Operation Of Building Systems Research Articles

Novel occupancy detection method based on convolutional neural network model using PIR sensor and smart meter data

Occupancy information, especially the number of occupants, can effectively guide the operation of building systems. Capturing real-time and accurate occupancy information is critical. Passive infrared (PIR) sensors and smart meters have been deployed extensively. To improve the occupancy detection accuracy, this study proposes an innovative occupant number detection approach involving a temporal sequential analysis of occupancy-related data using data fusion from PIR sensors and smart meters with a convolutional neural network (CNN) model. A university office was investigated as a case study. PIR sensors and smart meters were adopted to collect data. And CNN model was established with both various data types and historical data as multi-dimensional model inputs. The model outperformed than traditional ANN models with an improved accuracy by 26.8%. The proposed occupancy detection model was applied to optimize the control strategy of an outdoor air system, which helped to provide comfortable built environment and realize energy savings.

Innovative hybrid prediction method integrating wavelet threshold decomposition and entropy-based model selection strategy for building energy consumption prediction

Building energy prediction plays a pivotal role in the building energy management system, and an efficient prediction method is crucial to ensure the effective operation of building systems. Existing research enhances prediction accuracy by combining decomposition algorithms with prediction methods to separate non-stationary information from the original sequence. However, the decomposition process and the subsequent prediction of multiple decomposed subsequences result in significant time overhead, making it unsuitable for scenarios with high temporal requirements, such as short-term energy consumption forecasting. Additionally, a single prediction model struggles to adapt to the diverse features of decomposed subsequences, creating prediction accuracy and efficiency bottlenecks. Addressing these challenges, this study proposes a hybrid prediction method that combines wavelet threshold decomposition (WTD) with an entropy-based model selection strategy. The method initially utilizes the efficient WTD algorithm to decompose the original energy consumption data into three fixed subsequences. Subsequently, the permutation entropy for each subsequence is computed, and based on the comprehensive performance of four prediction models (ANN, LSTM, CNN-LSTM, and TCN) on sequences with different entropy values, the optimal prediction model is selected. Finally, the prediction results of the subsequences are aggregated to obtain the ultimate prediction outcome. Experimental results demonstrate that the proposed method significantly outperforms baseline methods in both prediction speed and accuracy across five real building energy consumption datasets with distinct operational modes.

Elastic buildings: Calibrated district-scale simulation of occupant-flexible campus operation for hybrid work optimization

Before 2020, the way occupants utilized the built environment had been changing slowly towards scenarios in which occupants have more choice and flexibility in where and how they work. The global COVID-19 pandemic accelerated this phenomenon rapidly through lockdowns and hybrid work arrangements. Many occupants and employers are considering keeping some of these flexibility-based strategies due to their benefits and cost impacts.This paper explores how demand-driven control strategies in the built environment might support the transition to increased workplace flexibility by simulating various scenarios related to the operational technologies and policies of a real-world campus using a district-scale City Energy Analyst (CEA) model that is calibrated with measured energy demand data and occupancy profiles extracted from WiFi data. These scenarios demonstrate the energy impact of ramping building operations up and down more rapidly and effectively to the flex-based work strategies that may solidify. The scenarios show a 5–15% decrease in space cooling demand due to occupant absenteeism of 25–75% if centralized building system operation is in place, but as high as 17–63% if occupancy-driven building controls are implemented. The paper discusses technologies and strategies that are important in this paradigm shift of operations.

Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality

Buildings are considered the enormous source of untapped energy efficiency potential in the global carbon neutrality. It is necessary to ensure that buildings are energy-efficient using operational pattern analytics and diagnostics. Therefore, this study proposes a novel symbolic hierarchical clustering method (named HOS-SAX) to evaluate the building system operation, efficiency, and energy usage patterns. The proposed HOS-SAX method is intended to enhance the existing methods that focus only on the energy usage characteristics and thus offer limited insights on the building system and operational efficiency. The proposed method consists of: (1) Holistic Operational Signature (HOS) and (2) HOS-based symbolic aggregate approximation (SAX) analyses. A HOS analysis is conducted to derive the representative operational signatures for building operation and efficiency using system-, building-, and weather-level data. Then, SAX is performed with the operational signatures derived from the HOS to cluster the building operation patterns. In a case study for a district heating substation serving residential buildings, the HOS-SAX cluster analysis showed 15 sections in the cluster map that visualize the: (1) energy usage, (2) design efficiency, and (3) control efficiency. The cluster map revealed that the sections that operated inefficiently account for approximately 71% of the entire operation period. Moreover, it is expected that the supply temperature of 0.87 °C can be reduced in the most inefficient sections.

