Applying Artificial Neural Networks to Promote

Behaviour Change for Saving Residential Energy

Yaqub Rafiq

, Shen Wei

, Robert Guest

, Robert Stone

and Pieter de Wilde

School of Marine Science and Engineering, Plymouth University, Plymouth, U.K.

Building Performance Analysis Group, Plymouth University, Plymouth, U.K.

School of Electronic, Electrical & Computer Engineering,

University of Birmingham, Birmingham, U.K.

Abstract. In this paper Artificial Neural Networks (ANNs) is used to model ef-

fects of various human behaviour on energy consumption of the residential

buildings in the UK. A virtual model of a bungalow has been developed in

which various aspects of the, physical changes in the building such as wall and

floor insulation, single and double glazing combined by the human behaviour

aspects such as thermostat setting, various periods of door and/or window open-

ing etc. are modelled using EnergyPlus software for evaluating energy con-

sumption for a combination of scenarios. ANNs were then used to learn the ef-

fects of various human behaviours on energy consumption. The results demon-

strated that the ANN is capable of learning the effects that changes in the hu-

man behaviour have in evaluating energy saving in residential buildings and it

generated results very quickly for unseen cases.

1 Introduction

Reducing residential energy consumption is essential for achieving the UK Govern-

ment’s 2050’s target of lowering CO

emissions. This is due to a high contribution to

the CO

emissions as the total energy consumption of the residential buildings in the

UK is about 30% [1]. Occupant behaviour plays an important role in energy usage in

residential buildings [2, 3]. Due to high importance of occupant behaviour on the

energy consumption, the UK government has funded several projects in the past 10

years, such as the green deal [4] and the 22 (Build)TEDDI projects [5], exploring

how to improve occupant behaviour in residential buildings for energy saving.

Currently, dynamic building performance simulation (DBPS) is being explored to

help occupants make informed decisions on adopting ways to save energy in their

houses [6-8]. An advantage of using DBPS is its flexibility in conducting parametric

studies by changing one aspect with respect to the definition of either the building,

building system or occupants’ building operation in DBPS, while keep other aspects

constant. Therefore, the impact of that particular change on the building energy con-

sumption can be reflected clearly by the simulation results. In this process, the energy

saving potential of possible behaviour change options was evaluated as the difference

between the predicted energy consumption before changing the behaviour with the

Raﬁq Y., Wei S., Guest R., Stone R. and de Wilde P..

Applying Artiﬁcial Neural Networks to Promote Behaviour Change for Saving Residential Energy.

DOI: 10.5220/0005049600030010

In Proceedings of the International Workshop on Artiﬁcial Neural Networks and Intelligent Information Processing (ANNIIP-2014), pages 3-10

ISBN: 978-989-758-041-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

one after the change.

In reality, behaviour change options, especially those relevant with occupants’

building operations, are complex [9, 10]. For example, the thermostatic setting in

winter can be set at any values within a reasonable range, i.e. between 14°C and

22°C. Therefore, using DBPS to predict the building energy consumption of all pos-

sible scenarios defined by these options or adding other options to this can make it a

complicated combinatorial problem, which would be unfeasible using DBPS. There-

fore, this paper has preliminarily tested the use of ANNs to model the energy con-

sumption with respect to behaviour change in buildings, and the performance of using

the ANN model for the prediction of changes in energy consumption has been vali-

dated against prediction results from DBPS.

2 Methodology

2.1 Case Study Building

The simulation model was developed based on a virtual UK bungalow building, as

shown in Figure 1a, by the teammates at the University of Birmingham. This house

has two bedrooms, a living room, a kitchen, a bathroom and a main corridor. The

main façade of the house faces the North.

(a) Case study house (b) Simulation model

Fig. 1. Case study Building and Simulation model.

2.2 Dynamic Building Performance Simulation

Generally, in residential buildings there are two ways for reducing energy consump-

tion [11]. The first way is by upgrading the insulation of the building façade or the

efficiency of the building systems, so that the building itself can be more energy-

efficient; the second method is by improving occupants’ operation of the building,

e.g. opening/closing windows and setting the temperature values for the heating sys-

tem etc., so that the building can be used more energy-efficiently. Based on this clas-

sification, the behaviour change options, investigated in this study, are listed in Table

1 with corresponding simulation scenarios defined for the DBPS.

Table 1. Simulation scenario definitions.

Behaviour change

options

Simulation

scenario (before)

Simulation scenario (after)

Upgrading external wall insulation No 50mm insulation

Upgrading ground floor insulation No 50mm insulation

Upgrading pitched roof insulation No 50mm insulation

Upgrading ceiling insulation No 50mm insulation

Upgrading window layers Single glazing Double glazing

Upgrading door layers Single glazing Double glazing

Lowering thermostatic setpoint 23°C

22°C, 21°C, 20°C, 19°C,

18°C, 17°C, 16°C or 15°C

Reducing window opening time 4 hours 3 hours, 2 hours, 1 hour or 0

Opening curtains during the day No Yes

Based on the information presented in the above table, 5760 possible scenarios can

be used to assess energy consumption in the building.

