Con en s lis s a ailable a ScienceDi ec
Ene gy
jou nal homepage: www.else ie .com/loca e/ene gy
Enhancing ene gy e iciency h ough use engagemen and beha iou
change: A e iew on gami ica ion app oaches and se ious games in ene gy
sys ems
Ne mina Abdu ahmano ic ∗
, Anna Cadenbach
F aunho e Ins i u e o Ene gy Economics and Ene gy Sys em Technology (IEE), Ge many
A R T I C L E I N F O
Keywo ds:
Gami ica ion
Dis ic hea ing sys ems
Use engagemen
Beha iou change
Ene gy e iciency
A B S T R A C T
Pu ely echnical solu ions a e insu icien o op imise dis ic hea ing sys ems (DHS) and end-use beha iou
signi ican ly impac s he sys em and i s pe o mance. Thus, p io i ising cus ome engagemen s a egies
and os e ing coope a ion wi h consume s a e c ucial o ealising he ull po en ial o DHS. This pape
explo es he po en ial o gami ica ion as an e ec i e me hod o engaging end-use s. While gami ica ion
has demons a ed success in a ious ields, i s po en ial in DHS equi es u he in es iga ion. To add ess
his, he pape conduc s a comp ehensi e li e a u e e iew on gami ica ion, ocusing on i s applica ion in
sma ene gy sys ems and DHS. The s udy examines a ious gami ica ion elemen s and echniques, analysing
case s udies and examples o gami ica ion implemen a ion in ene gy sys ems. I e alua es he po en ial
impac and bene i s on use engagemen and ene gy consump ion pa e ns in hea ing sys ems, including
inc eased ene gy e iciency, educed cos s, and enhanced use sa is ac ion. Repo ed ene gy sa ings om
gami ied solu ions a y signi ican ly, anging om 4 % o 42 %, highligh ing hei po en ial o ans o m
ene gy consump ion beha iou . Addi ionally, he s udy iden i ies challenges and limi a ions associa ed wi h
implemen ing gami ica ion in DHS and explo ed he use o echnologies like a i icial in elligence and machine
lea ning o o e come hem. These echnologies enhance use engagemen by analysing and p edic ing use
beha iou and p e e ences. By doing so, his esea ch iden i ies key gaps and po en ial ad ancemen s in
gami ica ion echniques o DHS, aiming o op imise sys em pe o mance and enhance digi alisa ion.
1. In oduc ion
The Pa is Ag eemen is es ablished o add ess he u gen challenges
o clima e change, se ing a global goal o limi empe a u e ise
o below 2 ◦C [1]. I unde sco es he necessi y o ans o ma ion
ac oss a ious sec o s, pa icula ly in ene gy sys ems. These sys ems
a e pi o al in implemen ing e ec i e clima e change measu es, as hey
di ec ly in luence g eenhouse gas emissions and ene gy consump ion
pa e ns. Wi hin ene gy sys ems, he he mal sec o accoun s o 50%
o Eu ope’s ene gy demand [2], wi h dis ic hea ing sys ems (DHS)
playing a c i ical ole in p omo ing sus ainable ene gy p ac ices [3].
Fu he mo e, aking in o accoun o he ene gy sec o s, he in eg a-
ion o sec o -coupled sys ems p esen s signi ican oppo uni ies o
enhancing he o e all e iciency and esilience o ene gy sys ems [4].
Recen in es iga ions in o sec o -coupled DHS highligh he impo -
ance o echnological ad ancemen s [5], s o age solu ions [6], he
ole o p osume s [7], and he po en ial bene i s o low- empe a u e
hea ing solu ions [8]. While echnological ad ancemen s a e c i ical,
∗Co esponding au ho .
E-mail add ess: [email p o ec ed] (N. Abdu ahmano ic).
he ac i e in ol emen o consume s is equally impo an o ealising
he ull po en ial o DHS [9]. Consume s can in luence sys em as
use s and con ibu o s, in luencing ene gy demand pa e ns and sys em
e iciency [10]. Engaging consume s in ene gy sys ems is essen ial o
maximising e iciency and achie ing sus ainabili y goals.
Di e en s a egies ha e been p oposed and used o enhance con-
sume in ol emen , including demand- esponsi e con ol s a egies,
sma home echnology [11] o coope a i e owne ship o DHS [12].
