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IT systems in the hotel industry mainly focused on addressing routine operational issues during hotel operations. Pakistan’s hotel industry has historically been criticized for being reluctant to make full use of IT. This paper reports and analyzes the results of a recent survey of IT applications in Pakistan hotels. This research is intended to highlight factors that can be considered critical for the successful application, particularly to deter a wider use of ERP (Enterprise resource planning) systems in the hospitality sector in Pakistan. The work also helps us understand how ERP systems can provide value for companies in this field, in particular in the hotel industry. The reference model provides the basis for explaining how the current supplier’s product responds to the needs of the market. This research provides an initial systematic review of ERP, Information System, PMS, POS, Back-Office Software, HRIS, Inventory Software, Business Performance, Automation, System Integration, E-commerce in hospitality industry. In this research ERP systems are critically analyzed which are being used in hotel industry of Pakistan. Finally there is review of findings of effect if ERP and MIS implement on hospitality industry in Pakistan that due to less awareness, fear, cost, they don’t want to implement or not need to change. After completing the research it will be clear that how hotel Industry bring change by applying this software or upgrade them self.

This study focuses on calibration and test campaigns of an IoT camera-based sensor system to monitor occupancy, as part of an ongoing research project aiming at defining a Building Management System (BMS) for facility management based on an occupancy-oriented Digital Twin (DT). The research project aims to facilitate the optimization of building operational stage through advanced monitoring techniques and data analytics. The quality of collected data, which are the input for analyses and simulations on the DT virtual entity, is critical to ensure the quality of the results. Therefore, calibration and test campaigns are essential to ensure data quality and efficiency of the IoT sensor system. The paper describes the general methodology for the BMS definition, and method and results of first stages of the research. The preliminary analyses included Indicative Post-Occupancy Evaluations (POEs) supported by Building Information Modelling (BIM) to optimize sensor system planning. Test campaign are then performed to evaluate collected data quality and system efficiency. The method was applied on a Department of Politecnico di Milano. The period of the year in which tests are performed was critical for lighting conditions. In addition, spaces’ geometric features and user behavior caused major issues and faults in the system.Incorrect boundary definition: areas that are not covered by boundaries; thus, they are not monitored

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.

Building energy management, in terms of both adopted technologies and occupant consumption behaviour, is becoming an essential element of sustainability and climate change mitigation programs. The global COVID-19 pandemic and the consequential lockdowns and remote working had a notable impact on office building operations and provided a unique opportunity for building energy consumption studies. This paper investigates the COVID-19 effects on energy consumption in office buildings, particularly in the education sector. We studied different buildings at the University of Technology Sydney (UTS) campus before and during the pandemic period. The results demonstrate that the changes in energy consumption due to COVID-19 in different UTS faculties are not as strongly correlated with occupant activity. The comparison shows that buildings with administrative offices or classrooms are easier to switch to a remote-working mode than those housing laboratories and special equipment. During weekends, public holidays, or conditions requiring working from home, the per capita energy consumption increases significantly translating into lower energy efficiency. Our findings highlight the essential need for some changes in office building energy management systems. We provide recommendations for office and commercial buildings in general to deal with similar crises and to reduce energy overconsumption in normal situations.

Improving energy efficiency and increasing the level of intelligence are two main factors determining the current development trends for new and modernized buildings. They are especially important in the perspective of development of prosumer installations and local microgrids. A key tool to achieve these goals is a well-designed and implemented Building Automation and Control System (BACS). This paper presents a new hybrid approach to the design and technical organization of BACS based on the provisions of the EN 15232 standard and the guidelines of the Smart Readiness Indicator (SRI) defined in the Energy Performance of Buildings Directive 2018 (EPBD 2018). The main assumptions of this hybrid approach along with examples of functional BACS designs for small prosumer installations organized according to them are provided. Potential impact on building energy performance is discussed as well. Finally, a SWOT analysis of the possibility of merging the EN 15232 standard guidelines and the SRI assessment methodology to develop uniform technical guidelines for the BACS functions design and evaluation of their impact on the buildings’ energy efficiency are discussed.

