Urban Building Energy Modeling (UBEM) is critical for improving the resilience of cities to climate change, but most regions lack of data sets necessary for its development. A bottom-up approach is a viable method to ...
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Urban Building Energy Modeling (UBEM) is critical for improving the resilience of cities to climate change, but most regions lack of data sets necessary for its development. A bottom-up approach is a viable method to initiate comprehensive UBEM frameworks. However, this process is often challenged by incomplete data, which can significantly affect the reliability and resolution of simulation results. Traditional deterministic approaches commonly used in UBEM fail to capture the diversity of the building stock. Thus, probabilistic methods are increasingly used, which require a careful examination of the types and patterns of missing data. This paper fills a critical gap in the literature by presenting a probabilistic approach to datageneration for data-scarce environments to build high-resolution bottom-up urban-scale models while preserving building stock heterogeneity and statistical consistency. Our methodology includes advanced data imputation and generation techniques based on density estimations. This approach is illustrated with a case study in the Bah & ccedil;elievler neighborhood in Ankara, Turkey. We have developed four different UBEM versions with varying degrees of data granularity to demonstrate the effectiveness of our methods. The proposed models incorporate comprehensive data on construction and occupant-related parameters, enhancing the resolution of energy simulations for buildings. This research provides a robust framework for the development of UBEM in regions lacking comprehensive datasets, ultimately supporting informed policy making and improved urban energy management.
Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power syste...
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Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power system may change rapidly, yielding time-varying characteristics of the TTC. As a result, the traditional time-consuming transient stability constrained TTC model is unable to assess the online transmission margin. In this paper, we propose an online measurement-based TTC estimator using the nonparametric analytics. It consists of three major components: the probabilistic data generation, the composite feature selection, and the group Lasso regression-based training scheme. Specifically, we present a probabilistic data generation approach to take into account the uncertainties of the day-ahead generation scheduling and to reduce the number of redundant or infeasible data. Then, the composite feature selection is used to reduce the dimension of the generated data and identify the features which are highly correlated with TTC. The features are determined by the maximal information coefficients and nonparametric independence screening approach. Finally, these selected features are trained by the group Lasso regression to learn the correlation between the TTC and the online measurements. Once real-time measurements are available, the TTC can be assessed immediately through the learned correlation relationship. Extensive numerical results carried out on the modified New England 39-bus test system demonstrate the feasibility of the proposed TTC estimator for online applications.
Simulation of legal policies is an important decision-support tool in domains such as taxation. The primary goal of legal policy simulation is predicting how changes in the law affect measures of interest, e.g., reven...
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Simulation of legal policies is an important decision-support tool in domains such as taxation. The primary goal of legal policy simulation is predicting how changes in the law affect measures of interest, e.g., revenue. Legal policy simulation is currently implemented using a combination of spreadsheets and software code. Such a direct implementation poses a validation challenge. In particular, legal experts often lack the necessary software background to review complex spreadsheets and code. Consequently, these experts currently have no reliable means to check the correctness of simulations against the requirements envisaged by the law. A further challenge is that representative data for simulation may be unavailable, thus necessitating a data generator. A hard-coded generator is difficult to build and validate. We develop a framework for legal policy simulation that is aimed at addressing the challenges above. The framework uses models for specifying both legal policies and the probabilistic characteristics of the underlying population. We devise an automated algorithm for simulation datageneration. We evaluate our framework through a case study on Luxembourg's Tax Law.
Legal policy simulation is an important decision-support tool in domains such as taxation. The primary goal of legal policy simulation is predicting how changes in the law affect measures of interest, e.g., revenue. C...
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ISBN:
(纸本)9781467369084
Legal policy simulation is an important decision-support tool in domains such as taxation. The primary goal of legal policy simulation is predicting how changes in the law affect measures of interest, e.g., revenue. Currently, legal policies are simulated via a combination of spreadsheets and software code. This poses a validation challenge both due to complexity reasons and due to legal experts lacking the expertise to understand software code. A further challenge is that representative data for simulation may be unavailable, thus necessitating a data generator. We develop a framework for legal policy simulation that is aimed at addressing these challenges. The framework uses models for specifying both legal policies and the probabilistic characteristics of the underlying population. We devise an automated algorithm for simulation datageneration. We evaluate our framework through a case study on Luxembourg's Tax Law.
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