Extracting Influential Factors for Building Energy Consumption via Data Mining Approaches

To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption. To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental data. The conditions that had a significant effect on energy consumption were analyzed to be: (i) conditions related to the building design—wall, floor, and window area ratio, the window-to-wall ratio (WWR), the window-to-floor area ratio (WFR), and the azimuth, and (ii) the internal conditions of the building—the illuminance, occupancy density, plug load, and frequency of room utilization. The room conditions derived through analysis were considered in each sample, and the final influential building energy consumption factors were derived by using them in a decision tree as being the WFR, window area ratio, floor area ratio, wall area ratio, and frequency of use. Furthermore, four room types were classified by combining the room conditions obtained from the key factor classifications derived in this study.

The Ventilation Efficiency of Urban Built Intensity and Ventilation Path Identification: A Case Study of Wuhan

Urban ventilation is being hampered by rough surfaces in dense urban areas, and the microclimate and air quality of the urban built environment are not ideal. Identifying urban ventilation paths is helpful to save energy, reduce emissions, and improve the urban ecological environment. Wuhan is the capital city of Hubei, and it has a high urban built intensity and hot summers. Taking Wuhan city, with a size of 35 km ×50 km, as an example, the built environment was divided into grids of 100 m × 100 m and included the building density, floor area ratio, and average building height. The ventilation mechanism of the urban built intensity index has previously been explained. The decrease in building density is not the sole factor causing an increase in wind speed; the enclosure and width of the ventilation path and the height of the front building are also influential. Twelve urban built units were selected for CFD numerical simulation. The ventilation efficiency of each grid was evaluated by calculating the wind speed ratio, maximum wind speed, average wind speed, and area ratio of strong wind. The relationship between the urban built intensity index and ventilation efficiency index was established using the factor analysis method and the Pearson correlation coefficient; building density and average building height are the most critical indexes of ventilation potential. In addition, the layout of the building also has an important impact on ventilation. A suitable built environment is that in which the building density is less than 30%, the average building height is greater than 15 m, and the floor area ratio is greater than 1.5. The urban built intensity map was weighted to identify urban ventilation paths. The paper provides a quantitative reference for scientific planning and design of the urban spatial form to improve ventilation.

Towards a Sustainable Energy Planning Strategy: The Utilisation of Floor Area Ratio for Residential Community Planning and Design in China

Floor Area Ratio, commonly known as FAR, is a primary planning metric in urbanism. It is commonly known as the ratio of accumulated built floor areas against the size of a site/plot. In recent years, China's rapid urban development has resulted in a substantial increase in FAR, particularly in residential areas. A notable shift from low-rise row housing typology to a more common high-rise residential unit is certainly a key factor behind the increase of energy use in many Chinese cities. This factor highlights the need for a sustainable energy planning strategy at the community level. As a novelty, this study reflects on the FAR changes in the context of China, and evaluates the impacts on energy use at the community scale. This study tests four FAR models of 1.0, 2.5, 3.0, and 4.0 and provides a computational modelling analysis, with a comparison analysis. FAR models of 1.0 and 4.0 are typical models of low-rise and mid-to-high rise, respectively. Findings suggest FAR 4.0 as the least efficient for energy planning while FAR 1.0 is the least economically viable model. The results support policy development on FAR monitory, and paradigm shifts for future urban development models, particularly for the small to medium-scale residential compounds in cities.

Determining Land Values from Residential Rents

The value of land is determined by the locations’ attractiveness and the degree of direct land use regulation. When regulations are binding, e.g., when a restriction on the maximum floor area ratio exists, the land price can be directly expressed as a function of the maximum floor area ratio and local amenities. We show theoretically and empirically how this approach can be used to determine land values from rental prices of residential structures built upon that land. From our empirical results, we derive two main sources for a monocentric structure of land prices. First, the location attractiveness of centrally located dwellings makes land prices more expensive. Second, as the maximum floor area ratio is high in central areas, the regulation works as a multiplier for land prices and inflates prices accordingly. Our model gives insights into the determinants of urban land prices and provides a useful approach for land appraisal in regions where land transactions are scarce.

Impact Analysis of Urban Morphology on Residential District Heat Energy Demand and Microclimate Based on Field Measurement Data

In this work, we focus on investigating the relationship between urban morphology parameters and residential building space heating energy performance, comparing microclimate conditions of existing residential blocks with central heating supply. Firstly, a dataset composed of district morphological parameters that measured heat energy consumption was established. Then, effects of morphological indicators including cover ratio, average building height, and floor area ratio on building space heating energy efficiency were assessed specifically. Analysis results show that a larger floor area ratio induced a reduction in heating energy consumption density, the observed effect is notable at an initial increase of floor area ratio. Thirdly, the case study shows that the heating load of residential districts with a high built density is more sensitive to solar radiation. To further assess how and to what extent urban forms alter microclimates, on-site measurement investigated detailed changes in the thermal environment of selected residential districts before and after the operational stage of central heating supply. Analysis results demonstrate that heat energy delivered by a central heating supply could dampen the variations of local outdoor air temperatures, more notable for residential districts with a higher floor area ratio during the night period. Findings from this work would be useful for urban planners considering energy-efficient design practices.

Analysis of the Effects of Floor Area Ratio Change in Urban Street Canyons on Microclimate and Particulate Matter

Air pollution, such as particulate matter (PM), and extreme weather are causing increasingly complex problems and socioeconomic damage in urban environments year-round. This study predicts extreme weather and air pollution changes that occur in urban street canyons as the basic data necessary for research on energy conservation. Changes in PM and microclimate elements based on the change in floor area ratio are analyzed. In addition, the effects of microclimate elements on the distribution of PM factors are examined. Based on the change in floor area ratio, high-concentration PM was negatively correlated with PM2.5, PM10, O3, NO2, NOx, Ta, Tmrt, and Tsurface. Extreme heat was observed to be negatively correlated with Tmrt and Tsurface, and extreme cold negatively correlated with PM2.5, PM10, NO2, and NOx. The higher the floor area ratio, the higher the wind speed (WS), indicating a positive correlation between the two factors. Ta, Tmrt, and Tsurface were observed to be negatively correlated with PM2.5, PM10, NO2, and NOx. WS showed negative correlations with PM2.5, PM10, NO2, and NOx. The results of this study can be used as basic data for the derivation of evaluation indices and to determine prediction and response strategies with respect to a combination of extreme weather and air pollution to ensure a suitable and sustainable quality of life. This study helps predict energy loads according to urban street canyon structures and examines whether trees and green walls are effective in reducing extreme weather and air pollution and saving energy.