Howe e , hey all ely on unde s anding and mo i a ing use be-
ha iou . Gami ica ion and se ious games a e eme ging as inno a i e
me hods o mo i a e and educa e use s [13] success ully applied in
ields like medicine [14], educa ion [15], ou ism [16], compu e
science [17] and o he s. Howe e , hei applica ion in ene gy sys-
ems emains limi ed, wi h mos examples ocusing on elec ici y o
building-le el in e en ions [18]. The po en ial o gami ica ion in DHS,
pa icula ly i s b oade bene i s and challenges, emains unexplo ed.
h ps://doi.o g/10.1016/j.ene gy.2025.137496
Recei ed 20 Decembe 2024; Recei ed in e ised o m 15 May 2025; Accep ed 8 July 2025
Ene gy 334 (2025) 137496
A ailable online 19 July 2025
0360-5442/© 2025 The Au ho s. Published by Else ie L d. This is an open access a icle unde he CC BY license ( h p://c ea i ecommons.o g/licenses/by/4.0/ ).
N. Abdu ahmano ic and A. Cadenbach
Abb e ia ions
DH Dis ic hea ing
DHS Dis ic hea ing sys em
4GDHS 4 h gene a ion dis ic hea ing sys ems
AI A i icial in elligence
ML Machine lea ning
ICT In o ma ion and Communica ion Technologies
IoT In e ne o Things
The e o e, his pape aims o con ibu e o he esea ch by add essing
key ques ions o ad ance his ield.
•Wha a e he mos e ec i e gami ica ion elemen s and echniques
o engaging end use s in DHSs, and how do hey in luence ene gy
consump ion pa e ns and use beha iou ?
•Wha a e he measu able bene i s o gami ica ion in DHSs and
wha a e he limi a ions and exis ing esea ch gaps in his a ea?
•How can digi alisa ion and sma echnologies be applied in DHS
o enhance he e ec i eness o gami ica ion?
To add ess hese esea ch ques ions, a comp ehensi e li e a u e
e iew is conduc ed using a s uc u ed me hodology (Sec ion 2), ol-
lowed by an analysis o gami ica ion elemen s (Sec ion 3.2) and hei
impac (Sec ion 3.3). The challenges and limi a ions a e hen iden-
i ied (Sec ion 3.4), along wi h p oposed solu ions o add ess hem
(Sec ion 4).
2. Me hodology
To add ess he esea ch ques ions, he li e a u e e iew has been
conduc ed ollowing a s uc u ed me hodology. The p ocess is di ided
in o h ee main s ages: li e a u e collec ion, il e ing and analysis. An
o e iew o he me hodology is gi en on Fig. 1.
2.1. Li e a u e collec ion
The i s s age in ol ed o mula ing a sea ch s ing designed o
cap u e key e ms ele an o he s udy. The sea ch s ing included
e ms: gami ica ion, sma ene gy sys ems, dis ic hea ing, cus ome
engagemen , demand-side managemen , and se ious games. These key-
wo ds we e selec ed based on hei s ong ele ance o he esea ch
objec i e, aiming o co e he key concep s o use engagemen s a e-
gies and ene gy sys ems. The e ms we e used in combina ion (using
Boolean ope a o OR) and que ies we e pe o med on majo academic
da abases, including ScienceDi ec , IEEE Xplo e, Google Schola , and
Sp inge Link. The s udies unde wen a p elimina y sc eening based on
hei i les o assess hei ele ance o he esea ch objec i es and 184
s udies we e iden i ied o u he e alua ion.
2.2. The il e ing s age
The il e ing s age e ined he selec ion by elimina ing duplica es
and applying a se o inclusion and exclusion c i e ia. This ensu ed
ha only high-quali y, ele an s udies we e e ained o analysis. The
c i e ia we e as ollows:
•Pee - e iewed con en : Only pee - e iewed a icles and book
chap e s we e included o ensu e academic igou .
•Recen de elopmen s: S udies published wi hin he las 10 yea s
we e p io i ised o ep esen he ecen de elopmen s in he ield.
•Rele ance o gami ica ion: Selec ed s udies had o add ess gami-
ica ion, se ious games, o bo h.
•Focus on ene gy sys ems and DHS: S udies we e equi ed o ha e
a p ima y ocus on ene gy sys ems, wi h pa icula emphasis on
DHS whe e possible.
Fig. 1. Me hodology o e iew including all s eps.
•Technological and beha iou al insigh s: S udies ha explo ed ei-
he echnological ad ancemen s (e.g. sma me e s, AI, machine
lea ning and ene gy managemen sys ems), beha iou al aspec s
(e.g., use engagemen , mo i a ion and beha iou change), o
hei in e sec ion we e conside ed. This c i e ion was impo an
o ensu e ha selec ed s udies con ibu ed insigh s in o bo h he
echnological po en ial and he human engagemen aspec s o
gami ica ion and se ious games in ene gy sys ems.
Applying hese c i e ia na owed he pool o 98 s udies ele an o
u he analysis.