Figure 1b shows the simulation model of the case study building developed using

DesignBuilder [12], a commercial graphical user interface of EnergyPlus [13], which

is one of the popular DBPS tools in assessing building energy usage. In this paper,

EnergyPlus V7.2 was adopted as the simulation engine and the simulation model was

exported from the DesignBuilder to EnergyPlus. The weather data used in this simu-

lation was provided by the Climate Design Data 2009 ASHRAE Handbook, for Bir-

mingham UK applications. The simulation period was chosen as from 1

to 31

Janu-

ary, based on an interval of 30 minutes. The inputs of the simulation include defini-

tions of building construction, systems, occupants’ building operations and weather

conditions, while the output is heating energy demand during the simulation period.

In this study, the energy saving potential of each intervention listed in Table 1 was

calculated as difference between the predicted heating energy demand before the

intervention applied and the one after the intervention applied.

2.3 Artificial Neural Networks

ANNs have been widely used in a number of applications in buildings [14], e.g. pre-

dicting building energy consumption [15, 16], predicting thermal comfort in buildings

[17], designing [18, 19] and managing buildings [20, 21]. A Neural Network has the

ability to learn from experience and examples and then to adapt with changing situa-

tions [22], imitating some of the learning activities of the human brain. The ANN

model used in this study consists of two hidden layers of 10 and 5 neurones using

TANSIG activation function; one output layer of one neurone using LINEAR activa-

tion. It is a back propagation Neural Network [23] that was used to model the simu-

lated energy consumption at different behavioural scenarios, presented in Table 1.

The data for the Neural Network model was generated using EnergyPlus as discussed

in section 2.2. Both Levenberg-Marquardt (trainlm) and Bayesian regulation (trainbr)

back-propagation learning algorithms were used. In this study MATLAB R2013b

Neural Network Toolbox was used for developing the ANN model [24]. The training

and testing data consist of 8 input and one output variables.

2.4 Training and Validation of the ANN

In this study, two sets of data were prepared using EnergyPlus, one for training the

ANN and another for validating the trained ANN model. Figure 2a presents the com-

bination of the simulation scenarios used in training the ANN and Figure 2b shows

those used in validating the trained ANN model. The input data of the ANN are vari-

ous parameter definitions with respect to occupant behaviour in buildings and the

output of the ANN is the energy consumption for that simulation scenario. In Figure

2, the most left hand side parameters define the base case scenario of the case study

building against which the energy saving potential could be calculated for comparison

purposes. The remaining parameters on the right hand side columns define the possi-

ble interventions. Changes in the energy consumption is evaluated by replacing the

house original conditions from those of base case scenario to the one or a combina-

tion of scenarios presented on the right hand side column in Figure 2.

23°C

Heating setpoint:

21°C 19°C

Window opening hours:

Curtain open during the day:

Ye s

17°C 15°C

External wall insulation:

Ground floor insulation:

Roof insulation:

External window type:

50mm

Single

Double

External door type:

Single

Double

Ceiling insulation:

No 50mm

Heating setpoint:

22°C 20°C

Window opening hours:

Curtain open during the day:

Yes

18°C 16°C

External wall insulation:

Ground floor insulation:

Roof insulation:

External window type:

50mm

Single

Double

External door type:

Single

Double

Ceiling insulation:

No 50mm

(a) ANN training dataset definition (b) ANN validation dataset definition

Fig. 2. Dataset definition.

After considering the possible combination of all simulation scenarios defined in

Figures 2a and 2b, finally, 2941 possible scenarios were generated for the ANN train-

ing and testing processes (1917 for training the ANN and 1024 for validating the

ANN).

3 Results

As mentioned before the MATLAB Neural Network toolbox was used for both train-

ing and testing the Neural Network. Both ‘trainlm’ and ‘trainbr’ back-propagation

algorithms were used. At first, due to the speed of learning, ‘trainlm’ was used in the

learning process. A back-propagation network architecture having 2 hidden layers

and one output layer, was adopted for both learning algorithms.

Initially the data was divided into two sets for training and testing/validating pur-

poses. The training data included all data for 23°C, 21°C, 19°C, 17°C and 15°C cov-

ering upper, lower and intermediate temperature sets of data. This constituted a total

of 1917 set of data. The testing date included the rest data (1017 sets of data in total).

0 200 400 600 800 1000 1200 1400 1600 1800 2000

500

1000

1500

2000

2500

Number of data Points

Energy Used

Neural Net Prediction

Target

0 200 400 600 800 1000 1200

500

1000

1500

2000

Number of data Points

Energy Used

Neural Net Prediction

Target

Fig. 3. Neural Network training results for

selected data.

Fig. 4. Neural Network testing/validating

results for selected data.

