Challenges and opportunities of machine learning control in building operations

Machine learning control(MLC)is a highly flexible and adaptable method that enables the design,modeling,tuning,and maintenance of building controllers to be more accurate,automated,flexible,and adaptable.The research topic of MLC in building energy systems is developing rapidly,but to our knowledge,no review has been published that specifically and systematically focuses on MLC for building energy systems.This paper provides a systematic review of MLC in building energy systems.We review technical papers in two major categories of applications of machine learning in building control:(1)building system and component modeling for control,and(2)control process learning.We identify MLC topics that have been well-studied and those that need further research in the field of building operation control.We also identify the gaps between the present and future application of MLC and predict future trends and opportunities.

Analysis and Evaluation of Thermal-cooling Loads of Office Buildings Using Carrier Software in Iran

The importance and necessity of energy saving in the world have been dis­cussed for many years,but achieving a logical and transparent solution is still one of the main challenges and problems of the world’s economy.The rapid growth of energy consumption in the last two decades has caused the security of the domestic energy supply of buildings to face serious prob­lems.In this research,first by entering parameters such as the type of mate­rials,doors and windows,and the type of soil on the floor connected to the ground,etc.in the heat and cold load calculation software(HAP Carrier)as the design calculations and then in the second step entering the specifica­tions inferred from the Iran’s national building code as a reference for ener­gy saving calculations,calculations are performed and compared as the first criterion,and finally these two outputs are compared.The actual energy consumption and determination of the building energy consumption index are determined as another criterion,as well as the degree of deviation from the actual consumption.The results showed that the theoretical method and the thermal and refrigeration load calculations of the Zanjan Gas Company building have 6%difference in cooling load but the heating load is about 34%different,which means for cooling loads,the theoretical model can be used with high accuracy but for heating loads,the national building code needs fundamental changes.

个人征信体系建设的国际比较与借鉴

健全的个人征信体系是解决我国社会主义市场经济体制建设中信用缺失问题的重要举措。许多国家和地区都建立起了成熟的个人征信体系,形成了以欧洲大陆国家为代表的公共模式和以美英两国为代表的民营模式。将对这两种模式进行多方面的对比,并在分析美国经验的基础上结合我国国情提出建立健全我国个人征信体系的建议。

Construction management through bot: Taiwan high speed rall case

One of the key elements in real estate management is streamlining the construction process. Thus, the facilities can be built on a faster, cheaper, and higher quality base. Consequently, it will enhance the owner’s competitiveness. Due to the high cost and lengthy duration of mega-construction projects in recent years, Build-Operate-Transfer (BOT) contracts are getting popular in delivering constructed projects in the public sector. With BOT, the public owners are able to focus on the effectiveness of fair resource allocation as well as bring the efficiency of private enterprise into governmental operations. This paper uses Taiwan High Speed Rail project to exemplify the BOT method in executing the constructed projects in the chain of real estate management processes. The paper explains the reasons for building HSR and adopting BOT approach. The detail of the HSR project and the feasibility analysis of the project will be presented in this paper. The feasibility analysis comprises the comparisons of different transportation means, the financial analysis, and other benefits from HSR. Finally, conclusions will be drawn.

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

Buildings have a significant impact on global sustainability.During the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance.Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications.Recent advances in information technologies and data science have enabled convenient access,storage,and analysis of massive on-site measurements,bringing about a new big-data-driven research paradigm.This paper presents a critical review of data-driven methods,particularly those methods based on larger datasets,for building energy modeling and their practical applications for improving building performances.This paper is organized based on the four essential phases of big-data-driven modeling,i.e.,data preprocessing,model development,knowledge post-processing,and practical applications throughout the building lifecycle.Typical data analysis and application methods have been summarized and compared at each stage,based upon which in-depth discussions and future research directions have been presented.This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling.Furthermore,considering the ever-increasing development of smart buildings and IoT-driven smart cities,the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

A systematic review on COVID-19 related research in HVAC system and indoor environment

The on-going COVID-19 pandemic has wrecked havoc in our society,with short and long-term consequences to people’s lives and livelihoods-over 651 million COVID-19 cases have been confirmed with the number of deaths exceeding 6.66 million.As people stay indoors most of the time,how to operate the Heating,Ventilation and Air-Conditioning(HVAC)systems as well as building facilities to reduce airborne infections have become hot research topics.This paper presents a systematic review on COVID-19 related research in HVAC systems and the indoor environment.Firstly,it reviews the research on the improvement of ventilation,filtration,heating and air-conditioning systems since the onset of COVID-19.Secondly,various indoor environment improvement measures to minimize airborne spread,such as building envelope design,physical barriers and vent position arrangement,and the possible impact of COVID-19 on building energy consumption are examined.Thirdly,it provides comparisons on the building operation guidelines for preventing the spread of COVID-19 virus from different countries.Finally,recommendations for future studies are provided.

ROBOD, room-level occupancy and building operation dataset

The availability of the building’s operation data and occupancy information has been crucial to support the evaluation of existing models and development of new data-driven approaches.This paper describes a comprehensive dataset consisting of indoor environmental conditions,Wi-Fi connected devices,energy consumption of end uses(i.e.,HVAC,lighting,plug loads and fans),HVAC operations,and outdoor weather conditions collected through various heterogeneous sensors together with the ground truth occupant presence and count information for five rooms located in a university environment.The five rooms include two different-sized lecture rooms,an office space for administrative staff,an office space for researchers,and a library space accessible to all students.A total of 181 days of data was collected from all five rooms at a sampling resolution of 5 minutes.This dataset can be used for benchmarking and supporting data-driven approaches in the field of occupancy prediction and occupant behaviour modelling,building simulation and control,energy forecasting and various building analytics.

A simulation-based method to investigate occupant-centric controls

Occupant-centric control (OCC) strategies rely on different algorithms to learn and predict occupants’ patterns and preferences, then utilize these predictions to optimize building operations. However, testing different OCC algorithms or fine-tuning their configurations in real buildings can be a lengthy process. To this end, we present a framework for testing OCCs in a simulation environment prior to field implementation. The proposed workflow entails using synthetic occupant behaviour models and simulating OCC strategies to learn their preferences. The goal is to enable quick comparison of different OCC configurations under various scenarios by modifying occupant behaviour assumptions, as well as climate and design parameters. For proof-of-concept, the proposed method was applied in a case-study to simulate OCCs for lighting and heating/cooling setpoint adjustments in a single office under various occupant types, as well as OCC settings and design configurations. Results demonstrated the benefits of the proposed framework and its potential for providing a more holistic evaluation of OCCs under different scenarios. Using the proposed framework, building designers and operators can identify potential issues with OCCs and fine-tune their settings prior to field implementation.