A Fine-grained Building Height Control Method for Heritage Areas from the Perspective of Visual Integrity

This study proposes a refined methodology for controlling building heights in heritage areas.In order to protect the visual integrity of the heritage area,buildings should not obstruct the view from important site points and viewpoints to the periphery.By calculating the building height thresholds that buildings should not obscure the view from each viewpoint,the results of which are weighted and superimposed,and the values are extracted to each building unit as a refined building height control guideline.This study takes the Zhoukoudian area as a case study,applies the refined building height control criterion to the Zhoukoudian Site,and relies on this refined criterion to assess the visual integrity of the Zhoukoudian area,so as to realize the scientific planning and monitoring of the Zhoukoudian area.The refined building height control method can be applied to building height planning and visual landscape protection in large heritage areas.

Reinforcement learning building control approach harnessing imitation learning

Reinforcement learning(RL)has shown significant success in sequential decision making in fields like autonomous vehicles,robotics,marketing and gaming industries.This success has attracted the attention to the RL control approach for building energy systems which are becoming complicated due to the need to optimize for multiple,potentially conflicting,goals like occupant comfort,energy use and grid interactivity.However,for real world applications,RL has several drawbacks like requiring large training data and time,and unstable control behavior during the early exploration process making it infeasible for an application directly to building control tasks.To address these issues,an imitation learning approach is utilized herein where the RL agents starts with a policy transferred from accepted rule based policies and heuristic policies.This approach is successful in reducing the training time,preventing the unstable early exploration behavior and improving upon an accepted rule-based policy-all of these make RL a more practical control approach for real world applications in the domain of building controls.

Timetabling optimization of classrooms and self-study rooms in university teaching buildings based on the building controls virtual test bed platform considering energy efficiency

The energy consumption of a teaching building can be effectively reduced by timetable optimization.However,in most studies that explore methods to reduce building energy consumption by course timetable optimization,self-study activities are not considered.In this study,an MATLAB-EnergyPlus joint simulation model was constructed based on the Building Controls Virtual Test Bed platform to reduce building energy consumption by optimizing the course schedule and opening strategy of self-study rooms in a holistic way.The following results were obtained by taking a university in Xi’an as an example:(1)The energy saving percentages obtained by timetabling optimization during the heating season examination week,heating season non-examination week,cooling season examination week,and cooling season non-examination week are 35%,29.4%,13.4%,and 13.4%,respectively.(2)Regarding the temporal arrangement,most courses are scheduled in the morning during the cooling season and afternoon during the heating season.Regarding the spatial arrangement,most courses are arranged in the central section of the middle floors of the building.(3)During the heating season,the additional building energy consumption incurred by the opening of self-study rooms decreases when duty heating temperature increases.

The reinforcement learning method for occupant behavior in building control:A review

Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy con-sumption and building performance.Modeling frameworks are usually built to accomplish a certain task,but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment.For complex and dynamic environments,the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable.It is expected that they will make a substantial contribution to reducing global energy consumption.Among these control techniques,the reinforcement learning(RL)method seems distinctive and applicable.The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control.Fruitful algorithms complement each other and guarantee the quality of the opti-mization.However,the examination of occupant behavior based on reinforcement learning methodologies is not well established.The way that occupant interacts with the RL agent is still unclear.This study briefly reviews the empirical applications using reinforcement learning,how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.

A Practical Approach to Representation of Real-time Building Control Applications in Simulation

Computer based automation and control systems are becoming increasingly important in smart sustainable buildings,often referred to as automated buildings(ABs),in order to automatically control,optimize and supervise a wide range of building performance applications over a network while minimizing energy consumption and associated green house gas emission.This technology generally refers to building automation and control systems(BACS)architecture.Instead of costly and time-consuming experiments,this paper focuses on development and design of a distributed dynamic simulation environment with the capability to represent BACS architecture in simulation by run-time coupling two or more different software tools over a network.This involves using distributed dynamic simulations as means to analyze the performance and enhance networked real-time control systems in ABs and improve the functions of real BACS technology.The application and capability of this new dynamic simulation environment are demonstrated by an experimental design,in this paper.

