Energy Efficiency In Smart Buildings Research Articles

A fog-based hybrid intelligent system for energy saving in smart buildings

In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, to constantly improve its performance by learning users’ needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users’ needs and preferences.

A computational toolchain for the automatic generation of multiple Reduced-Order Models from CFD simulations

This paper describes the development of a systematic tool chain capable of automatically extracting accurate and efficient Reduced-Order Models (ROMs) from Computational Fluid Dynamics (CFD) simulations. These ROMs can then be used to support the design and operation of Near-Zero Energy Buildings (NZEB), with a higher accuracy than traditional zonal models but at a fraction of the computational cost of CFD. This study assesses the accuracy and time to solution of these ROMs when solved for appropriate Boundary Conditions (BCs), found in the built environment, in order to define the usability envelope of the automatically extracted ROMs. The parameters used in this study are inlet temperatures (K) and mass flow rates (kg/s). Results demonstrate that the absolute error can be maintained at under 0.5 K for changes in temperature of up to ±15 K, and under 0.25 K for changes in mass flow rates of up to ±45% of the original value. The results show that this method has the potential for applications in the built environment where the ROM accuracy and low computational cost can bridge a gap between low order RC models and high order CFD, further improving the energy efficiency in smart buildings.

Improved Firefly Algorithm Based on Genetic Algorithm Operators for Energy Efficiency in Smart Buildings

Firefly algorithm (FA) is an easily implementable, robust, simple and flexible technique, but the major drawback associated with this technique is the imbalanced exploration and exploitation during firefly position changing stage. This imbalanced relation degrades the solution quality which ultimately results in either skipping the most optimal solution even present in the vicinities of the current solution or trapping the solution in the local optima. In this paper, this issue is resolved by introducing genetic algorithm (GA) operators namely selection, mutation and crossover operators in the firefly position stage of the standard FA. The performance of the proposed approach has been tested on energy consumption optimization and user comfort management inside smart building and has been compared with the standard FA, GA, artificial bee colony (ABC) and ant colony optimization (ACO) algorithm in terms of temperature, illumination, air quality and total power consumption minimization and user comfort maximization. The minimum, average, maximum and total power consumption and minimum, maximum and average user comfort were the performance evaluation parameters. The least amount of 145.39 kilowatt hour (kWh) of total power consumed for temperature control was observed for the proposed approach followed by ACO, ABC, FA and GA where the power consumed for temperature was observed as 173.68 kWh, 179.27 kWh, 181.93 kWh and 188.95 kWh, respectively. Similarly, for illumination control, the consumed power for the proposed model was 118.30 kWh followed by FA, ABC, ACO and GA where the power consumed was 146.93 kWh, 162.96 kWh, 169.28 kWh and 193.53 kWh, respectively. For air quality control, the minimum power of 186.94 kWh was found for the proposed algorithm followed by FA, ABC, ACO and GA where the power consumed was 229.01 kWh, 234.38 kWh, 240.47 kWh and 244.76 kWh, respectively. Likewise, maximum user comfort was observed for the proposed technique with the value of 0.94004/1 followed by ACO, ABC, FA and GA where the user comfort recorded was 0.939655/1, 0.93878/1, 0.938314/1 and 0.937896/1, respectively. The statistical analysis shows the efficiency of the proposed model for power consumption minimization and user comfort maximization.

Nonintrusive ultrasonic-based occupant identification for energy efficient smart building applications

