Evaluation of heating energy consumption patterns in the residential building sector using stepwise selection and multivariate analysis

Filippín, Celina; Ricard, Florencia; Larsen, Silvana Flores

doi:10.1016/j.enbuild.2013.07.054

Cited by 22 publications

(10 citation statements)

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“…For comparison purposes, we also estimate a stepwise regression using the same set of predictive factors. The stepwise regression method has been used for evaluating movements in crude prices (Alexandridis et al, 2008), electricity prices (Nan et al, 2014), heating energy consumption patterns (Filippin et al, 2013). In this study, we use the "bidirectional elimination" stepwise regression method to identify a subset of in ‡uential predictor variables.…”

Section: Stepwise Regression Methodsmentioning

confidence: 99%

Influential factors in crude oil price forecasting

et al. 2017

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This paper identi…es factors that are in ‡uential in forecasting crude oil prices. We consider six categories of factors (supply, demand, …nancial market, commodities market, speculative, and geopolitical) and test their signi…cance in the context of estimating various forecasting models. We …nd that the Least Absolute Shrinkage and Selection Operator (LASSO) regression method provides signi…cant improvements in the forecasting accuracy of prices compared to alternative benchmarks. Relative to the no-change and future-based models, LASSO forecasts at the 8-step ahead horizon yield signi…cant reductions in Mean Squared Prediction Error (MSPE), with MSPE ratios of 0.873 and 0.898, respectively. We also document substantial improvements in forecasting performance of the factor-based model that employs only a subset of variables chosen by LASSO. Finally, the time-varying nature of the relationship between factors and oil prices are used to explain recent movements in crude oil prices.

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Section: Stepwise Regression Methodsmentioning

confidence: 99%

Influential factors in crude oil price forecasting

et al. 2017

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“…These procedures iteratively construct regression models by adding or removing predictors based on a test statistic or minimizing an evaluative criterion, such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC), until a final model is attained. Stepwise regression techniques have been applied in numerous studies of energy consumption to identify relevant predictors [27,28,29,30]. Other approaches for variable selection in energy consumption studies include principal components regression (PCR) and partial least squares regression (PLSR) [31,32,33].…”

Section: Variable Selection and Related Challenges In Statistical Modelsmentioning

confidence: 99%

Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models

Satre-Meloy

2019

Energy

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Achieving further reductions in building electricity usage requires a detailed characterization of electricity consumption in homes. Understanding drivers of consumption can inform strategies for promoting conservation and efficiency. While there exist numerous approaches for modeling building energy demand, the use of regularization methods in statistical models can address challenges inherent to building energy modeling while also enabling more accurate predictions and better identification of variables that influence consumption. This paper applies five regularization techniques to regression models of original survey and electricity consumption data for more than one thousand households in California. It finds that of these, elastic net and two extensions of the lasso-group lasso and adaptive lasso-outperform other approaches in terms of prediction accuracy and model interpretability. These findings contribute to methodological approaches for modeling energy consumption in buildings as well as to our understanding of key drivers of consumption. The paper shows that while structural factors predominate in explaining annual electricity consumption patterns, habitual actions taken to save energy in the home are important for reducing consumption while pro-environmental attitudes and energy literacy are not. Implications for improving building energy modeling and for informing demand reduction strategies, are discussed in the context of the low-carbon transition.

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“…Esto es requisito para aplicar el análisis de datos a través de la matriz de correlaciones y posteriormente desarrollar un análisis de componentes principales (acp). (Balmaceda, Cantón & Correa, 2018;Filippín, Ricard & Larsen, 2013;Ruiz, Sosa, Correa & Cantón, 2015).…”

Section: Análisis Estadísticounclassified

Geomorfología y forma urbana. Comportamiento térmico de distintas tramas en áreas de piedemonte: el caso de Mendoza, Argentina

Castillo¹,

Correa²,

Cantón³

2019

EURE (Santiago)

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issn impreso 0250-7161 | issn digital 0717-6236 vol 45 | n o 136 | septiembre 2019 | pp. 183-207 | artículos | ©EURE Geomorfología y forma urbana. Comportamiento térmico de distintas tramas en áreas de piedemonte: el caso de Mendoza, Argentina Ana Castillo. Instituto de Ambiente, Hábitat y Energía (inahe), Mendoza, Argentina. Érica Correa. Instituto de Ambiente, Hábitat y Energía (inahe), Mendoza, Argentina. María Cantón. Instituto de Ambiente, Hábitat y Energía (inahe), Mendoza, Argentina.resumen | El crecimiento urbano sobre el piedemonte del área metropolitana de Mendoza, Argentina, ha modificado las condiciones del territorio y afectado el balance energético en él, dando como resultado un incremento de la temperatura. Esto produce un mayor consumo de energía para el acondicionamiento térmico de verano y empeora la habitabilidad térmica de los espacios públicos. Para detectar configuraciones urbanas que permitan mitigar este impacto, se analizó la interacción entre los parámetros microclimáticos y variables geomorfológicas que definen el comportamiento térmico de los espacios urbanos. Se seleccionaron, caracterizaron geomorfológica, morfológica y tecnológicamente, y monitorearon microclimáticamente, ocho canales viales urbanos representativos insertos en diferentes tipologías de tramas, presentes en el área de estudio. Los datos fueron evaluados estadísticamente mediante análisis multivariados. La investigación pone en relevancia el impacto de las decisiones de diseño al momento de priorizar estrategias para alcanzar la sustentabilidad del ambiente construido en áreas residenciales localizadas en el piedemonte.palabras clave | morfología urbana, sustentabilidad urbana, medioambiente urbano.abstract | The urban growth that takes place on the piedmont of the metropolitan area of Mendoza, Argentina has modified the conditions of the territory and the energy balance, resulting in an increase of temperature. This produces an increased energy consumption for summer thermal conditioning and deteriorates the thermal habitability of public spaces.To detect urban configurations that mitigate this impact, the interaction between microclimatic parameters and geomorphological variables that define the thermal behavior of urban spaces were analyzed. Eight representative urban canyons inserted in different types of frames, present in the study area, were selected, characterized geomorphologically, morphologically and technologically, and monitored microclimatically. The data were evaluated statistically by multivariate analysis. The research highlights the impact of design decisions when prioritizing strategies to achieve the sustainability of the built environment in residential areas located in the piedmont. keywords | urban morphology, urban sustainability, urban environmentRecibido el 14 de noviembre de 2017, aprobado el 10 de julio de 2018.

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Evaluation of heating energy consumption patterns in the residential building sector using stepwise selection and multivariate analysis

Cited by 22 publications

References 30 publications

Influential factors in crude oil price forecasting

Influential factors in crude oil price forecasting

Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models

Geomorfología y forma urbana. Comportamiento térmico de distintas tramas en áreas de piedemonte: el caso de Mendoza, Argentina

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