0 500 1000 1500 2000 2500

500

1000

1500

2000

2500

Number of data Points

Energy Saved

Neural Net Prediction

Target

0 100 200 300 400 500 600 700 800

500

600

700

800

900

1000

1100

Number of data Points

Energy Saved

Neural Net Prediction

Target

Fig. 5. Neural Network training results for

extended data.

Fig. 6. Neural Network testing/validating

results for reduced data.

Figure 3 shows the results of the ANN learning 1917 sets of data. The network

successfully learned the presented data in a few minutes. Figure 4 shows results of the

validation/testing to see if the Neural Network has satisfactorily learnt and general-

ised necessary information presented to it. In all Figures the horizontal axis shows the

number of data points used in training and testing the ANN model and the vertical

axis shows energy usage. From Figure 4 it becomes clear that the Neural Network has

learned the data within the range 15°C to 19°C perfectly but failed to learn the rest of

the data outside this range. A quick fix would be to add the upper range (all data

higher than 19°C) to the training set. Figures 5 and 6 show that by doing this both

training and testing of the Neural Network processes are successful.

One problem with this quick fix is that the generalisation aspect of the ANN has

not been verified. This model can be used within the range of 15°C to 23°C but re-

sults from any other data would be doubtful. Another issue with this process is that

the data sets used in the learning process is more than that of the testing process. In

order to find an acceptable solution to this problem, the following steps have been

taken:

1. The data (combined training and testing data) for each temperature degree was

plotted using different colours in a single graph. This information is presented

in Figure 7. Form Figure 7 it becomes clear that the data is divided into dis-

tinct clusters for each °C. The more clear division is observed in the range be-

tween 15°C to 21°C with the rest of the data between 22°C and 23°C.

2. Based on the findings from (a), to improve the quality of training and testing

data, the training data was sampled from within each cluster and the remaining

of the data from each cluster was used for testing purposes.

Figure 8 shows results of the training process using only the training data from

each cluster. Similarly Figure 9 presents results from the testing/validating of the

trained Neural Network with the rest of the unseen data. The overall result of the

combined training and testing data is shown in Figure 10.

0 500 1000 1500 2000 2500 3000

500

1000

1500

2000

2500

Number of data Points

Energy Used

23 Degrees C

22 Degrees C

21 Degrees C

20 Degrees C

19 Degrees C

18 Degrees C

17 Degrees C

16 Degrees C

15 Degrees C

0 200 400 600 800 1000 1200

500

1000

1500

2000

2500

Number of data Points

Energy Used

Neural Net Prediction

Target

Fig. 7. Combined training and testing data

clusters.

Fig. 8. Training for selected data from each

cluster.

0 200 400 600 800 1000 1200 1400 1600 1800 2000

500

1000

1500

2000

2500

Number of data Points

Energy Used

Neural Net Prediction

Target

0 500 1000 1500 2000 2500 3000

500

1000

1500

2000

2500

Number of data Points

Energy Used

Neural Net Prediction

Target

Fig. 9. Combined testing extended data

clusters.

Fig. 10. Combined testing all data clusters.

From both Figures 9 and 10 it is clear that by selecting data within each cluster, the

Neural Network was able to learn and generalise the information presented to it.

Hence this trained network can be used confidently to predict the energy usage and

energy saving using particular scenarios for the case study building.

4 General Applications to Real Structure and Future Research

This paper has demonstrated that the trained Neural Networks can successfully pre-

dict the simulated building energy consumption when general behaviour change op-

tions are applied to a virtual case study building. For applications to real buildings,

there are still some important issues that need to be handled:

1. developing a representative simulation model for the real building and using

it for the preparation of data to train the Neural Networks;

2. realistically capturing occupants’ real behaviours in operating the building,

so that the base case simulation model can be developed as close as possible

to real situations;

3. realistically quantifying the behaviour change options in accordance with the

real applications; and,

4. implementing other factors that may influence occupants’ choice of applying

behaviour change options in the ANN, such as indoor thermal comfort and

investment payback period, rather than using energy consumption only.

An initial exploration on solving the above challenges is currently on-going by the

authors of this paper, and an Energy Efficient Education tool(s) is being developed to

help building occupants make informed decisions on changing behaviour for saving

residential energy demand.

5 Conclusions

Reducing energy consumption in residential building is a major problem universally.

Occupant behaviour has shown to have an important role in reducing energy con-

sumption. Evaluation of energy consumption, using traditional analytical method

using DBPS tools is computationally expensive and very time consuming. In this

paper ANNs have been used to instantaneously evaluate the effects of various combi-

nation of human behaviour on the residential buildings in the UK. The results demon-

strated that the ANN techniques can be used to predict the simulated energy saving

potentials of various human behavioural changes which eliminates expensive and

time consuming DBPS operations.

Acknowledgements

The work reported in this paper is funded by the Engineering and Physical Sciences

Research Council (EPSRC) under the Transforming Energy Demand in Buildings

through Digital Innovation (TEDDI) eViz project (grant reference EP/K002465/1).

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