A growth curve-based Bayesian hierarchical model for multi-building energy use data analysis

2021 ◽  
Vol 206 ◽  
pp. 108349
Author(s):  
Hoyeon Hwang ◽  
Yiyi Chu ◽  
Hyejin Eom ◽  
Kristen Cetin ◽  
Jongho Im
Keyword(s):  
Data Analysis ◽  
Hierarchical Model ◽  
Growth Curve ◽  
Energy Use ◽  
Bayesian Hierarchical Model ◽  
Building Energy ◽  
Bayesian Hierarchical ◽  
Building Energy Use

scholarly journals Bayesian adaptive Lasso Tobit regression

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Haider Kadhim Abbas ◽  
Rahim Jabbar Thaher
Keyword(s):  
Data Analysis ◽  
Model Selection ◽  
Hierarchical Model ◽  
Gibbs Sampler ◽  
Real Data ◽  
Bayesian Hierarchical Model ◽  
Adaptive Lasso ◽  
Tobit Regression ◽  
Bayesian Hierarchical ◽  
Coefficient Estimation

      In this paper, we introduce a new procedure for model selection in Tobit regression, we suggest the Bayesian adaptive Lasso Tobit regression (BALTR) for variable selection (VS) and coefficient estimation.  We submitted a Bayesian hierarchical model and Gibbs sampler (GS) for our procedure. Our proposed procedure is clarified by means of simulations and a real data analysis. Results demonstrate our procedure performs well in comparison to further procedures.  

scholarly journals Measuring spatial allocative efficiency in basketball

2020 ◽  
Vol 16 (4) ◽  
pp. 271-289
Author(s):  
Nathan Sandholtz ◽  
Jacob Mortensen ◽  
Luke Bornn
Keyword(s):  
Spatial Distribution ◽  
Hierarchical Model ◽  
Opportunity Cost ◽  
Allocative Efficiency ◽  
Bayesian Hierarchical Model ◽  
National Basketball Association ◽  
Spatial Context ◽  
Bayesian Hierarchical ◽  
The Impact

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.

scholarly journals Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

2021 ◽  
Vol 13 (4) ◽  
pp. 1595
Author(s):  
Valeria Todeschi ◽  
Roberto Boghetti ◽  
Jérôme H. Kämpf ◽  
Guglielmina Mutani
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Space Heating ◽  
Engineering Model ◽  
Building Energy Use ◽  
The Impact ◽  
The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

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