Artificial Intelligence Applications for Identifying Key Features to Reduce Building Energy Consumption

Lakmini Rangana Senarathne, Purdue University

Abstract

The International Energy Agency (IEA) estimates that residential and commercial buildings consume 40% of global energy and emit 24% of CO2. A building's design parameters and location significantly impact its energy usage. Adjusting the building parameters and features in an optimum way helps to reduce energy usage and to build energy-efficient buildings. Hence, analyzing the impact of influencing factors is critical to reduce building energy usage.Towards this, artificial intelligence applications, such as Explainable Artificial Intelligence (XAI) and machine learning (ML) identified the key building features to reduce building energy. This is done by analyzing the efficiencies of various building features that impact building energy consumption. For this, the relative importance of input features impacting commercial building energy usage is investigated. Also analyzed is the parametric analysis of the impact of input variables on residential building energy usage. Furthermore, the dependencies and relationships between the design variables of residential buildings were examined. Finally, the study analyzed the impact of location features on cooling energy usage in commercial buildings.For the purpose of energy consumption data analysis, three datasets, named the Commercial Building Energy Consumption Survey (CBECS) datasets gathered in 2012 and 2018, University of California Irvine (UCI) energy efficiency dataset, and Commercial Load Data (CLD) were utilized. For this, Python and WEKA were used. Random Forest, Linear Regression, Bayesian Networks, and Logistic Regression predicted energy consumption using datasets. Moreover, statistical tests, such as the Wilcoxon-rank sum test were analyzed for the significant differences between specific datasets. Shapash, a Python library, created the feature important graphs.The results indicated that cooling degree days are the most important feature in predicting cooling load with contribution values 34.29% (2018) and 19.68% (2012). Also, analyzing the impact of building parameters on energy usage indicated that 50% of overall height reduction achieves a reduction of heating load by 64.56% and cooling load by 57.47%. Also, the Wilcoxon-rank sum test indicated that the location of the building also impacts energy consumption with a 0.05 error margin. The proposed analysis is beneficial for real-world applications and energy-efficient building construction.

Degree

M.S.

Advisors

Sundararajan, Purdue University.

Subject Area

Design|Artificial intelligence|Energy

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