Building Energy Optimisation Using Artificial Neural Network and Ant

Colony Optimisation

Authors:

Keivan Bamdad (Presentor)

Dr Michael E. Cholette

Dr Lisa Guan

Prof John Bell

➢ Buildings

▪ 40% of world energy consumption

➢ Zero-energy buildings

▪ A building that produces as much energy in a year as it consumes

➢ Key steps to design zero-energy buildings

1. Improve building design (Reduce energy)

2. Use renewable energy (Produce energy)

➢ Envelope parameters

▪ Orientation, window type, overhang, insulation

❖ Parametric (Sensitivity) Analysis

▪ Common method to improve building design

▪ Find optimum value of only one variable each time

➢ Drawbacks

▪ Cannot handle interactions among objective functions and variables

▪ Window-to-wall ratio will influence the optimal overhang

▪ Final design may be far from optimal design

▪ Some potential energy savings measures are not explored

➢ Select best solution from set of numerous available alternatives based

on mathematical optimisation algorithm

➢ Building Optimisation Problems

1. Software-in-the-loop (Simulation-based optimisation)

2. Surrogate-based optimisation

Optimisation

Global search(Optimisation)

Local search(Sensitivity Analysis)

Software-in-the-loop

▪ Most common method for BOPs

▪ Building simulation software is coupled with an optimisation algorithm

Building Simulation Software

(EnergyPlus)

OptimizationAlgorithm

Solution Stoppin

g Criteria?

➢Example

▪ Building energy optimisation (15 variables)

▪ ~4000 building simulation with EnergyPlus1

➢ Limitation: High computational cost

1 Keivan Bamdad, Michael E. Cholette, Lisa Guan, John Bell, Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms, Energy and Buildings,

Volume 154, 2017, Pages 404-414

Surrogate-based optimisation

➢ Surrogate model

Mathematical approximation of building thermal

performance created using data

➢ Surrogate-based optimisation

1. Create dataset of building simulation results

2. Create a surrogate model of the building

3. Optimisation of surrogate model

Surrogate Model

OptimizationAlgorithm

Solution Stoppin

g Criteria?

Dataset of building results

Building modelling

• Type B: representative of typical medium-size commercial buildings in Australia

EnergyPlus

Weather Data

Geometry(SketchUp)

Input Parameters (ABCB)

Building Energy Consumption

Model Validation

0

400

800

1200

1600

2000

Brisbane Darwin Hobart Melbourne

Type B State Average ±1 Std Dev

Discrepancy for Darwin 1

• Different building constructions in the climate• Higher cooling set-points • Differences in occupant behaviour

1Daly, D., P. Cooper, and Z. Ma, Understanding the risks and uncertainties introduced by common assumptions in energy simulations for Australian commercial buildings. Energy and Buildings, 2014. 75: p. 382-393

Problem Statement

Variables Description Variable Range

X1 Roof emissivity [0.5-0.9]

X2 Roof solar absorptance [0.3-0.85]

X3 Wall insulation (m) [0.01-0.1]

X4 Wall solar absorptance [0.3-0.9]

X5 North window height (m) [0.5-1.5]

X6 South window height (m) [0.5-1.5]

X7 East window height (m) [0.5-1.5]

X8 West window height (m) [0.5-1.5]

X9 North overhang depth (m) [0-1.5]

X10 South overhang depth (m) [0-1.5]

X11 East overhang depth (m) [0-1.5]

X12 West overhang depth (m) [0-1.5]

X13 Heating setpoint [18-22]

X14 Cooling setpoint [23-27]

X15 Building orientations [0-45]

➢ Objective Function

𝐦𝐢𝐧 𝒇 𝒙

𝐬𝐮𝐛𝐣𝐞𝐜𝐭 𝐭𝐨: 𝒙 ∈ 𝕏 ⊆ ℝ𝒓 × 𝕍𝒄

𝒇 . : Building energy consumption MJ/m2

ℝ𝒓: Search space for continuous variables,

𝕍𝒄: Search space for discrete variables

Ant Colony Optimisation

Interior Point Algorithm (IPA)

ACOR Optimisation:

1. Randomly generate initial solutions

2. Store all solutions in the Solution Archive3. New solution

▪ Select a solution from the archive based on the probability ▪ Generate solutions based on Gaussian function

