On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design

This paper presents the optimization of building envelope design to minimize thermal load and improve thermal comfort for a two-star green building in Wuhan, China. The thermal load of the building before optimization is 36% lower than a typical energy-efficient building of the same size. A total of 19 continuous design variables, including different concrete thicknesses, insulation thicknesses, absorbance of solar radiation for each exterior wall/roof and different window-to-wall ratios for each façade, are considered for optimization. The thermal load and annual discomfort degree hours are selected as the objective functions for optimization. Two prediction models, multi-linear regression (MLR) model and an artificial neural network (ANN) model, are developed to predict the building thermal performance and adopted as fitness functions for a multi-objective genetic algorithm (GA) to find the optimal design solutions. As compared to the original design, the optimal design generated by the MLRGA approach helps to reduce the thermal load and discomfort level by 18.2% and 22.4%, while the reductions are 17.0% and 22.2% respectively, using the ANNGA approach. Finally, four objective functions using cooling load, heating load, summer discomfort degree hours, and winter discomfort degree hours for optimization are conducted, but the results are no better than the two-objective-function optimization approach.

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Section: Mlr Modelmentioning

confidence: 99%

“…A multi-linear regression model is a very popular approach and was proved to be able to predict annual building energy consumption by Asadi et al [43]. The regression model can be presented as: All the simulation cases were run using DesignBuilder (DesignBuilder Software Ltd, Stroud, Gloucestershire, UK) with a time step of 30 min.…”

Section: Mlr Modelmentioning

confidence: 99%

Design Optimization Considering Variable Thermal Mass, Insulation, Absorptance of Solar Radiation, and Glazing Ratio Using a Prediction Model and Genetic Algorithm

et al. 2018

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“…Global SA based on a linear regression is normally adopted to evaluate the relative importance of input variables [2,3,12]. When many input variables are involved, stepwise regression provides an alternative.…”

Section: Introductionmentioning

confidence: 99%

A comparison of approaches to stepwise regression on variables sensitivities in building simulation and analysis

Wang

Wright

Brownlee

et al. 2016

Energy and Buildings

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Highlights: The robustness of stepwise regression is irrespective of selection approach.  The linear regression model constructed by AIC has a high risk of overfitting, especially when the sample size is small.  A mixture of discretized-continuous and categorical variables can be used for global SA.  For stepwise regression, increasing sample size can identify more sensitive variables, but the importance of highly sensitive variables remains the same.  The importance of variables for design objectives and constraints is better to be classified through more than one sensitivity indexes. AbstractDeveloping sensitivity analysis (SA) that reliably and consistently identify sensitive variables can improve building performance design. In global SA, a linear regression model is normally applied to sampled-based solutions by stepwise manners, and the relative importance of variables is examined by sensitivity indexes. However, the robustness of stepwise regression is related to the choice of procedure options, and therefore influence the indication of variables' sensitivities. This paper investigates the extent to which the procedure options of a stepwise regression for design objectives or constraints can affect variables global sensitivities, determined by three sensitivity indexes. Given that SA and optimization are often conducted in parallel, desiring for a combined method, the paper also investigates SA using both randomly generated samples and the biased solutions obtained from an optimization run. Main contribution is that, for each design objective or constraint, it is better to conclude the categories of variables importance, rather than ordering their sensitivities by a particular index.Importantly, the overall stepwise approach (with the use of bidirectional elimination, BIC, rank transformation and 100 sample size) is robust for global SA: the most important variables are always ranked on the top irrespective of the procedure options.

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“…As shown in Equation (2), the combined effect is a unit-less coefficient that compares the simultaneous effect of any two parameters with the summation of their individual effects [38,39]. Secondly, buildings typically exhibit linear relationships between the characteristics of their end-use systems (e.g., lighting, equipment, and HVAC) and energy use levels [46][47][48][49]. Such linearity helps explain the low synergies that are observed between parameters, which is shown to be independent of the specific base and test values used in the analysis.…”

Section: Fractional Factorial Analysis Resultsmentioning

confidence: 99%

Impact of Human Actions on Building Energy Performance: A Case Study in the United Arab Emirates (UAE)

Amoodi

Azar

2018

Sustainability

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There is a growing interest in the literature to understand how actions taken by occupants and facility managers can affect building performance. However, user-centric building energy research: (1) remains understudied compared to design-focused research efforts; (2) overlooks combined effects or uncertainty in multiple parameters; and (3) typically does not cover particular types of buildings (e.g., educational facilities), nor buildings subject to extreme weather conditions. This paper fills an important gap in the literature by proposing a comprehensive energy modeling and analysis framework to quantify the impact of human action on building energy consumption. The framework applies various data analysis methods such as differential, fractional factorial, and Monte Carlo analysis methods, in order to capture potential combined or synergetic effects of human actions on building performance. A case study is then presented on typical educational buildings located in the extreme hot climate of Abu Dhabi, United Arab Emirates (UAE). Results indicate that uncertainty in human actions can lead up to a ±25% variation from average energy consumption levels, confirming the significant role that people have in making their built environment more efficient and sustainable.

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On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design

Cited by 200 publications

References 45 publications

Design Optimization Considering Variable Thermal Mass, Insulation, Absorptance of Solar Radiation, and Glazing Ratio Using a Prediction Model and Genetic Algorithm

Design Optimization Considering Variable Thermal Mass, Insulation, Absorptance of Solar Radiation, and Glazing Ratio Using a Prediction Model and Genetic Algorithm

A comparison of approaches to stepwise regression on variables sensitivities in building simulation and analysis

Impact of Human Actions on Building Energy Performance: A Case Study in the United Arab Emirates (UAE)

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