2.3. Analysis
The selec ed s udies we e sys ema ically analysed using a s uc-
u ed empla e, gi en on Fig. 2, o ensu e consis ency. The analysis
ocused on mul iple aspec s o he s udies. Fi s , gene al in o ma ion
was ex ac ed, such as i le, au ho s, jou nal and publica ion yea .
Nex , he s udies we e ca ego ised based on hei ocus in o he s udies
including se ious games, gami ica ion o combina ion o bo h. The
ocus a ea o each s udy was iden i ied: gene al ene gy sys ems and
building au oma ion, he elec ici y sec o , o he hea ing sec o , wi h a
speci ic subca ego y o dis ic hea ing (DH) o align wi h he esea ch
objec i es. The de ailed analysis included cap u ing objec i es and
mo i a ion behind each s udy, and documen ing he me hods used o
de elop gami ica ion concep o se ious game. To add ess second and
hi d esea ch ques ion, special a en ion was paid o iden i ica ion o
elemen s and echniques employed, ield es alida ion, documen ed
bene i s, and any sma echnologies implemen ed. In addi ion o he
Ene gy 334 (2025) 137496
2
N. Abdu ahmano ic and A. Cadenbach
Fig. 2. P ocess o analysing he s udies.
s uc u ed e iew, a keywo d co-occu ence ne wo k was gene a ed
using VOS iewe [19] so wa e by ex ac ing keywo ds om he i les
and abs ac s o he selec ed s udies. Mino cleaning was pe o med
by emo ing gene al, non-speci ic e ms. A h eshold was applied o
include only keywo ds ha appea ed a leas h ee imes ac oss he
da ase . The ou comes o his keywo d analysis a e u he discussed
in Sec ion 3.4, whe e hey con ibu e o he iden i ica ion o esea ch
gaps and u u e di ec ions.
Finally, he esul s we e syn hesised o p o ide insigh s in o he
cu en s a e o gami ica ion and se ious games in ene gy sys ems, wi h
a speci ic emphasis on DH and cus ome engagemen . The esul s o
his analysis a e p esen ed in Sec ion 3 and se e as he ounda ion
o p oposed ad ancemen s in gami ica ion echniques o enhance use
engagemen and op imise DHS pe o mance.
3. Re iew esul s
This sec ion p esen s he syn hesised e iew esul s. I begins by
in oducing key e ms and hei de ini ions (Sec ion 3.1), highligh ing
bo h he o e laps and di e ences be ween gami ica ion and se ious
games. I hen examines he elemen s, echniques, and me hods used
in gami ica ion wi hin ene gy sys ems (Sec ion 3.2), ollowed by a
discussion o he measu ed impac s and bene i s (Sec ion 3.3). Las ly,
i explo es he challenges and limi a ions iden i ied in he analysed
s udies (Sec ion 3.4).
3.1. Te ms de ini ion
Gami ica ion is a b oad concep applied ac oss a ious disciplines.
This discussion ocuses speci ically on i s de ini ions as p esen ed in he
e e enced s udies. The mos commonly ci ed de ini ion [20–38] is om
De e ding e al. who desc ibe gami ica ion as ‘‘ he use o game design
elemen s in non-game con ex s’’. Se e al s udies ha e adap ed o ex-
panded his de ini ion o be e sui he con ex o ene gy sys ems. Fo
ins ance, Galli e al. [39] ex end he concep by de ining gami ica ion
as ‘‘ he use o game mechanics o enhance adi ional applica ions and
in luence use beha iou ’’, emphasising i s e ec i eness in add essing
Ene gy 334 (2025) 137496
3
N. Abdu ahmano ic and A. Cadenbach
speci ic challenges. Building on his, I ia e al. [40] de ine gami ica ion
as ‘‘ he use o game elemen s o mo i a e occupan s o adop ene gy-
e icien p ac ices’’. Beyond mo i a ion, gami ica ion should con ibu e
o o e all alue c ea ion o use s, as Lounis e al. [41] highligh , while
enabling play ul expe iences [42] and os e ing beha iou al changes,
pa icula ly in ene gy-e icien habi s. An ex ended de ini ion comes
om Chui and Wai [43], who emphasise ha ‘‘gami ica ion is no abou
c ea ing a eal game bu a he using game elemen s o enhance use
expe ience and engagemen ’’, unde sco ing i s ole in sus aining use
in e es and pa icipa ion. In addi ion o mo i a ion and beha iou
change, gami ica ion plays a c i ical educa ional ole. Mo on e al. [44]
no e ha gami ica ion seeks o be bo h en e aining and educa ional,
helping use s unde s and bo h hei choices and hei consequences.
