Energy Management for Intelligent Buildings (original) (raw)
Related papers
International Journal of Scientific & Technology Research, 2020
Due to the scarcity of energy resources and the high cost of production and transmission, human beings are always looking to optimize energy consumption so that they can pay the lowest cost while using all the tools that need to consume energy. Consumption optimization is not only economically beneficial to the consumer but also beneficial to production units and the environment. Equipping residential buildings with smart equipment is a solution to this problem, the implementation of which can be costly at first, but in the long run can reduce many economic costs and environmental pollution. Smart control systems have high flexibility and can be easily adapted to different needs. The smart management system, using the latest technologies, is the percentage that creates ideal conditions, along with optimal energy consumption in buildings. Therefore, in this paper these systems examined, and we have tried to examine how to control and reduce electrical energy. In this regard, two opti...
Intelligent Management Systems for Energy Efficiency in Buildings
ACM Computing Surveys, 2014
In recent years, reduction of energy consumption in buildings has increasingly gained interest among researchers mainly due to practical reasons, such as economic advantages and long-term environmental sustainability. Many solutions have been proposed in the literature to address this important issue from complementary perspectives, which are often hard to capture in a comprehensive manner. This survey article aims at providing a structured and unifying treatment of the existing literature on intelligent energy management systems in buildings, with a distinct focus on available architectures and methodology supporting a vision transcending the well-established smart home vision, in favor of the novel Ambient Intelligence paradigm. Our exposition will cover the main architectural components of such systems, beginning with the basic sensory infrastructure, moving on to the data processing engine where energy saving strategies may be enacted, to the user interaction interface subsystem, and finally to the actuation infrastructure necessary to transfer the planned modifications to the environment. For each component we will analyze di↵erent solutions, and we will provide qualitative comparisons, also highlighting the impact that a single design choice can have on the rest of the system.
Home automation to reduce CO2 emissions associated with energy consumption of buildings
With the massive deployment of smart meters across Europe allowing digital measurements, energy companies and authorities have access to a consequent database of energy consumption data throughout a country. European Union (EU) Member States have the obligation of implementing smart meters covering 80 % of consumers by 2020 at the latest (Office, 2012). In contrast to European Directive 2012/27/EU (Office, 2012), Finnish legislation (18.1.2013/50) sets a deadline of 2014. The deployment of smart meters also brings up the issue of data security and use of the collected information, in particular in relation to the role of energy utilities and Public Institutions (Fhom and Bayarou, 2011). Legal obligations to increase energy efficiency also provide a motivation to the deployment of renewable energy sources, as a vector for energy production, and an increase in the energy efficiency of buildings. Home energy management can have a significant role in contributing to energy efficiency and cutting down peak load. This can be achieved through an active collaboration of energy consuming systems and the information network e.g. at the local level . Putting together the different factors mentioned involves the development of a smart energy network (SEN), capable of managing the energy system through constant monitoring.
Smart energy managements in the built environment
International Conference In PROTECTION, 2004
This paper presents an e-learning tool on smart indoor environment and energy management in the built environment. The methodology for the syllabus development is presented and justified. Questionnaires are developed and sent to potential trainees as well as to relevant companies to collect information regarding their training needs. Furthermore, the relevant courses offered by universities or professional bodies around the world are investigated. The syllabus is developed according to the results obtained through these ...
Smart Energy Management in the Built Environment
This paper presents an e-learning tool on smart indoor environment and energy management in the built environment. The methodology for the syllabus development is presented and justified. Questionnaires are developed and sent to potential trainees as well as to relevant companies to collect information regarding their training needs. Furthermore, the relevant courses offered by universities or professional bodies around the world are investigated. The syllabus is developed according to the results obtained through these two studies. The training tool consists of seven modules covering introduction, the control systems for smart buildings, the communication protocols, sensors and actuators, economic issues and contracting energy management.
Energy consumption in Finland doubled since the 1970’s and, at the same time, peak power levels have increased by 330 %. The objective of this work was to demonstrate that the development of smart buildings integrated in a smart grid would reduce total energy consumption in the residential sector, and also cut peak consumption levels. The theory part underlines that legislation should promote small scale renewable energy production systems. Standardizing smart buildings will enhance and accelerate the deployment of such technology. The role of end-users to realize the energy efficiency potential is highlighted and different feedback strategies are presented. In order to facilitate data exchange between the home and the grid, communication technologies must be developed. To this end, data safety and data privacy are of major concern as well as the ownership and access to data. In the experimental part, a detached house was modelled using MatLab/Simulink, in order to simulate the energy flux. The house is modelled to be located in Oulu, and climate data (temperature, wind speed, wind frequency, solar radiation data) are available on an hourly basis for the last 10 years. The house model details the lighting system and includes twenty-one appliances with different power rates. The variables used where also the number of inhabitants and bedrooms, and potential small-scale energy production systems (wind turbine, photovoltaic panels and fuel cell). Three levels of user responses were evaluated from ‘green’ to ‘brown’ users. The feedback methods assessed were self-comparison, inter-comparison, and a target based system. Automatic control for some appliances was also integrated, in order to optimize the system. The results indicate a potential of 30 % reduction of energy consumption, using energy efficient appliances (A/B label) over regular appliances (C/D label). Applying a smart meter resulted in 2-8 % reduction, depending on the user response. Delaying the use of appliances from the day to the night resulted in the flattened of the mean daily energy consumption profile. Cutting the peaks reduced energy consumption by 18 % during the day and increased it by 47 % during the night in average. It was concluded that an hourly pricing system calls for the development of an iterative model and would also require interaction between the house and the grid. It is expected that the deployment of smart buildings will be an essential part of a smart grid system, and a key element of improving energy efficiency in the residential sector. This research was funded by the Pohjoista Voimaa Ympäristötili foundation.
