The paper presents the application of Internet of thing (IoT) in managing smart buildings and a proposal to study some of the functions, applications of building management system (BMS) in monitoring, controlling and using electricity effectively for high-rise buildings. Currently, high-rise buildings consume about 33% of global electricity. Managing energy consumption in the buildings is very important when the demand for electricity is increasing. Existing building management systems have high costs and reveal many weaknesses in data collection. Therefore, using the ARIMAX algorithm for predicts temperature, humidity and the amount of electricity that will be consumed in building which helps operators always plan to prepare the necessary energy source for the building, ensuring the electric energy is always provided fully, continuously and effectively
Buildings shape cities as those cities grow from and nurture people living and working within the built environment. Thus, the conceptualization of smart building should be brought closer to the smart city initiatives that particularly target ensuring and enhancing the sustainability and quality of urban life. In this paper, we propose that a smart building should be interlinked with a smart city surrounding it; it should provide good experiences to its various occupants and it should be in an ongoing state of evolving as an ecosystem, wherein different stakeholders can join to co-produce, co-provide and co-consume services. Smart buildings require a versatile set of smart services based on digital solutions, solutions in the built environment and human activities. We conducted a multiphase collaborative study on new service opportunities guided by a Design Thinking approach. The approach brought people, technology, and business perspectives together and resulted in key service opportunities that have the potential to make the buildings smart and provide enjoyable experience to the occupants who support their living and working activities in smart cities. This paper provides the resulting practical implications as well as proposes future avenues for research.
With the growing attention to smart buildings, local governments are seeking practical ways to optimize the energy consumption of commercial buildings. An ideal smart building is capable of monitoring its own energy consumption and adjusting the operation of electric devices, being lighting and air conditioners, based on the occupant behaviour. In this study, data had been obtained from the monitoring sensors in a commercial building located in the heart of Sydney from 2013 until 2020 on a 15-minute time intervals. The data derivation and analysis are intrinsically static at the moment which makes it difficult for building management to make instantaneous decision regarding the measures to be taken for a lower energy consumption. Using data analysis and visualization tools in Tableau, this study provides detailed insights about the trends in energy consumption in the given building. The outcomes facilitate the decision making for building management and can be seen as a milestone towards a dynamic optimization protocol in a bigger picture which is introduced in the second part of this study.
Smart buildings and smart cities are not the future perspectives anymore—the smart building integration into a smart city is an actual question for today and tomorrow. Development of smart buildings not only enhances the smart city concept but also promotes positivity to the urban development and national economy, and increases the quality of life of the whole population reacting to global challenges of sustainability. The innovative smart building and smart city technologies enable us to overcome these challenges by being employed through all real estate (RE) project development stages. The Evaluation Framework for Real Estate Development in Smart Cities created by the authors provides the possibility to assess the existing as well as to forecast future RE projects integration into a smart city during the whole life-cycle stage. The practical application of the presented evaluation framework was illustrated by the comparative case study. Based on the created smart building integration into a smart city evaluation framework for real estate development, 10 RE projects in Lithuania and over the world were assessed and rated by selected criteria relevant to different RE development stages. The evaluation results revealed that, especially at the design and construction stages, the existing intelligence of RE projects and/or cities is insufficient. Although real estate projects are technologically advanced as single entities, the integration into smart city networks is limited by interoperability capabilities of the cities or by different strategic goals settled by real estate developers.
Running costs of buildings represent a significant outlay for all businesses, thus finding a way to run facilities as efficiently as possible is vital. IoT-enabled Building Management Systems provide means for process and resource usage automation leading to overall efficiency improvements. Inferring spatial and temporal occupancy in all its forms (binary, numerical or continuous) is one of the key contextual inputs required for smart building management systems. In this work, we showcase design, implementation and experimental validation of a smart building occupancy detection and forecasting solution. The presented solution comprises three main building blocks: (1) A fog computing indoor positioning system (BLEMAT — Bluetooth Low Energy Microlocation Asset Tracking) which, combined with wireless access network monitoring processes, produces indoor location information in a semi-unsupervised manner; (2) Data analysis and pattern searching pipelines responsible for fusing data coming from different smart building and networking systems and deriving information on temporal and spatial occupancy patterns; (3) Long short-term memory (LSTM) neural networks trained to predict occupancy patterns in different areas of a smart building. Data analysis and neural network training are conducted on real-world smart building dataset which authors provide in public online repository. Experimental validation confirms that the proposed solution can provide actionable occupancy detection and prediction information, required by smart building management systems.
Purpose
Effective use of post-occupancy evaluation (POE) data – quantitative physical measurements and qualitative occupants’ perceptions are limited due to practical challenges and research gaps. Although building information modelling (BIM) has enabled a paradigm shift in the architecture, engineering and construction (AEC) industries, its use in facility management (FM) is still infancy. Limited research has used building performance data to enable changes to BIM models for the benefit of FM. This paper aims to propose the innovative process to collect and contextualize two fragmented types of POE data sets by filling methodological gap in POE research. Moreover, it presents innovative modelling techniques to facilitate BIM as a more effective platform to visualize such currently fragmented data sets in real-time while enabling a decision-making model to benefit facility managers.
Design/methodology/approach
The paper presents a process of capturing cloud-based POE data, both wireless sensor network-based physical measurement data and mobile app-based occupant perception data. Real-time capture and visualization of such building performance data was demonstrated through a pilot data collection. Subsequently, the innovative visualization of the cloud connected data is supported by a prototype game engine-based BIM model.
Findings
Cloud-based POE data, both quantitative physical measurements and qualitative occupants’ reported perceptions, can be effectively used in FM practice with the use of innovative data capture and visualization techniques in a beneficial manner for facility operation and management decisions. This paper also demonstrates the ability of BIM to serve as a “single source of truth” to support post-construction building performance data.
Originality/value
While addressing a number of research gaps, the paper provides a holistic approach to BIM-based performance monitoring for smart FM to achieve the ultimate vision of BIM enabled FM. The innovative system is expected to provide a powerful and practical tool for data collection, analysis and visualization for intelligent facility management decision making. This paper contributes to fill an important research and practice gap in the area of next generation smart building management practices.
Temperature and Load Consumption Forecast in Smart Building on Foundation IoT by ARIMA Algorithm
