Occupancy is one of the main factors affecting building energy consumption. The occupancy data, which refer to the occupancy number in this paper, has been widely used in the building simulation ...field. However, due to the stochastic nature of occupant behavior, it is hard to predict and measure how many people stay in a given building. The rapid development of mobile Internet technology provides an efficient and convenient option for occupancy detection. This paper proposes a concept of typical occupancy data (TOD), which are extracted from real-time occupancy data collected by mobile devices. K-means algorithm is employed to generate the TOD data through cluster analysis. An energy performance model of an office building is used as a case study to demonstrate the effectiveness of the TOD data.
Occupancy data is a critical input parameter for building energy simulation since it has a big impact on the precision and accuracy of building energy model performance. However, current approaches ...to get such data through the conventional occupancy detection technology require either implementation of a large-scale sensor network and/or sophisticated and time-consuming computational algorithms, which to some degree limits the application of the real-time occupancy data for building energy simulation. In the era of the mobile internet, the massive people position data, which is generated by smartphone users and stored on cloud servers, offers a potential to solve this important problem. Such mobile data source is precisely monitored, real-time updated, and accessible with affordable time and labor cost upon customer's agreements in some regions, and therefore could be one of the alternatives to traditional occupancy detection methods.
This paper presents an investigation of whether and how the mobile-internet positioning data can benefit building energy simulation. This paper first summarizes the pros and cons of several mainstream occupancy detection methods. Then, the principle of the proposed mobile-internet-based occupancy detection method is introduced. The methodology of using such occupancy data for building energy simulation is developed. An energy performance model of a complex building in Shanghai with a whole building simulation software EnergyPlus is used as a pilot case study to demonstrate the effectiveness of the proposed methodology. A calibration is performed using the building automation system data and the mobile-internet-based occupancy data. The simulation results show that mobile-internet-based occupancy data can help improve the building model prediction accuracy.
•An overview of current occupancy detection methods is presented.•A mobile-internet-based occupancy detection method is proposed.•A case study building energy model is built to investigate the application of the proposed method in calibrated simulation.•This occupancy detection method can be further used in occupant-centered HVAC control researches.
Obtaining reliable and detailed energy consumption information about building service (BS) systems is an essential prerequisite for identifying energy-saving potential and improving energy efficiency ...of a building. Therefore, in recent years, energy sub-metering systems have been widely implemented in public buildings in China. A majority of electrical systems and equipment can be directly metered. However, in actual sub-metering systems, the terminal units of heating, ventilation and air conditioning (HVAC) systems, such as fan coils, air handling units and so on, are often mixed with the lighting-plug circuit. This mismatch between theoretical sub-metering systems and actual electricity supply circuits constitutes a lot of challenges in BS system management and control optimization. This study proposed an indirect method to disaggregate the energy consumption of HVAC terminal units from mixed sub-metering data based on the CART algorithm. This method was demonstrated in two buildings in Shanghai. The case study results show that the weighted mean absolute percentage errors (WMAPE) are within 5% and 15% during working hours in the cooling and heating seasons, respectively.
Occupancy, which refers to the occupant count in this paper, is one of the main factors affecting the energy consumption of commercial buildings. It is important for both building managers and energy ...simulation engineers to understand how an entire building’s energy consumption varies with different occupancy levels in the process of building automation systems or in assessments of building performance with benchmarking lines. Because commercial buildings usually have large scales, complex layouts and a large number of people, it is a challenge to simulate the relationships between an entire building’s energy consumption and occupancy. This study proposes a fast method for calculating the influence of occupancy on the energy consumption of commercial buildings with different building layouts and existing occupancies. Other occupant behaviors, such as the opening of windows and adjustment of shading devices, are comprehensively reflected in two basic building parameters: the balance point temperature and the total heat transmission coefficient of the building. This new method can be easily used to analyze how building energy varies with occupancy without a physical building’s energy model. An office building in Shanghai is taken as a case study to validate the proposed method. The results show that the coefficient of determination R2 between the calculated value and actual value is 0.86, 0.8 and 0.71 for lighting, cooling and heating energy, respectively, which is suitable in engineering applications.
Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency, the heating, ventilation, and air conditioning (HVAC) design process is still very ...time-consuming. In this paper, we propose a conceptual framework for automating the entire design process to replace current human-based HVAC design procedures. This framework includes the following automated processes: building information modeling (BIM) simplification, building energy modeling (BEM) generation & load calculation, HVAC system topology generation & equipment sizing, and system diagram generation. In this study, we analyze the importance of each process and possible ways to implement them using software. Then, we use a case study to test the automated design procedure and illustrate the feasibility of the new automated design approach. The purpose of this study is to simplify the steps in the traditional rule-based HVAC system design process by introducing artificial intelligence (AI) technology based on the traditional computer-aided design (CAD) process. Experimental results show that the automatic processes are feasible, compared with the traditional design process can effectively shorten the design time from 23.37 working hours to nearly 1 hour, and improve the efficiency.
