predicting energy performance of an educational building through artificial neural network

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

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



Energy epidemiology (IEA-EBC Annex 70, 2016)

Energy epidemiology broadcasting theinterdisciplinary nature of the research and thecentrality of the users’ behaviour in the buildingenergy assessment.

The principle of interdisciplinary allows gainingrobust insights into end-use energy demand issuesintegrating techniques and synthesizing theories.

In practice, this means drawing on expertise from avariety of disciplines (e.g. social sciences, economics,engineering, statistical, physics) and collaborating onresearch problems to obtain findings that accountfor wide-ranging socio-cultural, economic andtechnical factors.



Research overview



eLUX lab at University of Brescia Smart Campus

The analysis on the building focuses on the variability ofenergy demand for heating and cooling given by occupancyuncertainty.

In the present research, the main topic is the use of theresults obtained by detailed simulation models to train anartificial neural network (ANN).

The ANN is used to reconstruct the thermal behaviour ofthe building with multiple benefit:

• reduction of calculation time;

• accuracy of the results in comparison with simplifiedmethods and detailed methods;

• overcome the uncertainties of the building physicsbehaviour to provide a prediction on energyperformance based on few and tuneable parameters.



Building Energy Simulations

The objective is to derive a modelthat should be useful in the earlymonitoring phase (easily tuneable),which can be adjusted using onlinemodelling (e.g. regression on dailydata).

The different occupancy patternsgenerated by randomly changing theattendance values in the educationalbuilding and simulated as input datato 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).



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



ANN performance optimization

ANN performance computed by mean squared normalized error



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



Detailed vs. Surrogate

The mean error (me) for each case measured as in equation:

The heating ANN shows in thedaily aggregated values somediscrepancy in term of peakenergy demand however, theaverage difference from thedynamic simulation results rangesbetween -0.53% and 2.58.

The cooling ANN has a lowerdiscrepancy ranging from -0.005%to 0.21%.



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 outputdata 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



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



Thanks for your attention

Lavinia Chiara Tagliabue

[email protected]

Angelo Luigi Camillo Ciribini

[email protected]

Fulvio Re Cecconi

[email protected]

EnricoDe Angelis

[email protected]

predicting energy performance of an educational building through artificial neural network

Engineering