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and air-conditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality and comfort. As a result, machine learning-based models and methodologies are progressively being used to forecast occupancy behaviour and routines in buildings, which may subsequently be used to aid in the design and operation of building systems. The present work reviews recent studies employing machine learning methods to predict occupancy behaviour and patterns, with a special focus on its related applications and benefits to building systems, improving energy efficiency, indoor air quality and thermal comfort. The review provides insight into the workflow of a machine learning-based occupancy prediction model, including data collection, prediction, and validation. An organised evaluation of the applicability or suitability of the different data collection methods, machine learning algorithms, and validation methods was carried out.

Influence of a Better Prediction of Thermal Satisfaction for the Implementation of an HVAC-Based Demand Response Strategy

Building system operation faces the challenge of reducing energy use and implementing a demand response, which can be defined as a temporary modification in energy loads affecting dynamic energy price and reliability information. The heating, ventilation, and air-conditioning (HVAC) system in buildings provides an opportunity for implementing demand response strategies due to the thermal inertia in building zones. However, an HVAC-based demand response is not a prevalent strategy in actual facility management due to the lack of understanding among building operators of their facilities and occupants. Herein, we focus on developing a better understanding of the occupant side by obtaining a reliable prediction of occupants’ thermal satisfaction. We evaluate the prediction performance of a probabilistic model provided in our previous paper using a case study with a subset of the ASHRAE Global Thermal Comfort Database II. The influence of a better prediction of thermal satisfaction on the implementation of the HVAC-based demand response strategy is further discussed. The conventional method overestimates productivity deterioration due to changes in the thermal environment, making it challenging to implement an HVAC-based demand response strategy aggressively. A robust prediction model using a probabilistic approach can solve this problem, allowing building operators to adopt an aggressive stance for implementing a demand response. The results of this study offer fresh insight into the impact of a probabilistic model in the prediction of thermal satisfaction for establishing an HVAC-based demand response strategy.

Current Trajectories and New Challenges for Visual Comfort Assessment in Building Design and Operation: A Critical Review

Visual comfort can affect building occupants’ behaviour, productivity and health. It is highly dependent on the occupant and how they perform a task indoors. In that regard, an occupant centred approach is more suitable for evaluating the lighting perception of the indoor environment. Nevertheless, the process of rating and estimating the visual comfort makes a limited distinction between physiological differences (e.g., ageing eye, light sensitivity), field of view, and personal preferences, which have been proven to influence the occupants’ lighting needs to complete their tasks. Such features were not considered while establishing the visually comfortable conditions; perhaps due to the challenge of coupling the assumptions made during building design to the performance indicators monitored during building operation. This work focuses on reviewing literature findings on how the common design approach deviates from real building performance, particularly failing to prevent visual disturbances that can trigger the inefficient operation of building systems. Additionally, it is highlighted that redesigned visual comfort assessment methods and metrics are required to bridge the gap between the lighting environment ratings computed and surveyed. One possibility is to consider such physiological features that induce lighting experiences. Finally, it was deducted that it is important to target the occupants’ eye response to calibrate limit thresholds, propose occupant profiling, and that it is convenient to continuously monitor the occupants’ perception of indoor lighting conditions.

Using WiFi connection counts and camera-based occupancy counts to estimate and predict building occupancy

Accurate occupancy information can help in optimizing the operation of building systems. To obtain this information, previous studies suggested using WiFi connection counts due to their strong correlation with occupancy counts. However, validating this correlation and investigating its variation have remained limited due to challenges regarding the collection of ground-truth data. Therefore, many studies suggested a single (fixed) value as the conversion factor of WiFi connection counts to actual occupancy counts based on short-term ground-truth data. This study addressed this gap by proposing a method for investigating the correlation between WiFi connection counts and actual building occupancy over a longer duration using continuous ground-truth data collected from camera-based occupancy counters. The proposed method focused on (i) identifying the influential features on this correlation and their effectiveness, as well as (ii) developing models to estimate real-time occupancy counts and to forecast day-ahead occupancy counts. To validate the proposed method, it was applied in a library building in Montréal, Canada with data collected between January and March 2020. Results showed time-related features including Hour of the day and Day of the week, as well as occupancy level influenced the correlation between Wifi and occupancy counts. Furthermore, the proposed models successfully estimated real-time occupancy counts with an average accuracy (R2) of 0.96 for weekdays and 0.98 for weekends, while day-ahead occupancy forecasting models had an average accuracy (R2) of 0.92 for weekdays and 0.82 for weekends. These findings provided a proof-of-concept for the proposed methodology, demonstrated the potential of using WiFi connection count for estimating/forecasting occupancy counts, and most importantly highlighted the important considerations that need to be addressed when using WiFi connection counts as a proxy for occupancy to optimize building operation.