•The state-of-the-art research of energy demand flexibility of demand responsive building is reviewed.•The energy demand flexibility measures range from renewable energy to HVAC, energy storage, ...building thermal mass, appliances, and customer behavior are introduced.•Systematic methodology framework to evaluate energy demand flexibility in buildings are developed.
This paper classifies and discusses the energy flexibility improvement strategies for demand responsive control in grid-interactive buildings based on a comprehensive study of the literature. Both supply and demand sides are considered. The flexibility measures range from renewable energy such as photovoltaic cells (PV) and wind to heating, ventilation, and air conditioning (HVAC) systems, energy storage, building thermal mass, appliances, and occupant behaviors. Currently, owing to the highly developed smart appliances and sensing communication techniques, DR is considered as an essential measure for improving energy flexibility in buildings without much additional investment. With the help of advanced demand response (DR) control strategies and measures, buildings can become more flexible in terms of power demand from the power grid. In this way, buildings achieve a better ability to balance differences in energy supply and demand. Furthermore, a synergistic approach with various measures is advisable, e.g., the use of energy storage technologies with PV and passive DR methods. This paper summarizes the measures for improving the flexibility of commercial and residential buildings, and develops a systematic methodology framework to evaluate energy demand flexibility in buildings.
•A power consumption model of IT equipment in data centers is built.•It predicts how IT equipment design and operation change a data center’s power consumption.•It only needs data from the design of ...data center and manufacturer specification.•It can be used in dynamic and real-time energy system simulation.
Due to the rapid rise of power consumption of data centers in recent years, much work has been done to develop energy-efficient design, controls and diagnosis of their cooling systems, while the energy system simulation is used as an effective tool. However, existing models of information technology (IT) equipment of data centers cannot well represent the effects of IT equipment design and operation status on the data center cooling demand, and this hinders the development of the energy saving cooling technologies of data centers. To address this issue, this paper introduces a power consumption model of IT equipment in data centers with coefficients and modeling script provided for immediate use in data center energy system simulation. This energy model can be used to simulate energy performance of typical IT equipment in data centers under real-time dynamic operation conditions conveniently and effectively without the need of data other than the specifications of a data center design and IT equipment manuals. Its use with a commonly used building simulation program is demonstrated with a building model of a typical large office in a subtropical area. The results show that the model can represent the change of power consumption of data centers with different IT equipment designs and operation appropriately.
In recent years, scholars have completed many studies on the fault diagnosis of air-conditioning systems based on Building Energy Management System (BEMS) data, but these studies seldom cover the ...leakage of water pipes and the damage of insulation layer, that are common but undetected by BEMS. To fill in the gaps, an automatic diagnosis algorithm based on infrared thermal images is proposed here to detect fault occurs on insulated heating pipes. This method can automatically diagnosis of pipeline leakage and insulation damage, so as to prevent pipeline corrosion and heat loss. The algorithm includes two sections: an image segmentation processor and a fault diagnosis module. The fault diagnosis module can detect three categories of faults: insulation damaged, insulation fall-off, and pipeline leakage. Experimental study demonstrates that the overall accuracy of the algorithm is 97.59%, while filed studies in commercial buildings exhibits an accuracy of 92.74%. These data prove the algorithm's feasibility in practice and the method can be applied with an infrared camera installed on an inspection robot or at a fixed location in a machine room. Although images of hot water pipes are used as inputs of the research, this method is also applicable to cold water pipes by modifying relevant parameters.
Display omitted
•Image processing methods are employed to extract the operating characteristic of pipes in the infrared thermal image.•The method proposed in this paper has the advantages of single data type, non-intrusive monitoring and convenient deployment. It can be conducted with inspection robots and realize the smart management of HVAC.
The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that ...data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data.
Along with the improvement of social productivity and living standard, residential buildings generate a growing portion of carbon emissions, especially during the operation stage. However, energy use ...behaviors are usually ignored in carbon emission calculation. This study focuses on calculating carbon emissions during the operation stage for residential buildings based on the characteristics of energy use behaviors in different regions. Firstly, we investigated energy use behaviors in dwellings across three cities in China: Xi’an, Shanghai and Fuzhou. Then, we established calibrated carbon emission models and optimization models with different green building measures for residential buildings. The results of this research reveal a significant disparity between the energy usage habits of residents in different climate regions. The carbon emissions of residential electricity bills in Xi’an, Shanghai and Fuzhou are 13.6 kgCO
2
/(m
2
·a) (excluding central heating), 29.3 kgCO
2
/(m
2
·a) and 17.2 kgCO
2
/(m
2
·a), respectively. Equipment carbon emissions account for 32.2%–64.1% of the total. In comparison to the model based on internal standard setting, the accuracy of the models using actual internal has improved by 25.9%–37.4%. The three-star green building methods have the highest carbon reduction rate among different star buildings, the emission reduction rates are around 30%. This study’s findings are useful for carbon emission calculation and green building design of residential buildings in the future.