In building management, energy optimization is one of the main concern that needs to be automated. For automation, an intelligent system needs to be developed. However, an intelligent system needs to be trained in a large dataset before it can be used reliably. In this paper, we present a transfer learning scheme to develop an intelligent system for smart building management system. Specifically, the intelligent system is able to count human inside a room, which can be utilized to adaptively adjust energy usage in a room. The transfer learning scheme employs a deep learning model that is pretrained on ImageNet dataset. To enable the human counting capability, the model is trained on a dataset specifically collected for human counting case.

In this paper the idea of functioning of Building Management Systems and Object Management Systems in intelligent buildings is presented. New functionalities of intelligent buildings resulting from the introduction of microgeneration are described. Low-Power Wide-Area Networks (LPWAN) are characterized and compared. The selected Long-Range (LoRaWAN) technology is tested for its use for communication with energy meters and monitoring the power supply network in intelligent buildings. In the paper a new system for reading and monitoring the network is proposed, consisting of hardware, communication, and application layers. A key element of the system is a specially developed converter, which has been designed and tested in a real urban environment. Using our solution in practice could allow to change the architecture of a measurement data acquisition system to much more flexible and efficient.

This publication presents a system for integrating machine learning and artificial intelligence solutions with building automation systems. The platform is based on cloud solutions and can integrate with one of the most popular virtual building management solutions, HomeAssistant. The System uses communication based on the Message Queue Telemetry Transport (MQTT) protocol. The example machine learning function described in this publication detects anomalies in the electricity waveforms and raises the alarm. This information determines power quality and detects system faults or unusual power consumption. Recently, increasing electricity prices on global markets have meant that buildings must significantly reduce consumption. Therefore, a fundamental element of energy consumption diagnostics requires detecting unusual forms of energy consumption to optimise the use of individual devices in home and office installations.

Smart buildings conserve energy and create a responsive, comfortable, and productive indoor environment for users and occupants. As a crucial component of smart buildings, smart building management system (SBMS) should provide a wide range of functions and bring about the intended benefits upon successful deployment. This paper identifies salient SBMS attributes and explores key factors influencing building professionals’ intention to use the system in commercial buildings. Responses were collected from 327 Hong Kong building professionals. Data were analyzed by exploratory factor analysis and structural equation modeling based on the refined Unified Theory of Acceptance and Use of Technology (UTAUT). Exploratory factor analysis shows that intelligent building operations and safety and recovery readiness are two dimensions of SBMS emerged. Specifically, intelligent building operations include intelligent and optimal scheduling of building systems, monitor and control of building facilities, having an intelligent and interactive interface, and enabling alarm settings and automatic notifications, showing the importance on the application of electrical engineering in smart building management. Structural equation model (SEM) results indicate that facilitating conditions affect habit, hedonic motivation, social influence, performance expectancy and effort expectancy. Additionally, habit, hedonic motivation and effort expectancy significantly affect building professionals’ intention to use SBMS. Practical implications of SBMS attributes for energy management and the ways in which SBMS is encouraged to be used by building professionals are given.

This research investigates the energy consumption of buildings managed by traditional Building Management Systems (BMSs) and proposes the integration of Internet of Things (IoT) technology to enhance energy efficiency. Conventional BMSs often suffer from significant energy wastage and safety hazards due to sensor failures or malfunctions. These issues arise when building systems continue to operate under unknown conditions while the BMS is offline, leading to increased energy consumption and operational risks. The study demonstrates that integrating IoT systems with existing BMSs can substantially improve energy efficiency in smart buildings. The research involved designing a system architecture prototype, performing MATLAB simulations, and a real-life case study which revealed that IoT devices are effective in reducing energy waste, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems and lighting. Additionally, an auxiliary bypass system was incorporated in parallel with the IoT system to enhance reliability in the event of IoT system failures. Preliminary findings indicate that the integration of IoT systems with traditional BMSs significantly boosts energy efficiency and safety in smart buildings. Simulation results reveal an hourly average power savings of 36.8 kw with the integrated failsafe model for all scenarios. This integration offers a promising solution for advancing energy management practices and policies, thereby improving both operational performance and sustainability in building management.