An intelligent system for electrical energy management in buildings

Recent studies have highlighted that a significant part of the electrical energy consumption in residential and business buildings is due to an improper use of the electrical appliances. In this context, an automated power management system - capable of reducing energy wastes while preserving the perceived comfort level - would be extremely appealing. To this aim, we propose GreenBuilding, a sensor-based intelligent system that monitors the energy consumption and automatically controls the behavior of appliances used in a building. GreenBuilding has been implemented as a prototype and has been experimented in a real household scenario. The analysis of the experimental results highlights that GreenBuilding is able to provide significant energy savings

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

Smart buildings are increasingly using Internet of Things (IoT)-based wireless sensing systems to reduce their energy consumption and environmental impact. As a result of their compact size and ability to sense, measure, and compute all electrical properties, Internet of Things devices have become increasingly important in our society. A major contribution of this study is the development of a comprehensive IoT-based framework for smart city energy management, incorporating multiple components of IoT architecture and framework. An IoT framework for intelligent energy management applications that employ intelligent analysis is an essential system component that collects and stores information. Additionally, it serves as a platform for the development of applications by other companies. Furthermore, we have studied intelligent energy management solutions based on intelligent mechanisms. The depletion of energy resources and the increase in energy demand have led to an increase in energy consumption and building maintenance. The data collected is used to monitor, control, and enhance the efficiency of the system

Systemdienstleistungserbringung durch intelligente Gebäude

Within the ongoing transition of energy systems, new technologies are integrated into electrical distribution systems—e. g. distributed generation, electrical storage, electric vehicles and automated building energy management—which transform buildings into actively participating components inside the grid. This thesis analyses the influences of those intelligent buildings’ capabilities of optimizing their in-house energy flows on low-voltage grids and discusses the usability of those capabilities to provide system services. In order to minimize the limitations which arise for the economic acting on energy markets for the inhabitants of such buildings, the traffic light concept is shaped as an approach to provide necessary needed system services. Firstly, a technical traffic light is introduced to determine critical situations in the grid. Secondly, a topological traffic light identifies active components that can reasonably participate in the clearance of a critical situation. Thirdly, aspects of coordination by the traffic light are tackled by a closed-loop feedback mechanism that controls utility equipment and intelligent buildings by utilizing a two-staged mechanism for demand response. The three parts of the proposed traffic light approach are implemented in a Regional Energy Management System that utilizes a proposed Extended Generic Observer/Controller-Architecture. For a close-to-reality evaluation three reference grids for a rural, village, and suburban residential low voltage grid are derived from literature as well as three scenarios for the distribution of active components. In particular distributed generation, electrical storage and electric vehicles. The simulation of intelligent buildings, utility equipment, and the low voltage grid as well as the Regional Energy Management System are implemented in a Co-Simulation environment that extends the Organic Smart Home to a microgrid simulation. Furthermore, this simulation is extended towards a Software-in-a-Hardware-Loop-Environment comprising the Co-Simulation and the KIT Energy Smart Home Lab as a real intelligent building, to comply with the necessity of evaluating the Regional Energy Management System with real hardware. Here, a loose coupling of software and hardware components is established by using event-based communication schemes utilizing a message bus and an artificial mains is used to align the environmental conditions between simulation and real building. The capabilities of the Regional Energy Management System to stabilize low voltage systems, especially in future scenarios, are investigated in simulation studies and its operation is successfully demonstrated in the presented Software-in-a-Hardware-Loop-Environment during a six-day test phase in the real intelligent building

Estimation of Energy Activity and Flexibility Range in Smart Active Residential Building

The smart active residential buildings play a vital role to realize intelligent energy systems by harnessing energy flexibility from loads and storage units. This is imperative to integrate higher proportions of variable renewable energy generation and implement economically attractive demand-side participation schemes. The purpose of this paper is to develop an energy management scheme for smart sustainable buildings and analyze its efficacy when subjected to variable generation, energy storage management, and flexible demand control. This work estimate the flexibility range that can be reached utilizing deferrable/controllable energy system units such as heat pump (HP) in combination with on-site renewable energy sources (RESs), namely photovoltaic (PV) panels and wind turbine (WT), and in-house thermal and electric energy storages, namely hot water storage tank (HWST) and electric battery as back up units. A detailed HP model in combination with the storage tank is developed that accounts for thermal comforts and requirements, and defrost mode. Data analytics is applied to generate demand and generation profiles, and a hybrid energy management and a HP control algorithm is developed in this work. This is to integrate all active components of a building within a single complex-set of energy management solution to be able to apply demand response (DR) signals, as well as to execute all necessary computation and evaluation. Different capacity scenarios of the HWST and battery are used to prioritize the maximum use of renewable energy and consumer comfort preferences. A flexibility range of 22.3% is achieved for the scenario with the largest HWST considered without a battery, while 10.1% in the worst-case scenario with the smallest HWST considered and the largest battery. The results show that the active management and scheduling scheme developed to combine and prioritize thermal, electrical and storage units in buildings is essential to be studied to demonstrate the adequacy of sustainable energy buildings

Data-driven remote fault detection and diagnosis of HVAC terminal units using machine learning techniques

