A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example

We present a method to integrate environmental time series into stock assessment models and to test the significance of correlations between population processes and the environmental time series. Parameters that relate the environmental time series to population processes are included in the stock assessment model, and likelihood ratio tests are used to determine if the parameters improve the fit to the data significantly. Two approaches are considered to integrate the environmental relationship. In the environmental model, the population dynamics process (e.g. recruitment) is proportional to the environmental variable, whereas in the environmental model with process error it is proportional to the environmental variable, but the model allows an additional temporal variation (process error) constrained by a log-normal distribution. The methods are tested by using simulation analysis and compared to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. In the traditional method, the estimates of recruitment were provided by a model that allowed the recruitment only to have a temporal variation constrained by a log-normal distribution. We illustrate the methods by applying them to test the statistical significance of the correlation between sea-surface temperature (SST) and recruitment to the snapper (Pagrus auratus) stock in the Hauraki Gulf–Bay of Plenty, New Zealand. Simulation analyses indicated that the integrated approach with additional process error is superior to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. The results suggest that, for the snapper stock, recruitment is positively correlated with SST at the time of spawning.

Effects of the user model on simulation-based learning of dialogue strategies

Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model. © 2005 IEEE.

Interactions between building integrated photovoltaics and microclimate in urban environments

BIPV (building integrated photovoltaics) has progressed in the past years and become an element to be considered in city planning. BIPV has significant influence on microclimate in urban environments and the performance of BIPV is also affected by urban climate. The thermal model and electrical performance model of ventilated BIPV are combined to predict PV temperature and PV power output in Tianjin, China. Then, by using dynamic building energy model, the building cooling load for installing BIPV is calculated. A multi-layer model AUSSSM of urban canopy layer is used to assess the effect of BIPV on the Urban Heat Island (UHI). The simulation results show that in comparison with the conventional roof, the total building cooling load with ventilation PV roof may be decreased by 10%. The UHI effect after using BIPV relies on the surface absorptivity of original building. In this case, the daily total PV electricity output in urban areas may be reduced by 13% compared with the suburban areas due to UHI and solar radiation attenuation because of urban air pollution. The calculation results reveal that it is necessary to pay attention to and further analyze interactions between BIPV and microdimate in urban environments to decrease urban pollution, improve BIPV performance and reduce cooling load. Copyright © 2006 by ASME.

Effect of building integrated photovoltaics on microclimate of urban canopy layer

Building integrated photovoltaics (BIPV) has potential of becoming the mainstream of renewable energy in the urban environment. BIPV has significant influence on the thermal performance of building envelope and changes radiation energy balance by adding or replacing conventional building elements in urban areas. PTEBU model was developed to evaluate the effect of photovoltaic (PV) system on the microclimate of urban canopy layer. PTEBU model consists of four sub-models: PV thermal model, PV electrical performance model, building energy consumption model, and urban canyon energy budget model. PTEBU model is forced with temperature, wind speed, and solar radiation above the roof level and incorporates detailed data of PV system and urban canyon in Tianjin, China. The simulation results show that PV roof and PV façade with ventilated air gap significantly change the building surface temperature and sensible heat flux density, but the air temperature of urban canyon with PV module varies little compared with the urban canyon of no PV. The PV module also changes the magnitude and pattern of diurnal variation of the storage heat flux and the net radiation for the urban canyon with PV increase slightly. The increase in the PV conversion efficiency not only improves the PV power output, but also reduces the urban canyon air temperature. © 2006.

A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method. © 2012 Elsevier B.V. All rights reserved.

Energy use of buildings at urban scale: A case study of london school buildings

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop building stock models. This research proposes an engineering-based bottom-up stock model in a probabilistic manner to address these issues. School buildings are used for illustrating the application of this probabilistic method. Two sampling-based global sensitivity methods are used to identify key factors affecting building energy performance. The sensitivity analysis methods can also create statistical regression models for inverse analysis, which are used to estimate input information for building stock energy models. The effects of different energy saving measures are analysed by changing these building stock input distributions.