4. Update Solution Archive5. Check the stopping criteria. If not satisfied, generate new

solutions

𝑥11

𝑥12

…𝑥1𝑖

…𝑥1𝑁

𝑓(𝐱1) 𝜔1

𝑥21

𝑥22

…𝑥2𝑖

…𝑥2𝑁

𝑓(𝐱2) 𝜔2

⋮ ⋮ … ⋮ ⋮ ⋮ ⋮ ⋮

𝑥𝑗1

𝑥𝑗2

…𝑥𝑗𝑖

…𝑥𝑗𝑁

𝑓(𝐱𝑗) 𝜔𝑗

⋮ ⋮ ⋱ ⋮ ⋮ ⋮ ⋮ ⋮

𝑥𝑀1

𝑥𝑀2

…𝑥𝑀𝑖

…𝑥𝑀𝑁

𝑓(𝐱𝑀) 𝜔𝑀

Solution Archive

➢ Finding the solutions using either Hessian function or gradient method

Optimisation framework

EnergyPlus

Weather Data

SketchUp(Geometry)

Input Parameters (ABCB)

Building Energy Consumption

ACOR or IPA

(Matlab)

New solutions (design)

Software in the loop

Optimisation framework

Surrogate Model

Created based on dataset of building simulation results

Building Energy Consumption

ACOR or IPA

(Matlab)

New solutions (design)

Surrogate based optimisation

Artificial Neural Network (ANN)

Building Energy Consumption

Input parameters

Hidden layer

Window size

Orientation⋮

𝑦 = 𝑓

𝑖=1

𝑛

𝑤𝑖 𝑥𝑖 + 𝑏

Dataset of building

simulation

Artificial Neural

Network (Surrogate)

Optimisation (ACOR) Solution

Results

0

200

400

600

800

1000

1200

1400

1600

1800

Before Optimisation After Optimisation Before Optimisation After Optimisation

Brisbane Melbourne

Heat Rejection Pumps Fans Interior Equipment Interior Lighting Cooling Heating

➢ Cooling loads reduction: 50%

➢ Energy savings : 20%

GJ

Results

Algorithm

Obje

ctiv

e F

unct

ion

()

Ro

of

emis

sivi

ty

Ro

of

sola

r

abso

rpta

nce

Wal

l in

sula

tio

n (c

m)

Wal

l so

lar

abso

rpta

nce

No

rth

win

do

w

hei

ght

(m)

Sou

th w

ind

ow

hei

ght

(m)

East

win

do

w h

eigh

t

(m)

Wes

t w

ind

ow

hei

ght

(m)

No

rth

ove

rhan

g

dep

th (

m)

Sou

th o

verh

ang

dep

th (

m)

East

ove

rhan

g d

epth

(m)

Wes

t o

verh

ang

dep

th (

m)

Hea

tin

g se

tpo

int

Co

olin

g se

tpo

int

Bu

ildin

g o

rien

tati

on

s

Brisbane

PSO 639.79 0.88 0.54 1 0.3 0.54 0.5 1.24 0.67 0.56 0.46 1.22 0.44 18 27 44.3

ACOR 629.73 0.88 0.3 1 0.3 0.5 0.5 0.5 0.72 0.55 0.54 0.54 1.44 18 27 11.10

SMO with ACOR 631.14 0.9 0.3 1 0.3 0.5 0.5 0.5 0.5 0.23 0.24 0.37 0.65 18.5 27 9.94

SMO with ACOR-

IPA631.14 0.9 0.3 1 0.3 0.5 0.5 0.5 0.5 0.23 0.24 0.37 0.65 18.5 27 9.94

Melbourne

PSO 591.15 0.88 0.37 5 0.84 0.55 0.5 0.83 0.73 0.26 0.47 0.93 1.46 18 27 16.3

ACOR 583.33 0.89 0.3 9 0.3 0.5 0.5 0.64 0.73 0.54 0.15 0.7 1.36 18 27 25.53

SMO with ACOR 583.17 0.9 0.3 10 0.3 0.5 0.5 0.5 0.5 0.34 0 0.48 0.68 18 27 21.2

SMO ACOR-IPA 583.18 0.9 0.3 10 0.3 0.5 0.5 0.5 0.5 0.34 0 0.48 0.68 18 27 21.2

Convergence speed:Surrogate based optimisation method

VS Software-in-the-loop

Thank you !