Syn hesising hese pe spec i es, gami ica ion can be de ined as:
The use o game elemen s and design, implemen ed h ough web pla -
o ms, applica ions, and mobile de ices, o communica e wi h use s, mo i-
a e engagemen , and educa e hem in adop ing beha iou s and habi s ha
p omo e ene gy e iciency.
In con as , se ious games we e less ep esen ed in he s udies
and a e de ined as digi al games wi h educa ional o aining objec-
i e [21]. Wee and Choong [45] s a e ha hey can be seen as any o m
o in e ac i e compu e -based game so wa e wi h in en ion o be mo e
ha en e ainmen . O he s claim ha se ious games a e used o educa-
ional pu poses and can p omp beha iou al change [46] o ha hey
ha e ‘‘explici ly educa ional objec i e’’ [47]. A mo e de ailed de ini ion
by Huseynli [48] desc ibes se ious games as i ual ec ea ions o eal-
wo ld scena ios ha ‘‘aim o change human beha iou (o a i udes and
cogni ions) ia he use o in insically compelling elemen s ound in
well-made digi al games’’. Conside ing hese de ini ions, se ious games
a e de ined as:
Games simula ing eal ene gy sys ems and ac i i ies ha educa e, en e -
ain, and mo i a e beha iou change while ensu ing a balance be ween hese
aspec s, enabling use s o e ec i ely explo e ene gy- ela ed challenges.
Wi h bo h e ms de ined and hei cha ac e is ics cla i ied, i can be
concluded ha gami ica ion and se ious games sha e a common goal:
mo i a ing and in luencing eal-wo ld beha iou . Bo h app oaches also
pu sue educa ional objec i es by engaging use s in lea ning p ocesses
ha p omo e beha iou al change.
The key di e ence is in hei implemen a ion and scope. Se ious
games a e ully de eloped games designed wi h an educa ional, ain-
ing, o mo i a ional objec i e. They o e in e ac i e expe iences ha
o en simula e eal-wo ld scena ios o achie e hei goals. In con as ,
gami ica ion is no a game i sel bu a he he in eg a ion o game
design elemen s in o non-game con ex s o enhance engagemen and
d i e speci ic beha iou s. While se ious games c ea e a comple e game
expe ience, gami ica ion applies only selec ed game-inspi ed s a egies
o make exis ing applica ions o sys ems mo e engaging and e ec i e.
They can be used indi idually o in combina ion o enhance ene gy
sys ems.
3.2. Elemen s and echniques
Since p e iously published e iews, such as [29,32,33,36,43,49–53]
ha e ex ensi ely co e ed gami ica ion and se ious games elemen s,
echniques, and case s udies, his sec ion p o ides only a b ie o e iew
and a isual ep esen a ion o subca ego ies (see Fig. 3). This inclusion
aims o enhance he pape ’s cohe ence wi hou ei e a ing con en ha
has al eady been ho oughly add essed, while placing emphasis on he
no el y o his esea ch, which is p esen ed la e (Sec ion 4).
Di e en challenges and ques s wi hin a gami ied sys em o in e -
ace can yield a ying poin alues, allowing o a weigh ed sys em
whe e mo e signi ican ac ions ea n mo e poin s [43]. This s uc u e
p o ides ins an eedback, mo i a ing use s o engage mo e ac i ely
wi h he sys em. Poin s can hen ansla e in o badges, which can be
ea ned and upg aded, os e ing a sense o p og ession and achie e-
men [23,52]. Simila ly, leade boa ds in oduce a compe i i e elemen ,
encou aging use s o imp o e hei sco es and achie e highe ank-
ings [43]. Highe sco es a e o en he esul o pe sonalised challenges
which may lead o ewa ds and incen i es. These can ake a ious
o ms, including mone a y ewa ds, non-mone a y gi s, and social
ecogni ion, all designed o encou age speci ic beha iou s wi hin a
communi y o sys em [54]. While compe i i eness can d i e use s o
ea n mo e ewa ds, os e ing social in e ac ion and collabo a ion plays
an equally impo an ole. This app oach builds a sense o communi y
and sha ed esponsibili y, leading o highe mo i a ion [55]. In addi-
ion o connec ing wi h o he s, gami ica ion bene i s om connec ing
use s o he eal wo ld. This is achie ed h ough s o y elling, which
c ea es engaging na a i es ha mi o eal-li e si ua ions and enhance
use in ol emen [56].
3.3. Impac and bene i s o gami ica ion and se ious games
The p e ious subsec ion (Sec ion 3.2 pa ially add essed he i s
esea ch ques ion by de ining gami ica ion and se ious games and
in oducing hei elemen s and echniques. Howe e , he second pa o
he ques ion: ‘‘how do hese app oaches in luence ene gy consump ion
pa e ns and use beha iou ?’’ emains o be explo ed. This sec ion
b idges he gap by syn hesising li e a u e indings on he impac s and
bene i s o gami ica ion and se ious games.