Sensors, actuators, and controllers, which collectively serve as the backbone of cyberphysical systems for building energy management, are one of the core technical areas of investment for achieving the U.S. Department of Energy (DOE) Building Technologies Office's (BTO's) goals for energy affordability in the national building stock-both commercial and residential. In fact, an aggregated annual energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20 th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms. First, centralized monitoring and control of operations through building automation systems (BAS) are prevalent in only 8% of floor space for small commercial buildings (<50,000 square feet) and 46% of floor space for large commercial buildings (>50,000 square feet) in the United States. This translates to 43% of the total floor space for the commercial building stock (U.S. Energy Information Administration [EIA] 2016). Similar to small commercial buildings, residential buildings typically do not have a centralized management system, although smart home assistants are beginning to take on this role. In the residential sector as of 2015, 41% of buildings had some type of programmable thermostat installed, but only 12% used the programmable functionality, and only 3% had a smart or learning thermostat that learns occupant behavior over time, eliminating the need for continual user activity (U.S. EIA 2017c). This number is steadily growing, with 40% of the 40 million thermostats sold in 2015 classified as smart (Parks Associates 2015). Second, most centralized systems currently installed exclusively manage heating, ventilating, and air conditioning (HVAC). These systems are typically separated from control of other building end uses such as common area lighting and plug loads. For example, home energy management systems usually consist of programmable thermostats for central and single-zone space conditioning, rather than more holistic management across multiple loads and appliances. Even modern systems incorporate a limited range of inputs and prescriptively map these inputs to control strategies to meet occupant needs and sometimes save energy. Much of installed equipment in buildings today is also not capable of digital communication and control. These conditions result in approaches that are customized in nature with new devices managing their own operation through built-in capabilities and intelligence. While efforts to embed intelligence in buildings that enable "smart" operations for energy management have proliferated in the past decade, they have generally lagged behind other sectors and applications (e.g., largescale industrial process plants, automotive, aerospace) due to several factors. These include utilization in less operationally critical applications (e.g., occupant comfort instead of safety and security); the fragmented nature of the buildings market (e.g., owner-owned and tenant-occupied); the customized nature of incorporating intelligence into building equipment rather than integrating into the design process; and the diversity of systems configurations and limited modeling and integration capabilities of stochastic variables (e.g., occupants, weather forecasts). As such, building controls are still predominately designed to meet short-term thermal and ventilation loads and are rule-based and reactive, rather than adaptive and autonomous, in nature. 2 Calculated based on EIA AEO 2017 data using Scout tool. 3 Cost premium based on 1-year payback period. 4 Full technical potential assuming no competition with measures from other technologies. 5 Based on all residential buildings; single/mobile homes use 0.0021 nodes/ft 2 floor and make up ~87% of all residential square footage (from residential EIA AEO 2017 microtables); multifamily homes use 0.0041 nodes/ft 2 floor and make up ~13% of all residential square footage (EIA AEO 2017 microtables). 6 Based on 0.002 nodes/ft 2 for large office commercial building. 7 Based on all commercial building types. INNOVATIONS IN SENSORS AND CONTROLS FOR BUILDING ENERGY MANAGEMENT: Research and Development Opportunities Report for Emerging Technologies xi INNOVATIONS IN SENSORS AND CONTROLS FOR BUILDING ENERGY MANAGEMENT: Research and Development Opportunities Report for Emerging Technologies xii 10 End uses labeled "other" include: for residential (small electric devices, heating elements, motors, swimming pool and hot tub heaters, outdoor grills, and any energy attributable to the residential buildings sector, but not directly to specific end uses) and for commercial (service station equipment, automated teller machines, telecommunications equipment, medical equipment, pumps, emergency electric generators, combined heat and power in commercial buildings, manufacturing performed in commercial buildings, and any energy attributable to the commercial buildings sector, but not directly to specific end uses).
Intelligent energy management system in buildings
2019
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 estimat...
The combination of physical world with computer capabilities would develop a new world which is able to think with a computer mind. In this research, we will investigate smart systems and review a number of cases which have utilized them. This paper attempts to answer the following questions: " Are smart systems able to manage energy consumption? " , " Given the considerable cost of smart systems, can one propose a cheaper alternative with the same efficiency at least for residential buildings? " , and " Which building groups best suit smart systems? The results of this study confirmed that the use of smart systems is cost effective for general, industrial and office buildings thanks to return on investment. With regard to residential buildings, however, one can minimize the waste of energy without suffering the costs of purchase and maintenance of smart systems, by using climatic design solutions such as selection of optimal building direction, optimization of ducts, adoption of efficient building design, and the utilization of appropriate attachments.
SmartBuildings: an AmI system for energy efficiency
2015 Sustainable Internet and ICT for Sustainability (SustainIT), 2015
Nowadays, the increasing global awareness of the importance of energy saving in everyday life acts as a stimulus to provide innovative ICT solutions for sustainability. In this scenario, the growing interest in smart homes has been driven both by socioeconomic and technological expectations. One of the key aspects of being smart is the efficiency of the urban apparatus, which includes, among others, energy, transportation and buildings. The present work describes SmartBuildings, a novel Ambient Intelligence system, which aims at reducing the energy consumption of "legacy" buildings by means of artificial intelligence techniques applied on heterogeneous sensor networks. A prototype has been realized addressing two different scenarios, i.e. the management of a campus and of a manufacturing facility. A complete description of the elements included in the case study is presented.