One-dimensional lazy quantum walks and occupancy rate

In this paper, we discuss the properties of lazy quantum walks. Our analysis shows that the lazy quantum walks have O（tn） order of the n-th moment of the corresponding probability distribution, which is the same as that for normal quantum walks. The lazy quantum walk with a discrete Fourier transform （DFT） coin operator has a similar probability distribution concentrated interval to that of the normal Hadamard quantum walk. Most importantly, we introduce the concepts of occupancy number and occupancy rate to measure the extent to which the walk has a （relatively） high probability at every position in its range. We conclude that the lazy quantum walks have a higher occupancy rate than other walks such as normal quantum walks, classical walks, and lazy classical walks.

Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates

Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,respectively.This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time.In order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office buildings.The first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’activities.After finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted separately.The second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather variables.In this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather variables.The proposed approaches are verified by the real data from the University of Glasgow as a case study.The simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity demand.In addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction method.In addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without requiring additional building parameters.

Matching of Residential and Commercial Space in Shrinking Cities from the Perspective of Supply and Demand:A Case Study of Yichun City,China

Urban shrinkage is becoming an increasingly common phenomenon in China.The research focus has been the identification,origin,and pattern of shrinking cities.Nevertheless,attention has also been paid to the problems associated with urban shrinkage.The present study examines one typical shrinking city in China,specifically the Yichun District in Yichun City.To explore the matching relationship between residential and commercial spaces,this study analyzes supply and demand data,electricity consumption data and multi-source points of interest(POI)of residents.The results showed that the occupancy rate is not reduced in the context of urban shrinkage,and that the supply level of various commercial facilities is not in decline.Apart from leisure and entertainment facilities,the supply levels of catering,shopping and supporting facilities for life were noted to have improved.In reference to urban shrinkage,the matching relationship between residential and commercial space in the 5-min,10-min,and 15-min living circles mainly shifted to a highlevel equilibrium.The matching relationships between residential space and different types of commercial spaces change in both direction and magnitude.From the perspective of supply and demand,the spatial and temporal changes in the relationship relate to multiple factors,such as the level of economic development,the buiding age pattern,public transportation accessibility,aging,and residents’willingness to move.This study provides relevant data for managing urban shrinkage.It also helps improve the relationships between residential and commercial spaces and works to optimize the layout and structure of functional urban spaces.

Comparative Analysis between Community and Occupants’ Rating Systems for Sustainable Urban Communities (SUC)

Overpopulation globally is an addressed issue impacting human lives, marine lives, and the surrounding ecosystem;it is adding pressure on the available resources that should be optimized to suit the needs. Yet with improper management of resources and monitoring of daily activities, the environment will be further negatively impacted. With overpopulation higher urbanization rates are noticed with the demand of seeking better health facilities, better education, better jobs and better well-being;this progression is driving more demand into the infrastructure sector to be able to accommodate the growth rates. Hence, the need to having sustainable communities aiming at optimizing the resources used, working towards more feasible, environmentally friendly and cost-effective communities with a better occupant’s experience is in action. Sustainable development goals (SDG) are vital goals developed by the United Nations Development Program (UNDP) in 2015 to address and guide through 17 interconnected global goals serving the previously mentioned trend. Out of the 17 goals, Sustainable Cities and Communities (goal #11) and Good Health and Well-Being (goal #3) are the focus of this paper directed towards holding a comparative analysis between the community scale commonly known and mostly used rating system Leadership of Energy and Environmental Design (LEED-Cities and Communities) (USA) versus similar rating systems like Tarsheed-Communities (Egypt) and Estidama-Pearl (UAE) rating systems meeting sustainable development goal #11. Conjointly, another complimenting comparative review of the occupant’s health and wellbeing rating systems, such as Fitwel (USA) and Well (USA) are studied under sustainable development goal #3;however, they are focused on a building scale assessment. Living Community Challenge (LCC, USA) rating system linking community rating system with health & wellbeing credits was first issued in 2006, yet is it not cost effective neither easy to apply acting as a primary step while being affordable, accessible, and easy to implement. The objective of this paper is to highlight the pros and gaps under both categories of studies of community rating system and occupants’ health & wellbeing rating systems based on scientific content and commercial acceptance and do-ability. This comparison is done via comparing credits and sections within each rating system type;this will support in addressing the focal points needed for an integrated rating system between both categories that will serve in meeting SDG Sustainable Cities and Communities (goal #11) and Good Health and Well-Being (goal #3).

Review on occupancy detection and prediction in building simulation

Energy simulation results for buildings have significantly deviated from actual consumption because of the uncertainty and randomness of occupant behavior.Such differences are mainly caused by the inaccurate estimation of occupancy in buildings.Therefore,the error between reality and prediction could be largely reduced by improving the accuracy level of occupancy prediction.Although various studies on occupancy have been conducted,there are still many differences in the approaches to detection,prediction,and validation.Reports published within this domain are reviewed in this article to discover the advantages and limitations of previous studies,and gaps in the research are identified for future investigation.Six methods of monitoring and their combinations are analyzed to provide effective guidance in choosing and applying a method.The advantages of deterministic schedules,stochastic schedules,and machine-learning methods for occupancy prediction are summarized and discussed to improve prediction accuracy in future work.Moreover,three applications of occupancy models—improving building simulation software,facilitating building operation control,and managing building energy use—are examined.This review provides theoretical guidance for building design and makes contributions to building energy conservation and thermal comfort through the implementation of intelligent control strategies based on occupancy monitoring and prediction.

Building energy efficiency and COVID-19 infection risk:Lessons from office room management

To prevent COVID-19 outbreaks,many indoor environments are increasing the volume of fresh air and running air conditioning systems at maximum power.However,it is essential to consider the comfort of indoor occupants and energy consumption simultaneously when controlling the spread of infection.In this study,we simulated the energy consumption of a three-storey office building for postgraduate students and teachers at a university in Beijing.Based on an improved Wells-Riley model,we established an infection risk-energy consumption model considering non-pharmaceutical interventions and human comfort.The infection risk and building energy efficiency under different room occupancy rates on weekdays and at weekends,during different seasons were then evaluated.Energy consumption,based on the real hourly room occupancy rate during weekdays was 43%–55%lower than energy consumption when dynamic room occupancy rate was not considered.If all people wear masks indoors,the total energy consumption could be reduced by 32%–45%and the proportion of energy used for ventilation for epidemic prevention and control could be reduced by 22%–36%during all seasons.When only graduate students wear masks in rooms with a high occupancy,total energy consumption can be reduced by 15%–25%.After optimization,compared with the strict epidemic prevention and control strategy(the effective reproductive number Rt=1 in all rooms),energy consumption during weekdays(weekends)in winter,summer and transition seasons,can be reduced by 45%(74%),43%(69%),and 55%(78%),respectively.The results of this study provide a scientific basis for policies on epidemic prevention and control,carbon emission peak and neutrality,and Healthy China 2030.