Measu ing beha iou al change and use sa is ac ion in he con-
ex o gami ica ion and se ious games is challenging, bu a ious
app oaches ha e been de eloped o assess hese ou comes. One widely
used me hod is he implemen a ion o ques ionnai es o collec use
eedback. Nas ollahi e al. [32] iden i y ques ionnai es as he mos
used ool o e alua ing use beha iou and sa is ac ion. Mylonas e al.
[37] implemen ed his app oach and demons a ed posi i e ou comes,
including enhanced use sa is ac ion and engagemen , indica ing ha
gami ica ion can e ec i ely cap u e use in e es . Fu he e idence
comes om Cas i e al. [38], who used p e- and pos - es su eys o
assess beha iou al changes. Thei esul s show ha bo h compe i i e
and collabo a i e games success ully p omo ed use engagemen and
beha iou change, al hough he di e ences be ween hese app oaches
may be sligh . Pane u and Ta igan [57] highligh he e ec i eness
o ene gy applica ions in os e ing use engagemen and beha iou
change. Howe e , hey no ed ha hese esul s we e de i ed om
small-scale es ing and ecommended la ge s udies o con i m b oade
applicabili y. In public buildings, gami ica ion has also shown signi -
ican bene i s. Ko sopoulos e al. [28] demons a ed ha employees
no only ac i ely engage wi h gami ied sys ems bu also de elop a
heigh ened awa eness o he impo ance o ene gy conse ing. This
p o es he ele ance o gami ica ion in wo kplace en i onmen s whe e
collabo a i e e o s can signi ican ly con ibu e o ene gy-sa ing goals.
In addi ion o p omo ing engagemen , gami ica ion p o ides edu-
ca ional bene i s. Fo ins ance, Peham e al. [35] e alua ed he educa-
ional impac o games, no ing ha he design and implemen a ion o
gami ied sys ems a e hea ily dependen on he cha ac e is ics and p e -
e ences o he a ge g oup. This sugges s ha success ul gami ica ion
equi es a use -can e ed app oach, whe e he p io i ies and needs o
he use s a e conside ed du ing he design phase o ensu e sa is ac ion
and sus ained engagemen .
O e all, he li e a u e indica es ha gami ica ion and se ious games
a e e ec i e in mo i a ing and engaging di e se use g oups, including
employees [49,58], s uden s [37] and domes ic building esiden s [59].
Howe e , while hese app oaches a e success ul in os e ing engage-
men and mo i a ing beha iou change, quan i ying hese changes
emains a complex challenge. As no ed in [58], use decision-making
p ocesses a e mul i ac o ial and con ex -dependen , making i di icul
o a ibu e beha iou al changes solely o gami ica ion in e en ions.
Al hough di ec quan i ica ion o beha iou al change and use en-
gagemen may be challenging, he impac o gami ica ion on ene gy
e iciency and ene gy sa ings is mo e measu able. To add ess his,
Table 1 summa ises quan i ied imp o emen s epo ed in he e iewed
s udies, ca ego ised in o wo g oups:
Ene gy 334 (2025) 137496
4
N. Abdu ahmano ic and A. Cadenbach
Fig. 3. Gami ica ion and se ious games elemen s: ca ego ies and subca ego ies.
Fig. 4. Bene i s measu ed in e ms o ene gy usage educ ion, ene gy sa ings and peak sha ing: mo e de ails in Table 1.
•Re iews ha p esen gene al indings on use engagemen and
ene gy sa ings.
•Indi idual s udies ha de ail speci ic measu ed bene i s, p o id-
ing con ex o he associa ed gami ica ion in e en ions.
The measu able bene i s o gami ica ion and se ious games in en-
e gy sys ems clea ly demons a e hei po en ial as impac ul ools o
ene gy sa ings and beha iou al change. Howe e he epo ed ange o
ene gy sa ings, om 4% o 44% (see Fig. 4), e lec s subs an ial a i-
a ion in how gami ica ion and se ious games a e implemen ed ac oss
di e en s udies. These in e en ions di e no only in con ex ( esiden-
ial households, o ice en i onmen s, and educa ional se ings) bu also
in hei echnological in eg a ion and in ensi y. Column ‘‘Me hodolo-
gies and in e en ions’’ in Table 1 sugges s ha while ce ain elemen s,
such as eedback, ewa ds, social compa ison, and pe sonalisa ion, ap-
pea ac oss mul iple s udies, mos in e en ions ely on a combina ion
o echniques a he han a single mechanism. As a esul , i is no
possible o di ec ly associa e ene gy sa ings wi h any one gami ica ion
elemen . Howe e , he epea ed p esence o hese elemen s sugges s
ha hei combina ion, a he han any indi idual componen , con-
ibu es o e ec i eness. Fu he mo e, highe sa ings a e epo ed in
s udies ha combine gami ica ion s a egies wi h eal- ime eedback
sys ems, in elligen ene gy con ol and managemen pla o m. In con-
as , s udies using mo e basic app oaches, such as s a ic eedback o
s and-alone gami ied elemen s, end o show mo e modes esul s.
