The energy performance is a relevant matter in the life cycle management of buildings in order to guarantee efficiency, affordability and compliance with the environmental and social purposes for sustainability in the long-term period. Accordingly, buildings’ energy efficiency is planned in the design phase and it is calculated according to procedure stated by Laws; nevertheless, the actual performance of the building differs by the predicted one due to factors associated to the uncertainties diffused in the modelling, construction and operating phases. In predicting the energy performance, design assumption and modelling tools define the boundaries of uncertainty while discussing about real performance built quality, occupancy behavior and management & controls determine the strong variability in the energy results. Therefore, building energy performance simulation requires models, which describe physical phenomena with different levels of detail and accuracy. Detailed dynamic models are accurate but on the other hand require detailed input data and the simulations are time-consuming whereas surrogate models consider only the most relevant parameters that contribute to outline the energy performance. The proposed methodology combines the two model strategies using the detailed simulations to train two Artificial Neural Network (ANN) capable of assessing the heating and cooling demands based on climate and occupancy data. The trained ANNs can predict energy performance of the building with different occupancy rates reducing the use of time-expensive detailed simulations. Moreover, in a Building Management System (BMS) ANNs may be fed by real-time data acquired by sensors and control the settings of systems and devices (e.g. HVAC, shading devices, artificial lighting, etc.). In the paper the Smart Campus Demonstrator or eLUX lab, a university building located in Brescia, Italy, is used as a case study to apply this methodology aiming into identify a range of performance reliability considering the users’ dependent segment of thermal consumption

1. BACK TO 4.0:
RETHINKING THE DIGITAL CONSTRUCTION INDUSTRY
Predicting Energy Performance of an Educational Building through
Artificial Neural Networks
Fulvio Re Cecconi, Lavinia Chiara Tagliabue, Angelo Luigi Camillo Ciribini, Enrico De Angelis
Convegno ISTeA 2016
Complesso dei SS. Marcellino e Festo - Università di Napoli Federico II
30 giugno – 1 luglio 2016

2. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
Performance gap
Main key factors
• Predicted energy performance
• design assumptions
• modelling tools
• Real performance
• built quality
• occupancy behaviour
• management & controls systems

3. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
Energy epidemiology (IEA-EBC Annex 70, 2016)
Energy epidemiology broadcasting the
interdisciplinary nature of the research and the
centrality of the users’ behaviour in the building
energy assessment.
The principle of interdisciplinary allows gaining
robust insights into end-use energy demand issues
integrating techniques and synthesizing theories.
In practice, this means drawing on expertise from a
variety of disciplines (e.g. social sciences, economics,
engineering, statistical, physics) and collaborating on
research problems to obtain findings that account
for wide-ranging socio-cultural, economic and
technical factors.

4. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
Research overview

5. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
eLUX lab at University of Brescia Smart Campus
The analysis on the building focuses on the variability of
energy demand for heating and cooling given by occupancy
uncertainty.
In the present research, the main topic is the use of the
results obtained by detailed simulation models to train an
artificial neural network (ANN).
The ANN is used to reconstruct the thermal behaviour of
the building with multiple benefit:
• reduction of calculation time;
• accuracy of the results in comparison with simplified
methods and detailed methods;
• overcome the uncertainties of the building physics
behaviour to provide a prediction on energy
performance based on few and tuneable parameters.

6. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
Building Energy
Simulations
The objective is to derive a model
that should be useful in the early
monitoring phase (easily tuneable),
which can be adjusted using online
modelling (e.g. regression on daily
data).
The different occupancy patterns
generated by randomly changing the
attendance values in the educational
building and simulated as input data
to the ANN are listed below:
• Minimum (5% of data);
• first quartile (25% of data);
• Median (50% of data);
• third quartile (75% of data);
• maximum (95% of data).

7. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
ANN structure
• Two-layer feedforward network with sigmoid hidden neurons and linear output
neurons trained with Bayesian regularization method.
• Custom function to find best network dimension.
1 2 3 4 59…
E
T I O1 O2 O4O3
1 2 3 4 85…
E
T I O1 O2 O4O3

8. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
ANN performance
optimization
ANN performance computed by mean squared normalized error

9. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis

10. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
Detailed vs. Surrogate
Energy demands for the five
occupancy profiles computed
using EnergyPlus are
compared to the ones
obtained by ANNs.
Surrogate models are a quite
a good approximation of
detailed simulations

11. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
Detailed vs. Surrogate
The mean error (me) for each
case measured as in equation:
The heating ANN shows in the
daily aggregated values some
discrepancy in term of peak
energy demand however, the
average difference from the
dynamic simulation results ranges
between -0.53% and 2.58.
The cooling ANN has a lower
discrepancy ranging from -0.005%
to 0.21%.

12. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
Conclusions
• ANNs to forecast energy demands for different occupancy patterns shows a high
potential of application
• The surrogate model can represent the interaction between input and output
data for the wide range of behavioral variability in the building use (-2.58% for
heating and 0.21% for cooling)
• ANNs are reliable tools suitable for multiple purposes, not limited to estimating
energy demand in multiple occupancy scenario.
• ANNs can be used to control building climate in real-time receiving data from
BMS sensors and seem to be a promising tool to define energy regulations

13. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
Future works
• Define probabilistic
occupancy patterns
• Monte Carlo simulation to
compute probabilistic
energy demands for
heating and cooling
• Outline acceptable errors
in predicted energy
demand to be used for
energy contracting

14. Convegno ISTeA 2016 - Napoli, 30 giugno e 1 luglio
“Predicting Energy Performance of an Educational Building through Artificial Neural Network”
F. Re Cecconi, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis
Thanks for your attention
Lavinia Chiara
Tagliabue
lavinia.tagliabue@unibs.it
Angelo Luigi Camillo
Ciribini
angelo.ciribini@unibs.it
Fulvio
Re Cecconi
fulvio.rececconi@polimi.it
Enrico
De Angelis
enrico.deangelis@polimi.it