary Economic‐emission load dispatch optimization problem is an essential task in power plants with internal combustion engines. In power plants, in addition to electricity, a lot of air pollution by exhaust gases is generated. There are many international standards that establish the permissible limits of different substances but still have not developed an expression to evaluate the environmental impact caused by all components of the exhaust gases as a whole. A new method to evaluate this impact is developed in this paper. The developed mathematical expression was called “emission index.” To get a better idea of the environmental impact of each type of engine, the “specific emission index”, which is the emission index divided by the power delivered by the engine. This paper also presents a mathematical model for a multiobjective optimization of economic‐emission load dispatch using nondominated sorting genetic algorithm II.

This paper presents a specific method to improve the reliability of the equipment and the quality of power supplied to the electrical systems with the frequency and voltage control of a thermoelectric plant, to guarantee a more stable system. The method has the novelty of combining Total Productive Maintenance (TPM) using only four pillars, with Electrical Predictive Maintenance based in failure analysis and diagnostic. It prevents voltage drops caused by excessive reactive consumption, thus guaranteeing the company a perfect functioning of its equipment and providing a longer life of them. The Maintenance Management Program (MMP) seeks to prevent failures from causing the equipment to be shut down from the electrical system, which means large financial losses, either by reducing billing or by paying fines to the regulatory agency, in addition to prejudice the reliability of the system. Using management tools, but applying only four TPM pillars, it was possible to achieve innovation in power plants with internal combustion engines. This study aims to provide maintenance with a more reliable process, through the implantation of measurement, control and diagnostic devices, thus allowing the management to reduce breakdown of plant equipment. Some results have been achieved after the implementation, such as reduction of annual maintenance cost, reduction of corrective maintenance, increase of MTBF (Mean Time between Failures) and reduction of MTTR (Mean Time to Repair) in all areas. Probabilistic models able to describe real processes in a more realistic way, and facilitate the optimization at maximum reliability or minimum costs are presented. Such results are reflected in more reliable and continual power generation.