This a iabili y sugges s ha while gami ica ion and se ious games
a e p omising s a egies, hei e ec i eness is no uni e sal and e-
qui es ca e ul ailo ing o he in ended audience and con ex . This
conclusion leads di ec ly o Sec ion 3.4 whe e he limi a ions and
challenges associa ed wi h gami ica ion and se ious games will be
explo ed.
3.4. Limi a ions o exis ing s udies and esea ch gap iden i ica ion
One o he p ima y challenges o gami ica ion and se ious games
is sus aining use mo i a ion and engagemen o e ime. Mul iple
s udies [49,58,59] highligh ha while ini ial pa icipa ion a es may
be high, many use s end o disengage o e ime due o epe i i e
asks ha lead o bo edom. Fu he mo e, gami ica ion elemen s do no
esona e equally wi h all use s [38], emphasising he need o adap i e
s a egies ailo ed o indi idual p e e ences.
Pe sonalisa ion emains a signi ican obs acle, as di e en g oups
o use s show a ied p e e ences o gami ica ion elemen s and app
unc ionali ies [35]. This ep esen s a challenge due o he limi ed
unde s anding o how o ailo pe suasi e echnologies o di e se use
p o iles, as explained by Böckle e al. [42]. Fac o s such as age and
use ype can signi ican ly a ec how indi iduals engage wi h gami ied
sys ems. Wi hou a pe sonalised app oach, many in e en ions lack
Ene gy 334 (2025) 137496
5
N. Abdu ahmano ic and A. Cadenbach
Table 1
Summa y o s udies on gami ica ion and se ious games and hei impac .
Ca ego y Re e ence S udy desc ip ion Desc ibed bene i Me hodologies and
in e en ions
Re iews
Nas ollahi e al.
[32]
Re iew on se ious
games in ene gy
sys ems.
Measu ed ene gy sa ings ypically ange
om 4% o 12%, wi h peak sa ings
occasionally exceeding 20%.
In-home ene gy displays —
4%–12% sa ings, pe sonalised
measu es up o 12%, sma
he mos a s — up o 17%
Paone and Bache
[58]
Re iew on
eco- eedback, social
in e ac ion, and
gami ica ion impac on
building occupan
beha iou .
Ene gy sa ings can each up o 55%
wi h eco- eedback, while beha iou al
impac s can sa e 62%–86% in speci ic
dwellings.
Eco- eedback — up o 17%,
social in e ac ion — up o 55.
%
Deleme e and Lis on
[60]
Re iew on se ious
games in ene gy
sys ems.
A e age educ ions o 15.2% in
esiden ial se ings, 18.4% in comme cial
en i onmen s, and 9.9% in educa ional
con ex s.
Se ious games and
gami ica ion (gene al, me hods
no speci ied).
Iweka e al. [51] Re iew on in e en ions
such as eedback,
economic incen i es,
communi y-based
in e en ions, and
gami ica ion.
Social in e en ions ha e epo ed
ene gy sa ings anging om 0.4% o
24.5%, while communi y-wide ini ia i es
ha e achie ed ca bon oo p in
educ ions o 17% o 27%. Gami ica ion
e o s ha e esul ed in a e age ene gy
sa ings o 4% o 24%, and goal-se ing
app oaches ha e demons a ed an
a e age ene gy sa ings o 20.7%.
Feedback — up o 24.5%,
goal se ing — up o 20.7%.
Gami ica ion,
se ious games,
IoT pla o ms
Zehi e al. [24] Gami ica ion app oach
o demand
managemen in
elec ici y sec o .
Mon hly consump ion di e ences
eached up o 20%, wi h peak pe iod
consump ion di e ences eaching as
high as 16%. On a e age, peak
consump ion a ia ions we e below 8%.
Goal se ings, poin s,
leade boa ds, eams, mon hly
epo .
Casals e al. [46] Se ious game o educe
o e all ene gy
consump ion.
A e age elec ici y sa ing was 3.46%
while a e age gas sa ing was 7.4%.
S o y elling, poin s, challenges
bonus ewa ds ( i ual).