Development of a testing and evaluation protocol for occupancy sensing technologies in building HVAC controls: A case study of representative people counting sensors

Occupancy-based control (OBC) in smart buildings provides numerous potentials to improve building energy efficiency. To achieve effective OBC, the occupancy status of a space or a building needs to be well understood in terms of whether the space is occupied and how many people are in the space. Well-designed and selected people counting technologies are cornerstones for OBC. However, the study and application of such technologies in OBC are relatively new. There are few mature people counting sensors available on the market. In addition, there is a lack of standardized guidance to comprehensively test, evaluate and compare the performance of people counting sensors, which further limits the selection and application of them in OBC. Hence, an innovative testing protocol was developed to fill in the gap. This paper introduces the design of the protocol with eight diversities and discusses case studies that evaluated four representative types of people counting sensors following the proposed protocol. It is found that the protocol can effectively guide a comprehensive evaluation of the selected sensors and lead to informative findings on the sensor performance. The test results can provide insights and suggestions not only for sensor developers to improve the sensor hardware and software design but also for heating, ventilation, and air-conditioning (HVAC) system designers and building managers to select the proper people counting sensors for OBC design. The test results on the sensor accuracy can also support additional evaluations of integrated OBC and building system operation. It is anticipated that the developed methodology can offer guidance on the test and evaluation of other occupancy sensing technologies.

HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study

Building sensing technologies have evolved rapidly in the last two decades in aid of monitoring building environment and energy system performance. A series of occupancy sensing systems were developed to track the occupant behavior in the indoor space. Occupancy-based building system control is defined as a control method that adjusts the building system operation schedules and setpoints based on the measured occupant behavior and has been identified as a smart building control strategy that can improve building energy efficiency as well as occupant comfort. Some studies demonstrated energy-saving potential and comfort-maintaining capability from occupancy-based control. This study adopted a first-of-its-kind side-by-side experimental approach to quantify the performance of the occupancy-based control in commercial buildings. Three state-of-the-art occupancy sensing technologies were integrated into the real-time Heating, Ventilation, and Air-Conditioning (HVAC) system control in this study. Their detection accuracy and its effectiveness on energy-saving and thermal comfort were analyzed. It was found that the occupancy-based control can maintain good thermal comfort and perceived indoor air quality with a satisfaction ratio greater than 80%. Although the daily energy-saving varied with occupancy sensor accuracy and outdoor environment conditions, the weekly averaged energy saving was between 17 and 24%.

Current state and future challenges in building management: Practitioner interviews and a literature review

Building management tools, such as building automation systems (BASs) and computerized maintenance management systems (CMMSs), are intended to support the efficient operation of building systems while maintaining occupants' comfort. However, observing current building management practices in commercial and institutional buildings demonstrates several challenges that compromise the core objectives of building management tools. To this end, this paper aims to provide recommendations to commercial and institutional building management stakeholders for improving current building management practices in North America. The recommendations are based on interviews with thirty building management professionals, examples and evidence from relevant literature, and the authors' involvement in resolving building management-related issues. The paper first documents current building management practices including professionals' backgrounds and qualifications, approaches and tools used, coordination, and building occupants’ involvement. Then, barriers to improving current building management practices are outlined including knowledge, technology, and organizational challenges. Finally, short- and long-term goals to improve current building management practices are outlined. This paper is intended to serve as a roadmap for improving building energy management practices for both researchers and industry.

A framework to identify key occupancy indicators for optimizing building operation using WiFi connection count data

Adapting building systems' operation to occupancy variations can provide significant energy savings, but this is typically constrained by the unavailability of occupancy information. Although several technologies have been introduced to integrate real-time occupancy information in heating, ventilation and air-conditioning (HVAC) systems operations (e.g., CO2 monitoring), the logistical and cost issues associated with deploying these technologies remained a key issue. Previous studies proposed using WiFi counts as a proxy for occupancy in building operations and showed a strong correlation between occupancy and WiFi counts in several building types. However, the difficulty of integrating real-time WiFi traffic data in building automation systems hinders wide-scale deployment of this approach. To this end, this study proposes a framework for extracting occupancy indicators from WiFi traffic data. The proposed framework utilizes several machine learning algorithms and statistical analysis methods to predict patterns of building occupancy as well as to identify peak occupancy time and earliest/latest arrival and departure times. To validate the proposed framework, it was implemented in a case-study using data collected from an academic building in Montreal, Canada between January and March 2020. Results revealed that the proposed models could successfully predict weekly building occupancy patterns, with an average accuracy (R2D) of 0.98 for weekdays and 0.81 for weekends. Furthermore, the analysis identified peak occupancy timing, as well as arrival and departure times variations between different zones. These findings provided a proof-of-concept for the proposed framework and demonstrated its potential to provide actionable information to modify the sequences of operation of building systems based on buildings’ unique occupancy patterns.