The ability to non-intrusively identify people will enable smart buildings to customize the environment to meet occupants’ comfort level while saving energy. Occupant identification can help in energy savings effort in a building because we can retrieve each occupant’s temperature preference profile and choose the temperature that minimizes the total discomfort of a group in the building. To enable occupant identification in buildings, many methods used can be intrusive, such as using cameras or requiring the users to carry mobile gadgets or a smart phone. Non-intrusive techniques are gaining interest in smart building applications. In this paper, we present a non-intrusive ultrasonic based sensing technique to identify people by sensing their body shape and movement. The ultrasonic sensors are placed on the top and sides of doors to measure the height and width as the occupant walks through the instrumented doorway. Height and width and their related features can give a unique signature to occupants to identify them. In this study, the proposed system senses a stream of height and width data, recognizes the walking event when a person walks through the door, and extracts features that capture a person’s movement as well as physical shape. These features are fed to a clustering algorithm that associates each occupant with a distinct cluster. The system was deployed for a total of three months. The results show that the proposed approach achieves 95% accuracy with 20 occupants suggesting the suitability of our approach in commercial building settings. In addition, the results show that using girth to distinguish between occupants is more successful than using height. We show that this system generalizes beyond our datasets and works for different populations of different physical distributions.

Energy Efficient Smart Buildings Using Coordination Among Appliances Generating Large Data

Internet of Things based smart grids (SGs) represent a vision of future power systems which helps to provide electricity in a smart and user friendly way. Demand side management is one of the most important component of a SG which allows energy consumers to change their electricity consumption patterns to reduce the electricity consumption cost. In this paper, we propose a home energy management system which helps to achieve our desired objectives: reduced electricity consumption cost, peak to average ratio and maximize user comfort. For this purpose, we have proposed a scheduling technique which is a hybrid of already existing optimization techniques: bacteria foraging algorithm and harmony search algorithm and is named as hybrid bacterial harmony (HBH) algorithm. Being producer of electricity units to the consumers, a utility establishes an incentive based pricing tariff; we, on top of it have employed seasonal time of use tariff which allows consumers to take decisions regarding their consumption patterns. Moreover, we introduce the concept of coordination among smart appliances using dynamic programming (DP) approach. The coordination among appliances is achieved by the help of the large data generated from the appliances of multiple homes with the joint work of heuristic techniques and DP. The resultant coordination not only reduces the electricity cost but also increases the user comfort. At last, we evaluate the performance of our proposed energy management system using our proposed optimization technique HBH. To comparatively evaluate the performance of our proposed technique, we compare it with already existing techniques. Simulation results validate that the proposed technique effectively accomplish the desired objectives while considering the consumer comfort.

Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings

Internet of Things (IoT) provides to everyone new types of services in order to improve everyday life. Through this new technology, other recently developed technologies such as Big Data, Cloud Computing, and Monitoring could take part. In this work, we survey the four aforementioned technologies in order to find out their common operations, and combine their functionality, in order to have beneficial scenarios of their use. Despite the boarder concept of a smart city, we will try to investigate new systems for collecting and managing sensors’ data in a smart building which operates in IoT environment. As a bases technology for the proposed sensor management system, a cloud server would be used, collecting the data that produced from each sensor in the smart building. These data are easy to be managed and controlled from distance, by a remote (mobile) device operating on a network set up in IoT technology. As a result, the proposed solutions for collecting and managing sensors’ data in a smart building could lead us in an energy efficient smart building, and thus in a Green Smart Building.

Ethical issues of monitoring sensor networks for energy efficiency in smart buildings: a case study

The development of Internet of Things (IoT) based sensors has become crucial for analyzing and optimizing the energy-performance of buildings. However, researchers and professionals should be prepared to deal with the social and thus ethical issues arising from the use of such technologies. Based on a real case-study, we present a detailed analysis of the networks of stakeholders and the consequent ethical issues related to the implementation of energy and IEQ sensors network in an Italian university campus. Alternative scenarios for eliminating or reducing the criticalities related to security and privacy issues are proposed.

Providing Personalized Energy Management and Awareness Services for Energy Efficiency in Smart Buildings.

Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants’ behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants’ behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants’ lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research.