Efficient Methodology for Design of Industrial Blast Resistant Electrical Substations and Control Buildings

This paper describes economical strategies to design blast resistant electrical substations and control buildings that are commonly used at industrial plants.Limited literature addressed design aspects for this class of buildings.Furthermore,little guidelines are available in practice to regulate this type of steel construction.The first part of the paper overviews the architectural and structural layouts of electrical buildings.Blast resistance requirements for occupied control buildings are also discussed.Simplified multiple degrees of freedom(MDOF)dynamic model is also illustrated that can be utilized for analysis of the blast resistant buildings.The economical aspects and cost savings resulting in using mobile blast resistant buildings are discussed.The article also highlights the engineering challenges that are encountered in design of mobile electrical facilities.The transportation procedure and design requirements are briefly described.Guidelines are proposed to calculate the center of mass of the building combined with interior equipment.The proposed design concept for electrical and control buildings is cost effective and can be implemented in industry to reduce projects cost.

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning(DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers(RBCs),but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process.Transfer Learning(TL)is a potential solution to address this limitation.However,existing TL applications in building control have been mostly tested among buildings with similar features,not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems.This paper assesses the performance of an online heterogeneous TL strategy,comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python.The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage(TES).The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems,different building envelope features,occupancy schedule and boundary conditions(e.g.,weather and price signal).The TL approach incorporates model slicing,imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings.Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers.Moreover,online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC.Conversely,online TL achieves worse performance compared to offline DRL in either transductive or inductive settings.However,offline Deep Reinforcement Learning(DRL)agents should be trained at least for 15 episodes to reach the same level of performance as the online TL.Therefore,the proposed online TL methodology is effective,completely model-free and it can be directly implemented in real buildings with satisfying performance.

In search of optimal building behavior models for model predictive control in the context of flexibility

Model predictive control(MPC)is an advanced control technique.It has been deployed to harness the energy flexibility of a building.MPC requires a dynamic model of the building to achieve such an objective.However,developing a suitable predictive model is the main challenge in MPC implementation forflexibility activation.This studyfocuses on the application of key performance indicators(KPls)to evaluate the suitability of MPC models via feature selection.To this end,multiple models were developed for two houses.A feature selection method was developed to select an appropriate feature space to train the models.These predictive models were then quantified based on one-step ahead prediction error(OSPE),a standard KPI used in multiple studies,and a less-often KPl:multi-step ahead prediction error(MSPE).An MPC workflow was designed where different models can serve as the predictive model.Findings showed that MSPE better demonstrates the performance of predictive models used for flexibility activation.Results revealed that up to 57% of the flexibility potential and 48% of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model.

After the Rot： Improving the Durability of Building Envelopes in Domestic Housing New Zealand

The issue of weathertightness of the external building envelope in domestic scaled timber frames continues to be an issue in New Zealand, some ten years after the results of a major cladding survey into the durability and weathertightness of the exterior cladding envelope carried out by the writer in the year 2000. The fallout from leaking buildings has estimated to have cost the country billions of dollars in lost production and expensive repair. The social impact on those caught up in the leaking home issue has been considerable; with often heart rending tales of stress and financial hardship. This paper will explore the initiatives taken by the building industry and the government since the issue became a major public concern. It will examine the influences, both positive and negative, that resulting legislation and changed building practices, brought in as a result of this crisis, have had on the sustainability and affordability of the domestic dwelling in New Zealand.

Research on Dynamic Model's Building of Active Magnetic Suspension Systems

An experimental method is introduced in this paper to build the dynamics of AMSS (the active magnetic suspension system), which doesn’t depend on system’s physical parameters. The rotor can be reliably suspended under the unit feedback control system designed with the primary dynamic model obtained. Online identification in frequency domain is processed to give the precise model. Comparisons show that the experimental method is much closer to the precise model than the theoretic method based on magnetic circuit law. So this experimental method is a good choice to build the primary dynamic model of AMSS.

Assessment of Minimizing the Environmental Functions Conflict in Buildings

Some building components are responsible for achieving more than one environmental function, these functions are usually of different requirements that can never be done by the same actions, and they are usually connected to changeable internal and external environment characteristics that vary among them. Minimizing the conflict of achieving the different environmental functions is an important challenge for all designers. Achieving a continuous thermal and optical comfort in an internal building space using the same window is an example of this challenge, as they have different requirements that may be sometimes contrary. It should be notable that there are a lot of recent technologies that may be used to find solutions for such a conflict. The Environmental Assessment Methods of Buildings appeared to set the principles of the optimum relation between buildings and their environment, they also could be used to encourage designers to reach the best environmental relations, and award them by main or additional assessment points. The research paper proposes to use the Environmental Assessment Methods of Buildings to assess the building ability of minimizing its environmental functions achievement conflict. This proposal depends on determining the inconsistency assessment items that depend on common building components to be achieved, and then determining the time periods that these items are achieved together within, to indicate the time periods without conflicting. Thus, the paper aims to raise the building environmental value in the assessment when the designer succeeds to minimize the expected conflict of the building environmental functions.

