Publication
Regression Analysis of Building Energy Demand
2015
Student project
Student project
Abstract
This project attempts to tackle the problem of analysing the heat demand of build- ings at a large (district or city) scale. Specifically, building data from Geneva is used as a case study while building an environment with suitable tools for a continued analysis. Detailed modelling of these systems is limited greatly by not only com- putational resources, but also by data availability. Meta-modelling is a potential technique to overcome these issues, and the first steps towards this are analysis of available data and parameter interactions, as well as identification of key influence parameters, through the use of stepwise regression techniques. A set of tools to accomplish these goals is introduced, and then used to perform an initial analysis.
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Related concepts (32)
Regression analysis
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.
Linear regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Segmented regression
Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions.
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