The modernising and retrofitting of older buildings has created a drive to install building management systems (BMS) aimed to assist building managers pave the way towards smarter energy use, improve maintenance and increase occupants comfort inside a building. BMS is a computerised control system that controls and monitors a building’s equipment, services such as lighting, ventilation, power systems, fire and security systems, etc. Buildings are becoming more and more complex environments and energy consumption has globally increased to 40% in the past decades. Still, there is no generalised solution or standardisation method available to maintain and handle a building’s energy consumption. Thus this research aims to discover an intelligent solution for the building’s electrical and mechanical units that consume the most power. Indeed, remote control and monitoring of Heating, Ventilation and Air-Conditioning (HVAC) units based on the received information through the thousands of sensors and actuators, is a crucial task in BMS. Thus, it is a foremost task to identify faulty units automatically to optimise running and energy usage. Therefore, a comprehensive analysis on HVAC data and the development of computational intelligent methods for automatic fault detection and diagnosis is been presented here for a period of July 2015 to October 2015 on a real commercial building in London. This study mainly investigated one of the HVAC sub-units namely Fan-coil unit’s terminal unit (TU). It comprises of the three stages: data collection, pre-processing, and machine learning. Further to the aspects of machine learning algorithms for TU behaviour identification by employing unsupervised, supervised, and semi-supervised learning algorithms and their combination was employed to make an automatic intelligent solution for building services. The accuracy of these employed algorithms have been measured in both training and testing phases, results compared with different suitable algorithms, and validated through statistical measures. This research provides an intelligent solution for the real time prediction through the development of an effective automatic fault detection and diagnosis system creating a smarter way to handle the BMS data for energy optimisation

Methods for Optimal Microgrid Management

Abstract During the last years, the number of distributed generators has grown significantly and it is expected to become higher in the future. Several new technologies are being de-veloped for this type of generation (including microturbines, photovoltaic plants, wind turbines and electrical storage systems) and have to be integrated in the electrical grid. In this framework, active loads (i.e., shiftable demands like electrical vehicles, intelligent buildings, etc.) and storage systems are crucial to make more flexible and smart the dis-tribution system. This thesis deals with the development and application of system engi-neering methods to solve real-world problems within the specific framework of microgrid control and management. The typical kind of problems that is considered when dealing with the manage-ment and control of Microgrids is generally related to optimal scheduling of the flows of energy among the various components in the systems, within a limited area. The general objective is to schedule the energy consumptions to maximize the expected system utility under energy consumption and energy generation constraints. Three different issues related to microgrid management will be considered in detail in this thesis: 1. The problem of Nowcasting and Forecasting of the photovoltaic power production (PV). This problem has been approached by means of several data-driven techniques. 2. The integration of stations to charge electric vehicles in the smart grids. The impact of this integration on the grid processes and on the demand satisfaction costs have been analysed. In particular, two different models have been developed for the optimal integration of microgrids with renewable sources, smart buildings, and the electrical vehicles (EVs), taking into account two different technologies. The first model is based on a discrete-time representation of the dynamics of the system, whereas the second one adopts a discrete-event representation. 3. The problem of the energy optimization for a set of interconnencted buildings. In ths connection, an architecture, structured as a two-level control scheme has been developed. More precisely, an upper decision maker solves an optimization problem to minimize its own costs and power losses, and provides references (as 3 regars the power flows) to local controllers, associated to buildings. Then, lower level (local) controllers, on the basis of a more detailed representation of each specific subsystem (the building associated to the controller), have the objective of managing local storage systems and devices in order to follow the reference values (provided by the upper level), to contain costs, and to achieve comfort requirements

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

Intelligent Energy Optimization for User Intelligible Goals in Smart Home Environments

Intelligent management of energy consumption is one of the key issues for future energy distribution systems, smart buildings, and consumer appliances. The problem can be tackled both from the point of view of the utility provider, with the intelligence embedded in the smart grid, or from the point of view of the consumer, thanks to suitable local energy management systems (EMS). Conserving energy, however, should respect the user requirements regarding the desired state of the environment, therefore an EMS should constantly and intelligently find the balance between user requirements and energy saving. The paper proposes a solution to this problem, based on explicit high-level modeling of user intentions and automatic control of device states through the solution and optimization of a constrained Boolean satisfiability problem. The proposed approach has been integrated into a smart environment framework, and promising preliminary results are reporte

Determination of the influence of specific building regulations in smart buildings

The automation of domestic services began to be implemented in buildings since the late nineteenth century, and today we are used to terms like ‘intelligent buildings’, ‘digital home’ or ‘domotic buildings’. These concepts tell us about constructions which integrate new technologies in order to improve comfort, optimize energy consumption or enhance the security of users. In conjunction, building regulations have been updated to suit the needs of society and to regulate these new facilities in such structures. However, we are not always sure about how far, from the quantitative or qualitative point of view, legislation should regulate certain aspects of the building activity. Consequently, content analysis is adopted in this research to determine the influence of building regulations in the implementation of new technologies in the construction process. This study includes the analysis of different European regulations, the collection and documentation of such guidelines that have been established and a study of the impact that all of these have had in the way we start thinking an architectural project. The achievements of the research could be explained in terms of the regulatory requirements that must be taken into account in order to achieve a successful implementation of a home automation system, and the key finding has been the confirmation of how the design of smart buildings may be promoted through specific regulatory requirements while other factors, such as the global economic situation, do not seem to affect directly the rate of penetration of home automation in construction