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.

管道化熔盐炉加热系统CB仿真模型的研究

针对管道化溶出过程中熔盐炉加热系统难于控制、运行效率低、系统能耗高的现象,依据加热系统的运行机理,利用Control-Build工程控制软件建立了熔盐炉加热系统的仿真模型。得到了不同运行工况下热工参数的仿真值,与实际测得数据进行比较,误差在5%以内。应用该模型对系统进行仿真优化研究,结果表明:在保证矿浆温度的情况下,控制矿浆流量为283.5～297.5 m3/h,有利于降低加热矿浆所需熔盐的进口温度,达到节能目的;计算得到管道化溶出系统的清疤周期为56 d。

Call for an Institution with the Authority and Mandate with Regard to Technical Disputes and Fundamental Consideration for the System

Based on this evidence and many other examples, this paper advocates a drastic overhaul of the system, in which a distinction is made between simple, standard projects, less simple solutions that can be assessed with performance requirements, and high-value projects which are handled in accordance with the science of probabilistics. Next to or in addition to the Council of State, there has to be a technical body appointed under statute with non-departmental public body status, which can issue binding rulings in technical disputes, with very short procedural delays.

Building Blocks for Tobacco Control

The tobacco epidemic is a global challenge demanding concerted global and national action. Recognizing that globalization is accelerating the epidemic’s spread and perceiving the limits of national action to contain a public health problem with transnational dimensions, Member States of the World Health Organization negotiated and adopted a unique public health treaty for tobacco control. Today, the WHO FCTC contains the blueprint for coordinated global action to address one of the most significant risks to health.

Auditing and design evaluation of building automation and control systems based on eu.bac system audit-Danish case study

Improving the energy performance of buildings will prove vital for countries worldwide to reduce their energy consumption and emissions.A key player in reaching this goal is building automation and control,as having well-designed and operated building automation and control systems(BACS)provide large capabilities in optimizing the energy performance of different systems.In this regard,building owners and planners must be able to assess and evaluate the current status of their BACS and identify potential improvements.While there has been a large block of work done in Denmark along with regulations aiming to audit the overall building performance and individual systems characteristics,very little has been done in the field of auditing the building automation and control system and evaluating its structure and operation patterns.This lack of systematic BACS auditing and evaluation in Danish buildings is addressed in this work with the first implementation and evaluation of the eu.bac System methodology in a university office building.The building was found to comply with the lowest automation and control class E.Two BACS retrofit packages were proposed and evaluated,and energy savings up to 28.5%are reported.The preliminary assessment results reported demonstrate the potential of building automation and control retrofit measures in a combined holistic improvement package alongside building envelope upgrade.In addition,the impact of the eu.bac System improvements and labeling on the building’s classification based on the recent Danish building regulation BR18 is evaluated.The study discusses the feasibility of eu.bac System tool implementation in Danish buildings and suggests improvements.It also correlates and compares the eu.bac System audit to the upcoming European SRI instrument.In light of the huge efforts to digitalize the Danish energy sector,ensuring proper design and operation of BACS is of great importance.Thus,a systematic and methodical BACS auditing and evaluation methodology will be a crucial part of buildings’initial and retro-commissioning platforms.

Review of onsite temperature and solar forecasting models to enable better building design and operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most buildings are in environments that are quite different than those at the airport miles away.Tree cover,adjacent buildings,and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature,humidity,solar irradiance,and wind that are large enough to influence design and operation decisions.In order to overcome this challenge,there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales.This paper reviews and complies knowledge on common weather data resources,data processing methodologies and forecasting techniques of weather information.Commonly used statistical,machine learning and physical-based models are discussed and presented as two major categories:deterministic forecasting and probabilistic forecasting.Finally,evaluation metrics for forecasting errors are listed and discussed.

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.