Xu e al. [59] Feedback amewo k
wi h ocus on
elec ici y consump ion
educ ion, es ed in
Singapo e.
Achie ed home ene gy educ ion o
8.18%, wi h ene gy sa ing goal o 5%,
and 12.56% wi h he ene gy sa ing goal
o 10%.
Tailo ed in o ma ional
eedback, goal-se ing,
mone a y ewa ds.
Gomes e al. [61] ICT gami ica ion
app oach applied in
o ice buildings in
Lisbon.
O e all ene gy sa ings a ied by
loca ion, wi h he lib a y achie ing a
42% educ ion, o ices eco ding a 12%
educ ion, and Amphi hea e ealising a
3.5% educ ion.
Real- ime in o ma ion sys ems,
in elligen ene gy
managemen , eedback,
in eg a ed ech epo .
Van De Neu e al.
[62]
Gami ica ion concep o
educe ene gy
consump ion in
households.
Ene gy sa ings ange om 4% o 24%. Use eedback, pe sonalisa ion,
compa ison, challenges,
anking.
Sin o e al. [63] Gami ica ion app oach
wi h ocus on
elec ici y consump ion
educ ion.
S udy showed 14% ene gy sa ings wi h
disagg ega ed eedback.
Poin s, compe i ion, anking,
push no i ica ions.
Gami ica ion,
se ious games,
IoT pla o ms
Gangolells e al.
[64]
Ene gy game
ene GAwa e es ed in
Uni ed Kingdom.
The game has he po en ial o sa e o e
48.9 TWh annually, con ibu ing
app oxima ely 8% owa d emissions
educ ion a ge s, wi h es ima ed yea ly
sa ings o 0.009 GWh and 4 ons o
CO2.
Challenge, educa ional
con en , na a i e, eedback,
social sha ing.
Méndez e al. [65] Mul iple app oaches
(gami ica ion, con ol
o he mos a s, and
eedback) in es iga ed
in a s udy in Mexico.
Gami ica ion s a egies can educe
ene gy by 22% while connec ed
he mos a s educe peak load by 10% o
35%. Feedback app oaches can educe
ene gy by 5% o 12%.
Rewa ds, in e ac i e in e ace,
compe i ion, communi y
engagemen , sma
echnologies ( he mos a s).
Ca ei a e al. [66] ICT-based engagemen
o use s.
S udy indica ed a 5.81% ene gy usage
dec ease.
Feedback no i ica ions
Kons an akopoulos
e al. [67]
Gami ica ion amewo k
o elec ici y usage
educ ion in esiden ial
do m buildings.
Ceiling ligh usage dec eased by 23.6%
while desk ligh usage educed by
60.8% on weekdays. Ceiling an usage
dec eased by 19.0% on weekdays.
Rewa ds, poin s, eedback,
lo e y mechanisms, gi ca ds.
he abili y o c ea e meaning ul and las ing impac s. Addi ionally,
p o iding clea and pe sonalised eedback is essen ial [68] bu his
has been shown o be challenging [61]. E ec i e gami ied sys ems
mus o e imely, goal-o ien ed eedback o guide use s owa d desi ed
beha iou s.
On he echnical side, high implemen a ion cos s and ins alla ion
equi emen s o IoT sys ems and o he da a collec ion me hods p esen
ba ie s o widesp ead adop ion [69]. While da a collec ion is in alu-
able o gami ica ion, i also aises p i acy and secu i y conce ns,
u he complica ing he implemen a ion o gami ied sys ems [53].
Ene gy 334 (2025) 137496
6
N. Abdu ahmano ic and A. Cadenbach
Fig. 5. Keywo ds om s udies connec ed o gami ica ion and se ious games in ene gy sys ems: elec ici y sec o ocus isible.
Add essing hese conce ns equi es obus solu ions o build us and
encou age pa icipa ion.
Ano he ela i ely unexplo ed a ea is he applica ion o gami ica ion
in demand esponse p og ams [39]. Since demand esponse p og ams
a e o en connec ed o dynamic a i s and incen i es, a ques ion a ises:
how can gami ica ion be e ec i ely in eg a ed, gi en ha s udies [59]
show mone a y ewa ds o en ha e limi ed e ec i eness? Many use s
s uggle o link in-game ac ions o eal-li e beha iou s [46], and he
ebound o d awback e ec s can lead o a e e sion o old habi s. To
o e come hese challenges, sys ems mus p io i ise in insic mo i a ion
o e ex insic ewa ds o achie e las ing change. E en when hese
challenges a e add essed, measu ing he e ec i eness o gami ica ion
emains a c i ical issue. Quan i ying he impac o use beha iou on
ou comes such as ene gy e iciency is o en di icul , and he e is a
lack o long- e m s udies o alida e he sus ained bene i s o hese
sys ems [69].