Impacts of diversity in commercial building occupancy profiles on district energy demand and supply

Urban building energy models (UBEM) have the potential to become integral planning tools for district energy systems due to the dynamic, interactive and complex nature of temporal building energy demand patterns. Although the demand patterns are related to the occupancy profiles of buildings supplied by district energy systems, occupant behavior in current UBEM approaches does not usually consider diversity in occupancy profiles among buildings of the same use-type.In this work, a novel method to create context-specific, data-driven commercial building occupancy profiles was used to generate diverse and non-diverse urban building occupant presence models (UBOP). Diverse UBOP randomly assigned occupancy profiles to buildings. Non-diverse UBOP assigned the data-driven mean or median profile to all buildings. ASHRAE standard profiles and occupant densities served as a baseline for comparison.The impact of diverse vs. non-diverse UBOP was assessed by comparing UBEM simulations for district energy efficiency benchmarking, renewable energy integration potential, and district energy system design, using a case study in Singapore. The results demonstrate that, because of the relationship between occupant presence and building systems operation, occupancy profiles are highly sensitive parameters for district energy demand predictions. For the case study, the energy demand estimation is significantly influenced by the shape of occupancy profiles. In particular, the choice of UBOP influences the cooling demand to the degree that district cooling system design decisions might be impacted. Therefore, it is advisable to use diverse UBOP and to run probabilistic UBEM simulations for district energy system design.

Optimizing energy consumption and occupants comfort in open-plan offices using local control based on occupancy dynamic data

Optimal management of buildings' energy-consuming systems is of great importance for minimizing building energy consumption while satisfying the occupants. Since the operation of building systems are highly dependent on the presence of occupants, considering the dynamic occupancy information has become crucial to reflect the occupancy dynamism within offices and the random patterns in occupant behavior. Thus, occupancy-centered control strategies are required in order to enhance the energy management of buildings. On the other hand, the need for localized and customizable comfort controls is increasing in office buildings to improve the occupants’ satisfaction, and consequently their productivity. To this end, a framework aiming at developing optimal occupancy-centered local control strategies is proposed in this paper. A new simulation-based multi-objective optimization model of the energy consumption in offices is developed to exploit occupancy-related data and evaluate possible local control strategies to select the best ones. A set of real occupancy data collected over a period of one year is fed to the integrated simulation-based optimization model for investigating the energy-saving potentials. Comparing the results shows that a considerable improvement in the indoor comfort condition can be achieved through the application of the proposed framework. We conclude that optimal control strategies not only provide demand-driven control solutions but also optimize building energy performance. The integrated model enables dynamic building energy management according to dynamic occupancy patterns. It avoids over-conditioning that is the result of the application of common practices, which control building systems based on the peak occupancy.

Research on the Decision-making Model of Value Information Selection in the Context of Big Data

Data filtering is the first step in the utilization and development of big data, that is, obtaining value data from massive data resources, building system operation database, and realizing the mining and sorting of value data. However, restricted by the infinite big data environment and limited data acquisition ability, almost no technology can obtain all the value data. In this paper, a series of indicators are proposed to evaluate and enhance the data filtering algorithm.

Observations of energy consumption and IEQ in a ‘Tiny House’

ABSTRACT The ‘Tiny House’ is a class of residential construction with floor areas less than 400 ft2 (37.16 m2). The components of the Tiny House (TH) pose certain unique challenges in terms of maintaining appropriate indoor environmental quality and resultant energy consumption that have not been previously researched. Hence, the objectives of this research were to measure the impact of physical characteristics, such as building envelope and volume of a TH on the resultant indoor environmental quality and energy consumption. The intent is to identify the parameters responsible to ensure comfortable and healthy conditions in the TH. This paper presents an analysis of some of the first reported measurements of indoor environmental conditions and energy use in the TH in a cold climate. To accomplish this task, two THs built in Bozeman Montana were instrumented and monitored. Occupant logs were conducted to collect data for occupant related activities. The results obtained from this research contributed to the development of strategies for the construction and operation of building systems that are tailored to the unique configuration of the TH. In doing so, the study reassessed specifications that are currently available in building codes that may not be applicable to THs.