Cellulose nanofibers enable paraffin encapsulation and the formation of stable thermal regulation nanocomposites

Non-leaking, green materials with high content of phase change materials (PCM) can conserve solar energy and contribute to a sustainable society. Here, paraffin was encapsulated by nanocellulose (CNF) through a pickering emulsion method, while simultaneously forming a composite material. The thermodynamic drive for phase separation was confirmed by molecular modeling. Particle formation was characterized by dynamic light scattering and they were processed into stable PCM/CNF composites in the form of PCM paper structures with favorable mechanical properties. The PCM composite was lightweight and showed a solid content of paraffin of more than 72wt%. Morphology was characterized using FE-SEM. The thermal regulation function of the PCM composite was demonstrated in the form of a model roof under simulated sunlight. No obvious leakage was observed during heating/cooling cycles, as supported by DSC and SAXS data. The PCM composite can be extended to panels used in energy-efficient smart buildings with thermal regulation integrated in load-bearing structures.

An optimal energy co-scheduling framework for smart buildings

The Heating, Ventilation and Air Conditioning (HVAC) system accounts for nearly half of the energy consumption of a typical building. Additionally, the need for HVAC changes over hours and days as does the electric energy price. Level of comfort of the building occupants is, however, a primary concern, which tends to overwrite pricing. Dynamic HVAC control under a dynamic energy pricing model while meeting an acceptable level of occupants' comfort is thus critical to achieve energy efficiency in buildings in a sustainable manner. Finally, there is the possibility that the building is equipped with some renewable sources of power such as solar panels mounted on the rooftop. The presence of Hybrid Electrical Energy Storage (HEES) system in a target building would enable peak power shaving by adopting a suitable charging and discharging schedule for each Electrical Energy Storage (EES) element, while simultaneously meeting building energy efficiency and user comfort requirements. Achieving this goal requires detailed information (or predictions) about the amount of local power generation from the renewable source plus the power consumption load of the building.This paper addresses the co-scheduling problem of HVAC control and HEES system management to achieve energy-efficient smart buildings, while also accounting for the degradation of the battery state-of-health during charging and discharging operations (which in turn determines the amortized cost of owning and utilizing a battery storage system). A time-of-use dynamic pricing scenario is assumed and various energy loss components are considered, including power dissipation in the power conversion circuitry, the rate capacity effect in the batteries, and the self-discharge in the super-capacitor. A global optimization framework targeting the entire billing cycle is presented and an adaptive co-scheduling algorithm is provided to dynamically update the optimal HVAC air flow control and the HEES system management in each time slot during the billing cycle to mitigate the prediction error of unknown parameters. Experimental results show that the proposed algorithm achieves up to 10% in the total electric utility cost compared with some baseline methods.

A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings

We present a mathematical model to diagnose HVAC systems in buildings based upon the analysis of a small number of ambient state variables. In particular, the equations of the model accurately fit recorded data of temperature, relative humidity and carbon dioxide concentration in different workplaces. For validation, data were obtained under different conditions and with different sensors. In particular, we designed and fabricated a wireless sensor that measures and transmits data to a remote device and we also applied our model to data collected using a commercial sensor. For each case, information was obtained that could be used to understand and predict the evolution of ambient variables that impact thermal comfort and energy consumption in buildings. The tools presented here can thus be of great interest to achieve affordable, smart energy-efficient buildings, while adhering to environmental laws and comfort for work spaces.

Integrated living environment: Measurements in modern energy efficient smart building with implemented the functionality of telemedicine

Abstract The paper depicts the metrology as a vivid fundamental science with implications for possible applications in various engineering fields. This depiction uses the energy efficient smart building with embedded the functionality of telemedicine as the example of integrated living environment. State of the art for such issue and object has been reviewed here. Results provided for exemplary CTE’s office building show the effectiveness of performance of multisensory measurement network in the task of heating system optimization. These results highlight also the meaning of fusion algorithms in management of complex systems. Although the building infrastructure described in the paper aggregates a bench of open questions, it moves also the perception to more generalized objects, which can be explained in the context of complex systems, networking science, scaling rules, etc. Proposed attempt has opened the challenges for researchers and engineers in numerous domains. This domains and problems have been denoted and shortly characterized in the paper.

