Modeling and Optimization of Smart Building Energy Management System Considering Both Electrical and Thermal Load

Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.

Simulating Physiological Potentials of Daylight Variables in Lighting Design

A holistic approach to daylight dynamics in our built environment can have beneficial outcomes for both physiological and visual effects on humans. Simulations of how daylight variables affect light levels on the horizontal work plane are compared to their physiological effects, measured as melanopic EDI (Melanopic Equivalent Daylight Illuminance) on a vertical plane. The melanopic EDI levels were calculated in a simulated office space in ALFA software (Adaptive Lighting for Alertness) employing the daylight variables of orientation, time of day, season, sky conditions and spatial orientation. Results were analyzed for how daylight design can contribute to the physiological effects of dynamic light in office buildings. Daylight is shown to be a sufficient light source in the majority of cases to meet the recommended values of EDI and provide the suggested horizontal lx level according to the Danish Standards. A mapping of daylight conditions, focusing on the specific factors presented here, can provide guidelines in the design process and future smart building systems. The complex interrelationship between these parameters is important to acknowledge when working with daylight dynamics as a sustainable element in architecture and lighting design.

A Low-Cost Monitoring Platform and Visual Interface to Analyse Thermal Comfort in Smart Building Applications Using a Citizen–Scientist Strategy

Smart building issues are critical for current energy and comfort managing aspects in built environments. Nevertheless, the diffusion of smart monitoring solutions via user-friendly graphical interfaces is still an ongoing issue subject to the need to diffuse a smart building culture and a low-cost series of solutions. This paper proposes a new low-cost IoT sensor network, exploiting Raspberry Pi and Arduino platforms, for collecting real-time data and evaluating specific thermal comfort indicators (PMV and PPD). The overall architecture was accordingly designed, including the hardware setup, the back-end and the Android user interface. Eventually, three distinct prototyping platforms were deployed for initial testing of the general system, and we analysed the obtained results for different building typologies and seasonal periods, based on collected data and users’ preferences. This work is part of a large educational and citizen science activity.

Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management

Data-driven fault detection and diagnosis (FDD) methods, referring to the newer generation of artificial intelligence (AI) empowered classification methods, such as data science analysis, big data, Internet of things (IoT), industry 4.0, etc., become increasingly important for facility management in the smart building design and smart city construction. While data-driven FDD methods nowadays outperform the majority of traditional FDD approaches, such as the physically based models and mathematically based models, in terms of both efficiency and accuracy, the interpretability of those methods does not grow significantly. Instead, according to the literature survey, the interpretability of the data-driven FDD methods becomes the main concern and creates barriers for those methods to be adopted in real-world industrial applications. In this study, we reviewed the existing data-driven FDD approaches for building mechanical & electrical engineering (M&E) services faults and discussed the interpretability of the modern data-driven FDD methods. Two data-driven FDD strategies integrating the expert reasoning of the faults were proposed. Lists of expert rules, knowledge of maintainability, international/local standards were concluded for various M&E services, including heating, ventilation air-conditioning (HVAC), plumbing, fire safety, electrical and elevator systems based on surveys of 110 buildings in Singapore. The surveyed results significantly enhance the interpretability of data-driven FDD methods for M&E services, potentially enhance the FDD performance in terms of accuracy and promote the data-driven FDD approaches to real-world facility management practices.

Using virtual reality (VR) simulation to help developing a smart meeting space with AI Product to support moderator

Smart building has a pivotal role in promoting the industrialization of buildings. Meeting room is a common type of public space. Developing a smart meeting space can play an important role in addressing the issue of research on smart building. In this paper, we develop a smart meeting space with AI product to support moderator. We create 3D model of the meeting room and import it into VR headset to having an experiment. 20 participants are engaged to answer questionnaire and have interview. The results of this study show that most of the participants think the meeting space we develop is smart than usual one. We also found that participants expect functions of smart meeting room in AI assistance, energy saving, smart display solution, remote meeting. And participants expect devices of smart meeting room in AI assistance product, sensors, and devices for remote meeting.

Comparative study on Conventional and Smart Building Material in terms of TQC

There are several new-age „smart‟ building materials which are cost effective and long lasting and which would help to build or rebuild smart buildings, as a more eco- friendly and sustainable habitat. In India since ancient time construction is done by using conventional building materials. But now a days concept of smart building materials is introduced. These are durable, eco-friendly, cost effective. Still use of conventional materials is more than smart building materials. Due to lack of knowledge about smart building materials we don't use such materials. Control over quality and sustainability of finished building. Justification of the environmental cost of manufactures. So with the help of project i.e. Comparative study between smart and conventional building, there is comparison as per cost, strength, durability. In this, how both buildings are different in their features like long lasting life, implementation of materials, speed of construction, sustainability of materials is shown. For this project there is use of software for developing, analyzing and designing the reinforced concrete building. Keywords: smart material, conventional material, cost

ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network

The Internet of Things (IoT) interconnects massive cyber-physical devices (CPD) to provide various applications, such as smart home and smart building. Bluetooth Mesh is an emerging networking technology, which can be used to organize a massive network with Bluetooth Low Energy (BLE) devices. Managed-flooding protocol is used in Bluetooth Mesh to route the data packets. Although it is a highly desirable option when data transmission is urgent, it is inefficient in a larger and denser mesh network due to the collisions of broadcast data packets. In this paper, we introduce ACE: a Routing Algorithm based on Autonomous Channel Scheduling for Bluetooth Mesh Network. ACE relies on the existing Bluetooth Mesh messages to distribute routes without additional traffic overhead and conducts a beacon-aware routing update adaptively as the topology evolves. In ACE, BLE channel resources can be efficiently utilized by a channel scheduling scheme for each node locally and autonomously without any neighborly negotiation. We implement ACE on the nRF52840 from Nordic Semiconductor and evaluate its effectiveness on our testbed. Compared to the Bluetooth Mesh, our experiments proved that ACE could reduce the end-to-end latency by 16%, alleviate packets collisions issues, and increase the packet delivery ratio (PDR) by 30% under heavy traffic. Moreover, simulation results verified that ACE has better scalability when the size and density of networks become larger and denser.