Abstract

Building energy models (BEMs) are usually developed by subject matter experts during the design phase to help with decision making for achieving a more energy-efficient design at a minimum cost. The energy performance of a building is subject to significant changes as its operational parameters vary (e.g., occupancy, schedule of operation, etc.) due to different reasons such as change in building spaces application, demands, pandemic situation, among other reasons. In other words, a BEM that is created based on “as-designed” condition to predict building energy consumption (EC) can potentially become much less accurate during the lifetime of the building given the potential changes to the “in-operation” conditions. While BEMs can be adjusted to address operational changes, the end-user (i.e., building owner, manager, etc.) usually does not possess the knowledge to work with physics-based models (e.g., eQUEST) and therefore the initial BEM may no longer be of use to them. In the present paper, an approach is proposed and assessed through which a physics-based model is developed using eQUEST and simulated for several different operating conditions. The resulting data are then used for training an artificial neural network (ANN) which can serve as a simple and data-driven model for prediction of building energy consumption in response to changes in operating conditions. A case study is performed for a building on the campus of Florida Institute of Technology, to explore the changes that occurred in the building schedule of operation during COVID-19 pandemic and its impact on the performance of BEM. The inputs to the ANN are considered average daily values for outside dry bulb temperature, total daily global horizontal irradiation, hours of operation for the heating, ventilation, and air conditioning (HVAC) system for the main building, and hours of operation for the HVAC system for the conference room, while the output is considered as the monthly energy consumption of the building. The trained ANN is then tested against the actual measured data for energy consumption (post-construction) under different scenarios and good agreement between the results is found. The approach presented in this work aims to serve as a methodology for using data-driven surrogate models that can be used beyond the construction phase of the building and in response to sudden changes in building operating conditions.