Intelligent multi-objective control and management for smart energy efficient buildings

Abstract Energy management in buildings has become an increasing trend in their transformation to smart and efficient in utilizing energy resources. The potential affinity of these buildings is coping energy sustainability, security and reliability. Building energy management has been primarily associated with development and implementation of an efficient control scheme. The challenging task of building controls is to achieve indoor building environment comfort with improved energy efficiency. In this study, multi-agent control system has been developed in combination with stochastic intelligent optimization. The multi-objective genetic algorithm (MOGA) and hybrid multi-objective genetic algorithm (HMOGA) are used as optimization algorithms. The corresponding case study simulations of effective management of energy and user comfort are presented. The developed control system provides substantial enhancement in energy efficiency and indoor environmental comfort in smart buildings. An energy efficiency of 31.6% has been achieved with an 8.1% improvement of comfort index using the HMOGA technique.

Big data: the key to energy efficiency in smart buildings

Due to the high impact that energy consumption by buildings has at global scale, energy-efficient buildings to reduce $$\mathrm{CO}_2$$CO2 emissions and energy consumption are needed. In this work we present a novel approach to energy saving in buildings through the identification of the relevant parameters and the application of Soft Computing techniques to generate predictive models of energy consumption in buildings. Using such models it is possible to define strategies for optimizing the day-to-day energy consumption of buildings. To verify the feasibility of this proposal, we apply our approach to a reference building for which we have contextual data from a complete year of monitoring. First, we characterize the building in terms of its contextual features and energy consumption, and then select the most appropriate techniques to generate the most accurate model of our reference building charged with estimating the energy consumption, given a concrete set of inputs. Finally, considering the energy usage profile of the building, we propose specific control actions and strategies to save energy.

Environmental-Friendly Amendment of the Non-Resident Supervision Systems for the Private Small Buildings

Current supervision systems have been changed by the demands of social, physical and institutional environment, and have a role of preventing safety accident and in improving the construct ability through the analysis of issues in quality, time, construction, safety, and environmental management. The public sector "Construction Technology Management Act", a general and private sector "Building Act" and "Certified Architects Act", the residential building "Housing Act" are dealt with supervision systems, respectively, but private small building construction is excluded from the discussion of the main targets because of their relatively small scale and the lack of social interests, Thus, this study focused on the small buildings for improving the non-resident supervision systems. Survey results revealed that the non-resident supervisor needs to be selected not by clients but by officers in order to obtain its publicity. Based on the proper selection and execution of the supervision system suggested in this study, the potential effects can be summarized as 1) recovering of publicity, 2) correcting abnormal practice, and 3) realizing design intention for increasing the public value of buildings. In addition, findings in this study will be effective to amend current non-resident supervision systems for improving the quality of buildings and communities as well as for adopting the environmental-friendly energy-efficient smart building technologies.

A smart building power management concept: Smart socket applications with DC distribution

Abstract Recent developments in power electronics increase DC bus utilization in electrical distribution systems due to its numerous advantages compared to AC distribution system in term of energy efficiency, safety, electromagnetic compatibility and renewable energy integration. This study presents a power management system concept based on domestic DC distribution with smart sockets for future smart houses. Energy efficient smart buildings are possible by integrating smart meter, smart sockets, domestic renewable energy generation and energy storage systems for integrated energy management, and this integrated system supports demand side load management, distributed generation and distributed storage provisions of future smart grids. Coming era of smart grids has implications for domestic DC distribution concepts with smarts sockets. Paper describes use of smart DC sockets as an integral part of building power management automation and presents a load shedding algorithm for plug load control for buildings. Simulations show performance of the proposed system components.

Stochastic optimized intelligent controller for smart energy efficient buildings

Abstract Smart buildings are rising concept for energy resource management in the buildings to reduce energy consumption and wastage. Energy management and control system comprises of variety of technologies, which affect human working comfort. The challenging task of the building control is to achieve interior building environment comfort with high-energy efficiency. In this study, multi-agent control system has been developed in combination with stochastic optimization using genetic algorithm (GA). The corresponding simulations of effective management of energy and consumer comfort are presented. The developed control system provides significant improvement in energy consumption and interior environmental comfort in smart buildings.