Intelligent energy management system in buildings

Energy management systems have become one of the most significant concepts in the power energy area, due to the dependency of nowadays human’s lifestyle on electrical appliances and increment of energy demand during the past decades. From a general perspective, the total energy consumption by humans can be divided into three main economic sectors, namely industry, transportation, and buildings. Based on recent studies, the buildings present the largest share of consumption, standing for approximately 40% of the total consumption. This fact makes buildings energy management the most important component of energy management. On another hand, according to the variety of different types of buildings and several existing consumption appliances, the management of energy consumption in the building becomes a challenging problem. The main goal of a building energy management system is to control the energy consumption of the building by considering several facts, such as current and estimated consumption and generation, the energy price and comfort of the users. Due to the complexity of this management and limitations of available information, most of the existing systems focus on optimizing the consumption value and the cost of the energy with less consideration of the comforts and habits of the users. Moreover, the context of decision-making is also not sufficiently explored. However, the energy management in the building can be designed based on an intelligent system which has the knowledge to estimate the comforts and needs of the users and acts based on this awareness. This work studies and develops an intelligent energy management system for buildings energy consumption. This system receives the historical data of the building and uses a set of artificial intelligence techniques as well as several designed rulesets and acts as a recommender system. The goal of the generated recommendations by this system is to attune the usage of the electrical appliances of the building by comforts and habits of the residents while considering the price of the electricity market and the current context. Results show that the system enables users to obtain a comfortable environment in the building in the most affordable way.Nas últimas décadas, a dependência do estilo de vida na elevada utilização de dispositivos elétricos e grande consumo energético, faz com que os sistemas de gestão de energia sejam um dos conceitos mais relevantes no setor energético. Numa perspetiva geral, o total da energia consumida divide-se essencialmente em três setores económicos: industrial, transporte e edifícios. Os edifícios têm a maior representatividade, correspondendo aproximadamente a 40% do consumo total. Assim, a gestão energética em edifícios é a componente com maior importância nesta área. Por outro lado, devido à variedade dos diferentes tipos de edifícios e dispositivos de consumo, a gestão do consumo de energia nos edifícios apresenta desafios. O objetivo principal de um sistema de gestão energética em edifícios consiste em controlar o consumo energético no edifício, considerando diversos fatores, tais como o consumo e produção atuais, a sua estimativa, o preço de mercado e conforto dos seus utilizadores. Perante a complexidade desta gestão e das limitações da informação disponível, a maioria dos sistemas tem foco na otimização do consumo e os seus custos, tendo em menor consideração o conforto e hábito dos utilizadores. Além disso, o contexto da tomada de decisão não é devidamente explorado, enquanto a gestão energética em edifícios pode ser baseada num sistema inteligente, cujo conhecimento pode estimar o conforto e necessidades dos seus utilizadores, e assim atuar com base nessa consciência. Este trabalho estuda e desenvolve um sistema inteligente para a gestão do consumo de energia em edifícios. O sistema recebe o histórico de dados de um edifício, e utiliza um conjunto de técnicas de inteligência artificial e conjuntos de regras, funcionando como um sistema de recomendações. O objetivo das recomendações geradas pelo sistema é adaptar os dispositivos elétricos do edifício ao conforto e hábitos dos utilizadores enquanto são considerados o preço de mercado e o contexto atual. Os resultados demonstram que o sistema permite aos utilizadores obter um ambiente confortável no edifício, da forma mais económica possível

Enhancing the performance of flexible AC transmission systems (FACTS) by computational intelligence

The thesis studies and analyzes UPFC technology concerns the management of active and reactive power in the power networks to improve the performance aiming to reach the best operation criteria. The contributions of the thesis start with formatting, deriving, coding and programming the network equations required to link UPFC steady-state and dynamic models to the power systems. The thesis derives GA applications on UPFC to achieve real criteria on a real world sub-transmission network. An enhanced GA technique is proposed by enhancing and updating the working phases of the GA including the objective function formulation and computing the fitness using the diversity in the population and selection probability. The simulations and results show the advantages of using the proposed technique. Integrating the results by linking the case studies of the steady-state and the dynamic analysis is achieved. In the dynamic analysis section, a new idea for integrating the GA with ANFIS to be applied on the control action procedure is presented. The main subject of the thesis deals with enhancing the steady-state and dynamics performance of the power grids by Flexible AC Transmission System (FACTS) based on computational intelligence. Control of the electric power system can be achieved by designing the FACTS controller, where the new trends as Artificial Intelligence can be applied to this subject to enhance the characteristics of controller performance. The proposed technique will be applied to solve real problems in a Finnish power grid. The thesis seeks to deal, solve, and enhance performances until the year 2020, where the data used is until the conditions of year 2020. The FACTS device, which will be used in the thesis, is the most promising one, which known as the Unified Power Flow Controller (UPFC). The thesis achieves the optimization of the type, the location and the size of the power and control elements for UPFC to optimize the system performance. The thesis derives the criteria to install the UPFC in an optimal location with optimal parameters and then designs an AI based damping controller for enhancing power system dynamic performance. In this thesis, for every operating point GA is used to search for controllers' parameters, parameters found at certain operating point are different from those found at others. ANFISs are required in this case to recognize the appropriate parameters for each operating point