References

1.
Tricoire
,
J.-P.
,
2021
, “
Why Buildings are the Foundation of an Energy-Efficient Future
,” [Online]. Available: https://www.weforum.org/agenda/2021/02/why-the-buildings-of-the-future-are-key-to-an-efficient-energy-ecosystem/
2.
U.S. Energy Information Administration
,
2021
, “
How Much Energy is Consumed in the U.S.?
,” [Online]. Available: https://www.eia.gov/tools/faqs/faq.php?id=86&t=1
3.
Doiphode
,
G.
,
Najafi
,
H.
, and
Migliori
,
M.
,
2020
, “
Energy Efficiency Improvement in K-12 Schools: A Case Study in Florida
,”
ASME J. Eng. Sustainable Bldgs. Cities
,
2
(
1
), p.
011001
.
4.
Thompson
,
J.
, and
Krarti
,
M.
,
2021
, “
Cost-Effectiveness and Resiliency Evaluation of Net-Zero Energy U.S. Residential Communities
,”
ASME J. Eng. Sustainable Bldgs. Cities
,
2
(
3
), p.
031002
.
5.
Flores
,
J. A.
,
2011
,
Focus on Artificial Neural Network
,
Nova Science Publishers
,
New York
.
6.
Paterson
,
G.
,
Mumovic
,
D.
,
Das
,
P.
, and
Kimpian
,
J.
,
2017
, “
Energy Use Predictions With Machine Learning During Architectural Concept Design
,”
Sci. Technol. Built Environ.
,
23
(
6
), pp.
1036
1048
.
7.
Mocanu
,
E.
,
Nguyen
,
P. H.
,
Gibescu
,
M.
, and
Kling
,
W. L.
,
2016
, “
Deep Learning for Estimating Building Energy Consumption
,”
Sustainable Energy Grids Netw.
,
6
, pp.
91
99
.
8.
Banihashemi
,
S.
,
Ding
,
G.
, and
Wang
,
J.
,
2017
, “
Developing a Hybrid Model of Prediction and Classification Algorithms for Building Energy Consumption
,”
Energy Procedia
,
110
, pp.
371
376
.
9.
Moayedi
,
H.
, and
Mosavi
,
A.
,
2021
, “
Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings
,”
Energies
,
14
(
5
), p.
1331
10.
Li
,
R.
,
Zhang
,
X.
,
Liu
,
L.
,
Li
,
Y.
, and
Xu
,
Q.
,
2020
, “
Application of Neural Network to Building Environmental Prediction and Control
,”
Build. Serv. Eng. Res. Technol.
,
41
(
1
), pp.
25
45
.
11.
Biswas
,
M. A. R.
,
Robinson
,
M. D.
, and
Fumo
,
N.
,
2016
, “
Prediction of Residential Building Energy Consumption: A Neural Network Approach
,”
Energy
,
117
(
1
), pp.
84
92
.
12.
Elbeltagi
,
E.
, and
Wefki
,
H.
,
2021
, “
Predicting Energy Consumption for Residential Buildings Using ANN Through Parametric Modeling
,”
Energy Rep.
,
7
, pp.
2534
2545
.
13.
Luo
,
X. J.
, and
Oyedele
,
L. O.
,
2021
, “
Forecasting Building Energy Consumption: Adaptive Long-Short Term Memory Neural Networks Driven by Genetic Algorithm
,”
Adv. Eng. Inform.
,
50
, p.
101357
14.
Tran
,
D. H.
,
Luong
,
D. L.
, and
Chou
,
J. S.
,
2019
, “
Nature-Inspired Metaheuristic Ensemble Model for Forecasting Energy Consumption in Residential Buildings
,”
Energy
,
191
, p.
116552
.
15.
Al-Shargabi
,
A. A.
,
Almhafdy
,
A.
,
Ibrahim
,
D. M.
,
Alghieth
,
M.
, and
Chiclana
,
F.
,
2021
, “
Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
,”
Sustainability
,
13
(
22
), p.
12442
.
16.
Diophode
,
G.
, and
Najafi
,
H.
,
2020
, “
A Machine Learning Based Approach for Energy Consumption Forecasting in K-12 Schools
,”
Proceedings of the ASME International Mechanical Engineering Congress and Exposition
,
Portland, OR
,
Nov. 16–19
.
17.
ASHRAE
,
2017
, “
Climatic Data for Building Design Standards
”.
18.
Kahn
,
L.
, and
Najafi
,
H.
,
2021
, “
An Investigation of the Impact of COVID-19 Pandemic on Energy Consumption in the United States
,”
ASME J. Eng. Sustainable Bldgs. Cities
,
2
(
3
), p.
031004
.
19.
González
,
J. E.
, and
Krarti
,
M.
,
2021
, “
Reflecting on Impacts of COVID19 on Sustainable Buildings and Cities
,”
ASME J. Eng. Sustainable Bldgs. Cities
,
2
(
1
), p.
010201
.
20.
Amoah
,
K.
,
Nguyen
,
T.
, and
Najafi
,
H.
,
2020
, “
A Multi-Facet Retrofit Approach to Improve Energy Efficiency of Existing Class of Single-Family Residential Buildings in Hot-Humid Climate Zones
,”
Energies
,
13
(
5
), p.
1178
.
21.
Betharte
,
O.
,
Najafi
,
H.
, and
Nguyen
,
T.
,
2018
, “
Towards Net-Zero Energy Buildings: A Case Study in Humid Subtropical Climate
,”
ASME International Mechanical Engineering Congress and Expo
,
Pittsburgh, PA
,
Nov. 9–15
.
22.
Department of Energy
, “
eQUEST, the Quick Energy Simulation Tool
,” [Online]. Available: https://www.doe2.com/equest/, Accessed December 2020.
23.
Standford
, “
Neural Networks: Biological Inspiration
,” [Online]. Available: https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/Biology/index.html, Accessed March 2021.
24.
Towards Data Science
,
August 2016
, “
A Concise History of Neural Networks
,” [Online]. Available: https://towardsdatascience.com/a-concise-history-of-neural-networks-2070655d3fec#:∼:text=The%20idea%20of%20neural%20networks,McCulloch%20and%20mathematician%20Walter%20Pitts, Accessed March 2021.
25.
M.
Hudson
,
B.
Martin
,
T.
Hagan
, and
H. B.
Demuth
,
2020
, “
Deep Learning ToolboxTM Getting Started Guide
,” MathWorks, [Online]. Available: www.mathworks.com, Accessed January 2021.
26.
MATLAB
, “
Supervised Learning Workflow and Algorithms
,” [Online]. Available: https://www.mathworks.com/help/stats/supervised-learning-machine-learning-workflow-and-algorithms.html, Accessed March 2021.
27.
MATLAB
, “
Plot Network Performance
,” [Online]. Available: https://www.mathworks.com/help/deeplearning/ref/plotperform.html, Accessed March 2021.
You do not currently have access to this content.