An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks

Azadeh Sadeghi, Roohollah Younes Sinaki, William A. Young and Gary R. Weckman
Additional contact information
Azadeh Sadeghi: Industrial and Systems Engineering, Russ College of Engineering, Ohio University, Athens, OH 45701, USA
Roohollah Younes Sinaki: Industrial and Systems Engineering, Russ College of Engineering, Ohio University, Athens, OH 45701, USA
William A. Young: Analytics and Information Systems, College of Business, Ohio University, Athens, OH 45701, USA
Gary R. Weckman: Industrial and Systems Engineering, Russ College of Engineering, Ohio University, Athens, OH 45701, USA

Energies, 2020, vol. 13, issue 3, 1-23

Abstract: As the level of greenhouse gas emissions increases, so does the importance of the energy performance of buildings (EPB). One of the main factors to measure EPB is a structure’s heating load (HL) and cooling load (CL). HLs and CLs depend on several variables, such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. This research uses deep neural networks (DNNs) to forecast HLs and CLs for a variety of structures. The DNNs explored in this research include multi-layer perceptron (MLP) networks, and each of the models in this research was developed through extensive testing with a myriad number of layers, process elements, and other data preprocessing techniques. As a result, a DNN is shown to be an improvement for modeling HLs and CLs compared to traditional artificial neural network (ANN) models. In order to extract knowledge from a trained model, a post-processing technique, called sensitivity analysis (SA), was applied to the model that performed the best with respect to the selected goodness-of-fit metric on an independent set of testing data. There are two forms of SA—local and global methods—but both have the same purpose in terms of determining the significance of independent variables within a model. Local SA assumes inputs are independent of each other, while global SA does not. To further the contribution of the research presented within this article, the results of a global SA, called state-based sensitivity analysis (SBSA), are compared to the results obtained from a traditional local technique, called sensitivity analysis about the mean (SAAM). The results of the research demonstrate an improvement over existing conclusions found in literature, which is of particular interest to decision-makers and designers of building structures.

Keywords: artificial neural networks; deep neural networks; machine learning; energy performance of buildings; heating load; cooling load; local sensitivity analysis; global sensitivity analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/3/571/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/3/571/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:3:p:571-:d:312867

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().