Intelligent OPC Building Information Management System Based on IoT

With the rapid development of artificial intelligence, computers and Internet of Things technologies, building intelligence is becoming more and more popular. However, as the various functional subsystems and equipment of intelligent buildings are connected to each other, the traditional management system needs to monitor and modulate more and more programs, which will have a great impact on the interoperability of the subsystems. Therefore, a more complete and powerful management system is needed. OPC provides a unified standard that can effectively solve this problem. Therefore, the purpose of this article is to design an intelligent OPC building information management system based on the Internet of Things. This article first summarizes the development history and status quo of OPC technology, and then extends the design principles of building information management system based on OPC. Based on its design principles, a detailed analysis of the various subsystems of the building information management system, such as fire protection, intrusion prevention, monitoring, access control, and central air conditioning, is carried out. This article systematically explained the application of PID in the building information management system. And use comparative analysis method, observation method and other research forms to conduct experimental research on the intelligent OPC building information management system based on computer Internet of things. The research shows that compared with the traditional building management system, the intelligent OPC-based building management system researched in this paper can transmit information faster and have higher accuracy.

Impact of demand response on occupants’ thermal comfort in a leisure center

Many assets that are normally installed during an energy-efficient building retrofit can also be used to provide flexible services to the electricity grid. By turning off or turning down some mechanical systems during peak times, it is possible for a building to reduce its load on the electricity network. A field demand response event was simulated at a leisure center in Ireland to evaluate the suitability of the site to participate in the Irish demand response market, to assess how much flexibility it can provide, how much the indoor conditions changed during the test, and to examine whether these remained within satisfactory limits. A survey was conducted to determine whether the occupants perceived any changes to their thermal comfort. The simulation was achieved by identifying non-critical mechanical equipment and turning them off for 2 h. A processing station for demand response and energy monitoring delivered the demand response signal to the site’s building management system. The results show that this site had a flexibility potential of 45 kW, which is considered too low to participate in the demand response market, as Irish aggregators favor sites that can offer over 250-kW flexibility. However, the indoor thermal conditions remained within reasonable ranges and the occupants did not notice the impact of the demand response event. This shows that theoretically, if smaller sites were allowed to sell their flexibility to the electricity market, such leisure centers could participate in demand response services without impacting occupants’ comfort.

Numerical Model for Prediction of Indoor COVID-19 Infection Risk Based on Sensor Data

In addition to infection with SARS-CoV-2 via direct droplet transmission or contact with contaminated surfaces, infection via aerosol transport is a predominant pathway in indoor environments. The developed numerical model evaluates the risk of a COVID-19 infection in a particular room based on measurements of temperature, humidity, CO2 and particle concentration, the number of people and instances of speech, coughs and sneezing using a dedicated low-cost sensor system. The model can dynamically provide the predicted risk of infection to the building management system or people in the room. The effect of temperature, humidity and ventilation intensity on the infection risk is shown. Coughing and especially sneezing greatly increase the probability of infection in the room; therefore distinguishing these events is crucial for the applied measurement system.

Motion and Sash Height (MASH) alarms for efficient fume hood use

Ventilation, including fume hoods, consumes 40–70% of the total energy used by modern laboratories. Energy-conscious fume hood usage—for example, closing the sash when a hood is unused—can significantly reduce energy expenditures due to ventilation. Prior approaches to promote such behaviors among lab users have primarily relied on passive feedback methods. In this work, we developed a low-cost fume hood monitoring device with active feedback to alert lab users when a fume hood is left open and unused. Using data collected by the building management system, we observed a 75.6% decrease in the average sash height after installation of these “Motion and Sash Height” (MASH) alarms, which would result in a reduction roughly equal to 43% of the annual carbon emissions of a typical American vehicle, for each fume hood. The MASH alarm presented here reduced energy costs by approximately $1,159 per year, per hood, at MIT.

Potential and limitation of AI system in building services and control management system

In the construction industry, traditional method for analysing human comfort is time consuming. Thus, artificial intelligence (AI) has been slowly being applied in the software stimulation and building management system to solve the typical comfort analysis method. The potential and limitation of the AI system in the building service are presented through PRISMA review. The AI system enables the building service system to analysis in real-time, optimising energy efficiency, enhance occupant’s satisfaction, risk mitigation, cost minimisation and work efficiency increased. However, the AI system application in the building service still faces some challenges such as lack of big data and the varying parameter of data input in the software system, expensive initial cost and required expertise.

Energy use of buildings in relation to occupancy rates

This paper presents basic data of the energy demand for district heating and plug loads logged by a building management system of an energy-efficient academic building located in Lund, Sweden. The data refers to the years 2019 and 2020 when occupancy varied significantly due to the Corona pandemic. The data shows that the building energy demand adapts poorly to fluctuating occupancy rates. With a possible increase of smart working in the future, building codes should account for more fluctuating occupancy rates in the modelling of the energy demand of buildings.

Data-driven occupant-centric rules of automated shade adjustments: Luxembourg case study

This study presents key findings of observed datasets in a nearly zero-energy office building for over 66 working days from June to mid-September in 2019, Luxembourg. Measurements of indoor and outdoor environmental parameters as well as user-shade override adjustments were extracted from the KNX-based building management system (BMS) in 47 office rooms located in three typical floor levels. Relative frequency and “rate of change” of blind use were analysed in terms of window orientation, occupancy level, and the time of the day. Logistic regression and data mining techniques were used to identify potentially useful and understandable occupant behaviour patterns and reveal the main triggers behind blind adjustments. The well-designed automation system together with the inner glare protection formed the base of very low user-shade interactions. A mean of 0.184 manual blind adjustments per day per office. Eight regression sub-models were developed and all were incapable of predicting user-shade lowering and raising events. Alternatively, two user profiles were mined based on 20 rules gained from clustering analysis: user (ß) was representing the passive user, and user (μ) the medium user. Overall, we conclude that the automated shading system in this building is satisfactory, user-friendly, and a robust control system.