Smart Eco Home: Towards Sustainable Community Within Relevant Strategic Integrated Intelligence of Architecture.

While various countries all over the world suffer from a real energy lack, Energy becomes essential for economic evolution in any country for two main causes; first that its mandatory for domestic development, and it‘s a rich source of foreign currencies. To avoid energy scarcity in Egypt; the community has to change its habits in different approaches and aspects to overcome any future lack in energy and at the same time to offer a sustainable community. Nowadays environmental technologies become an important discipline to take in consideration, as the sustainable smart approach will be a significant arena for economic competitions between building (especially Homes), which of them is more environmentally friend and fulfill human intelligent needs. The main aim of this research is to consolidate Smart approach with ecological Discipline aiming to enhance ecological sustainability through integrated intelligent of architecture, the research gathered information about architecture chronological approaches through a logical review. Further, the research educes from this approaches an elaborated criterion to follow, and a real range for designers to apply in smart sustainable homes This productive scientific intellectual offers specific procedures for innovating and designing a new compatibility viable architecture. this architecture countenance merging between two major poles, which are; building smartness and environmental control,Turning residential buildings to Smart Ecological (SE) Home through; the application of intelligent and /or knowledge to automate the operation of building systems, the fulfillment of the ecological potentials concerning the environmental issue, preserving the natural environment, and minimize the economics used by home before and after construction. The study closes with the synthesis of main preliminary guide lines of the SE concept. Not only through hypothetical stage based on elaborated scientific search but also via practical investigation based on further analysis of the two major branches (smart-ecology). The purpose of the study is to create a new approach that integrates smart and ecological features with each other creating an elaborated approach that fulfill occupants needs, and at the same time provide comfort for occupants inside the homes. Also it offers energy conservation and a natural atmosphere desired for building consumers without harming the contiguous environment.

An algorithmic module toolkit to support quality management for building performance

Data from building automation systems is so far used for the operation of building systems and components only. The following work shows how this data can be used to enhance the building’s performance by strategically detecting potential sources for building optimization. With this method, faults and optimization potentials of the building operation can be detected; thus, the quality gap regarding efficiency and comfort aspects between design and operation can then be reduced. Furthermore, the intelligent use of data enables the realization of economic savings to support facility management with regards to increasingly complex HVAC systems. Effective quality management – rapid, transparent and cost effective – is carried out with the aid of digital methods, which are already state of the art in other industries.

Optimal sensor placement strategy for office buildings using clustering algorithms

Sensor networks embedded in the built environment provide critical information for intelligent building energy management. Data from these sensors enable optimizing energy efficiency and indoor environmental quality without compromising occupant comfort. Thus sensors help achieve efficient operation of building systems at reduced operating costs. Ideally, towards these goals all possible measurement points in buildings should be measured and verified. However, this would inevitably incur tremendous cost and time. Alternatively, an approach to identify the optimal measurement points that can provide a holistic picture of the indoor environment is desirable. This paper proposes a novel data driven approach based on field measurements in an office building to derive the optimal (number and locations of) measuring points. Clustering algorithms, information loss approach and Pareto principle were used to derive the optimal sensor placement strategy. The findings of this study can have important implications for researchers and practitioners.

Spoken dialogue BIM systems – an application of big data in construction

PurposeWith the rapid development in the internet technologies, the applications of big data in construction have seen considerable attention. Currently, there are many input/output modes of capturing construction knowledge related to all construction stages. On the other hand, building information modelling (BIM) systems have been developed to help in storing various structured data of buildings. However, these systems cannot fully capture the knowledge and unstructured data used in the operation of building systems in a usable format that uses the intelligent capabilities of BIM systems. Therefore, this research aims to adopt the concept of big data and develop a spoken dialogue BIM system to capture buildings operation knowledge, particularly for building maintenance and refurbishment.Design/methodology/approachThe proposed system integrates cloud-based spoken dialogue system and case-based reasoning BIM system.FindingsThe system acts as an interactive expert agent that seeks answers from the user for questions specific to building maintenance problems and helps searching for solutions from previously stored knowledge cases. The practices of monitoring and maintaining buildings performance can be more efficient by the retrieval of relevant solutions from the captured knowledge to new problems when maintaining buildings components. The developed system enables easier capture and search for solutions to new problems with a more comprehensive retrieval of information.Originality/valueCapturing multi-modes data into BIM systems using the cloud-based spoken dialogue systems will help construction teams use the high volume of data generated over building lifecycle and search for the most suitable solutions for maintenance problems. This new area of research also contributes to the current BIM systems by advancing their capabilities to instantly capture and retrieve knowledge of operations instead of only information.