Building energy performance forecasting: A multiple linear regression approach. (1st November 2019)

• Record Type: Journal Article
• Authors: Ciulla, G.; D'Amico, A.
• Is Part Of: Applied energy. Volume 253(2019)
• Publication Date: 2019-11-01
• Subjects: Building energy demand -- Sensitivity analysis -- Forecast method -- Dynamic simulation -- Black box method -- Multiple linear regression; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113500
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: Building energy demand assessment designed with high-energy performance. Parametric simulation to develop an accurate energy database. Sensitivity analysis to identify the main parameters of the building energy balance. Forecasting of the building energy needs through the black box method. Multiple Linear Regression to identify simple correlations with high reliable degree. Abstract: Different ways to evaluate the building energy balance can be found in literature, including comprehensive techniques, statistical and machine-learning methods and hybrid approaches. The identification of the most suitable approach is important to accelerate the preliminary energy assessment. In the first category, several numerical methods have been developed and implemented in specialised software using different mathematical languages. However, these tools require an expert user and a model calibration. The authors, in order to overcome these limitations, have developed an alternative, reliable linear regression model to determine building energy needs. Starting from a detailed and calibrated dynamic model, it was possible to implement a parametric simulation that solves the energy performance of 195 scenarios. The lack of general results led the authors to investigate a statistical method also capable of supporting an unskilled user in the estimation of the building energy demand. To guarantee high reliability and ease of use, a selection of the most suitable variables was conducted by careful sensitivity analysis using the Pearson coefficient. The Multiple Linear Regression method allowed the development of some simple relationships to determine the thermal heating or cooling energy demand of a generic building as a function of only a few, well-known parameters. Deep statistical analysis of the main error indices underlined the high reliability of the results. This approach is not targeted at replacing a dynamic simulation model, but it represents a simple decision support tool for the preliminary assessment of the energy demand related to any building and any weather condition.