Energy performance forecasting of residential buildings using fuzzy approaches

The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. The characteristics of a building are an important factor to determine the necessities of heating and cooling loads. Therefore, the study of the relevant characteristics of the buildings, regarding the heating and cooling needed to maintain comfortable indoor air conditions, could be very useful in order to design and construct energy-efficient buildings. In previous studies, different machine-learning approaches have been used to predict heating and cooling loads from the set of variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. However, none of these methods are based on fuzzy logic. In this research, we study two fuzzy logic approaches, i.e., fuzzy inductive reasoning (FIR) and adaptive neuro fuzzy inference system (ANFIS), to deal with the same problem. Fuzzy approaches obtain very good results, outperforming all the methods described in previous studies except one. In this work, we also study the feature selection process of FIR methodology as a pre-processing tool to select the more relevant variables before the use of any predictive modelling methodology. It is proven that FIR feature selection provides interesting insights into the main building variables causally related to heating and cooling loads. This allows better decision making and design strategies, since accurate cooling and heating load estimations and correct identification of parameters that affect building energy demands are of high importance to optimize building designs and equipment specifications.Peer ReviewedPostprint (published version

Decision Support Application for Energy Consumption Forecasting

Energy consumption forecasting is crucial in current and future power and energy systems. With the increasing penetration of renewable energy sources, with high associated uncertainty due to the dependence on natural conditions (such as wind speed or solar intensity), the need to balance the fluctuation of generation with the flexibility from the consumer side increases considerably. In this way, significant work has been done on the development of energy consumption forecasting methods, able to deal with different forecasting circumstances, e.g., the prediction time horizon, the available data, the frequency of data, or even the quality of data measurements. The main conclusion is that different methods are more suitable for different prediction circumstances, and no method can outperform all others in all situations (no-free-lunch theorem). This paper proposes a novel application, developed in the scope of the SIMOCE project (ANI|P2020 17690), which brings together several of the most relevant forecasting methods in this domain, namely artificial neural networks, support vector machines, and several methods based on fuzzy rule-based systems, with the objective of providing decision support for energy consumption forecasting, regardless of the prediction conditions. For this, the application also includes several data management strategies that enable training of the forecasting methods depending on the available data. Results show that by this application, users are endowed with the means to automatically refine and train different forecasting methods for energy consumption prediction. These methods show different performance levels depending on the prediction conditions, hence, using the proposed approach, users always have access to the most adequate methods in each situationThe present work has been developed under Project SIMOCE (ANI|P2020 17690) co-funded by Portugal 2020 "Fundo Europeu de Desenvolvimento Regional" (FEDER) through COMPETE, the EUREKA - ITEA2 Project M2MGrids (ITEA-13011), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019info:eu-repo/semantics/publishedVersio

Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen

Controls and guidance research

The objectives of the control group are concentrated on research and education. The control problem of the hypersonic space vehicle represents an important and challenging issue in aerospace engineering. The work described in this report is part of our effort in developing advanced control strategies for such a system. In order to achieve the objectives stated in the NASA-CORE proposal, the tasks were divided among the group based upon their educational expertise. Within the educational component we are offering a Linear Systems and Control course for students in electrical and mechanical engineering. Also, we are proposing a new course in Digital Control Systems with a corresponding laboratory

A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

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