An improved window opening behavior model involving the division of the dummy variable’s interval level:Case study of an office building in Xi’an during summer

Window opening behavior significantly impacts indoor air quality,thermal comfort,and energy consumption.A field measurement was carried out in three typical rooms(a standard office,a meeting room and a smoking office)within an office building.The window state and the physical environment were continuously recorded during the measured periods.Three typical window opening behaviors were found in the measured samples,namely,active,moderate,and passive.The common logistic regression coefficient indicated that solar radiation exhibited the greatest effect on window opening behavior in the smoking office and standard office.Typically,window opening behavior in the meeting room was the most strongly correlated with time of the day,mainly because of the meeting schedule for occupants in the meeting room.This study discussed the dividing principles involved in setting the dummy variable interval level(discretizing continuous variables and dividing them into different intervals),and proposed a method to determine the optimal interval level of each variable.The improved model led to the increase in the prediction accuracy rate of the window being opened by 2.0%and 3.3%according to the comparison with the original model based on dummy variables and the common model based on continuous variables,respectively.This study can provide a reference value for simulating energy consumption in office buildings in the future.

Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau

Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This study aimed at developing a method to estimate aboveground biomass(AGB)for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition(VIUPD)from the spectra to estimate AGB.First,we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type.Next,we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables.At last,we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately,all eight vegetation indices provided good estimates of AGB,with the best predictor of AGB varying among different ecosystems.When AGB of all the five ecosystems was estimated together using a simple linear model,VIUPD showed the lowest prediction error among the eight vegetation indices.The regression models containing dummy variables predicted AGB with higher accuracy than the simple models,which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index(VI).These results suggest that VIUPD is the best predictor of AGB among simple regression models.Moreover,both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models.Therefore,ground-based hyperspectral measurements are useful for estimating AGB,which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.