The aim of this article is to propose and examine a quantitative method of determining the degree of compatibility between municipal services. Provision of services and facilities maintenance are usually two biggest expenditures of local governments. Traditionally, facilities host only one service, whereas the challenge and opportunity lies in combining various, compatible services and offering them together under one roof. Such a combination decreases municipal expenditure and has a strong positive impact on the general service quality. For this purpose, we take advantage of the City-block distance formula to calculate the degree of compatibility between municipal services. The method is examined and discussed on a sample of 30 real municipal services. This allows us to find possible combinations of strongly compatible services that should be offered together in Multi-Service Facilities and, at the same time, avoid an unwanted combination of services that are incompatibl
Background In recent years, the monitoring of occupant presence patterns has become an imperative for building energy optimization. Very often, there is a significant discrepancy between the building energy performance predicted at the design stage and the actual performance rendered during the building operation. This stems from the difference in user occupancy. In spite of this, user interaction and feedback are rarely taken into account and evidence of the impact of occupant presence patterns on energy consumption is still scarce. Thus, the purpose of this study is to apply crowd-sensing techniques to understand how energy is consumed and how appropriate performance indicators should be defined to provide inputs for building operations regarding more efficient use of resources. Methods Monitoring strategies were implemented in an office lab with controlled variables to collect quantitative data on occupancy patterns, ambient factors and energy consumption. In addition, crowd-sensing techniques were applied to model user activity in different ambient conditions over time and to contrast their occupancy with energy consumption patterns in combination with new inquiry tools to identify how occupants perceive their comfort level. In addition, a set of energy efficiency indicators was used to compare energy performance over different periods. Results It was discovered that there is a strong relation between user occupancy patterns and energy consumption. However, more than 50% of energy was consumed when no user activity was registered. Energy performance indicators revealed that measuring energy efficiency in terms of kWh per surface area encourages a less efficient use of space and, therefore, including a coefficient of person hours is advisable. It was also discovered that users do not fully rely on feedback mechanisms and they prefer to take action to adapt the ambient conditions rather than simply expressing their opinion. Analysis of energy usage during the Covid-19 lock down revealed substantial use of energy contrary to what was expected. This was because home computers were used as terminals only, while the actual tasks were performed on the lab computers, using remote desktop connections, which were turned on 24/7. In addition, energy consumed by each employee at his/her home should be taken into account. Moreover, a set of practical recommendations was formulated.
BackgroundIn recent years monitoring of user behavior became an imperative for building energy optimization. Very often there is a significant discrepancy between predicted building energy performance at the design stage and the actual one rendered during the building operation. This stems from the difference in users’ behavior. In spite of that, users’ interaction and feedback is rarely taken into account and evidence of the impact of occupants’ behavior on energy consumption is still scarce. Thus, the purpose of this study is to apply crowd-sensing techniques to understand how energy is consumed, define appropriate performance indicators, and provide inputs for building operations on more efficient use of resources.MethodsMonitoring strategies were implemented in an office lab with controlled variables to collect quality data on occupancy patterns, ambient factors and energy consumption. In addition, crowd-sensing techniques were applied to model user behavior at different ambient conditions over time and to contrast this behavior with energy consumption patterns combined with new inquiry tools to identify how occupants perceive their comfort level. Also, a set of energy efficiency indicators was used to compare energy performance in different periods. ResultsIt was found out, that there is a strong relation between users’ behavior and energy consumption, however, more than 50% of energy was consumed when no users’ activity was registered. Energy performance indicators revealed that measuring energy efficiency in terms of kWh per surface area encourages less efficient use of space and therefore including coefficient of person hours is advised. It was also found out that users do not relay fully on feedback mechanism and they rather prefer to take an action to adapt the ambient conditions instead of simply expressing their opinion. Analysis of energy usage during the covid-19 lock down revealed substantial use of energy unlike it was expected. It was because home computers were used only as terminals, while the actual tasks were performed on the lab computers with remote desktop connection turned on 24/7. In addition, energy consumed by each employee at their home has to be taken into account. Moreover, a set of practical recommendations was formulated.
This article focuses on the issue of a sustainable space-use in public facilities and beneficial arrangement of services. Uncorrelated facility planning and service programming as well as environmental factors cause discrepancies between space demand and space supply leading to space overuse or underuse. To enhance the functional and economic efficiency of public facilities a conceptual framework, which is a planning and evaluation tool for decision support, is presented and discussed on examples. The framework consists of two decisive elements: space-Manuscript Click here to access/download;Manuscript;Manuscript.docx use analysis and service compatibility analysis. The first one aims to determine the degree of space utilization in multiple public buildings while the latter reports on how services are related to each other in terms of their compatibility. The article explains these concepts in details on examples.
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