In addi ion o hese limi a ions, a signi ican esea ch gap exis s in
he con ex o DHS. The conduc ed s udies a e p ima ily ocused on
he elec ici y sec o o ene gy sys ems in gene al, as illus a ed by he
isualisa ion o a ailable li e a u e based on he keywo ds (see Fig. 5
c ea ed wi h VOSViewe [19]). In his e iew, and o he bes o he
au ho s’ knowledge, no gami ica ion app oach has been iden i ied ha
analyses he impac o gami ica ion on DHS as a whole, wi h only one
s udy on se ious games in his a ea a ailable [18].
These limi a ions unde sco e he need o hough ul design and
con inuous adap a ion in gami ica ion and se ious games o maximise
hei e ec i eness wi h a special ocus on DHS.
This sec ion highligh s he need o adap i e s a egies, pe sonal-
isa ion, e ec i e eedback mechanisms, demand esponse in eg a ion
wi h dynamic a i s, and solu ions o secu i y and da a p i acy issues.
Fu he mo e, i poin s o he necessi y o in es iga ing he applica ion
o gami ica ion in DHS. The e o e, he nex sec ion o e s p oposals o
imp o emen alongside applica ion possibili ies o DHS.
4. Gami ica ion (and) op imisa ion in DHS
The iden i ied bene i s o gami ica ion in ene gy sys ems, pa icu-
la ly in DHS, can be enhanced by using sma echnologies such as
machine lea ning (ML) and a i icial in elligence (AI) [70–74]. Table 2
summa ises examples o how AI and ML ha e been applied in p e ious
ene gy- ela ed s udies.
Al hough he e iewed s udies apply AI and ML echnologies in
ene gy sys ems, hey do no explici ly quan i y he di ec impac s o
hese implemen a ions. Ne e heless, gi en ha hese echnologies can
bo h ampli y exis ing bene i s and add ess challenges associa ed wi h
he implemen a ion o gami ica ion in ene gy sys ems, u he esea ch
is necessa y o explo e hei po en ial, pa icula ly in he con ex o
DHS. Howe e , he e ms AI and ML a e e y b oad, so his sec ion
will be s uc u ed in wo pa s:
•De ining key e ms, hei connec ion o DHS, and in oducing
me hods wi hin AI and ML (Sec ion 4.1).
•S a egies o implemen ing and op imising gami ica ion in DHS
using AI and ML (Sec ion 4.2), ca ego ised by ocus a eas.
–Technological ocus
–Economic ocus
–Beha iou al o psychological ocus
–Educa ional and accep ance ocus
4.1. Te ms de ini ion
AI e e s o sys ems ha display in elligen beha iou by analysing
hei en i onmen and aking ac ions o achie e speci ic goals [75].
This enables machines o pe o m asks such as lea ning, p oblem
sol ing and decision making. In he con ex o DHS, AI can p ocess and
analyse la ge amoun s o da a o op imise gami ica ion s a egies and
imp o e sys em e iciency.
ML is a subse o AI ocused on algo i hms and s a is ical models
ha enable machines o lea n om da a and imp o e hei pe o mance
o e ime. Fo example, in gami ica ion o DHS, ML can be used o
p edic i e analy ics based on his o ical and eal- ime da a.
The subca ego ies which could be used a e:
•Adap i e lea ning sys ems use AI o modi y hei beha iou and
ou pu s based on new da a o e ime [76].
•Fede a ed lea ning is a p i acy-p ese ing ML app oach whe e
model lea ns om decen alised da a s o ed on local de ices
wi hou ans e ing i o a cen al se e [77].
Ene gy 334 (2025) 137496
7
N. Abdu ahmano ic and A. Cadenbach
Table 2
AI and ML implemen a ion in e iewed s udies.
Re e ence AI and ML implemen a ion
A ila e al. [27] The s udy uses an adap i e neu o- uzzy in e ence sys em (ANFIS) o classi y elec ici y consump ion
le els and ailo gami ica ion s a egies by p o iding pe sonalised use eedback. While he app oach
is sugges ed o be e ec i e, no measu able impac is epo ed.
Méndez e al. [65] Two a i icial neu al ne wo ks a e used o link household consump ion da a and pe sonali y ai s
wi h gami ied elemen s. An AI-based decision sys em adap s in e ace design and gami ica ion
s a egies o use p o iles. No measu able impac is epo ed.
Sin o e al. [63] AI echniques o anomaly de ec ion and use beha iou analysis enable pe sonalised appliance
moni o ing, eal- ime eedback, and gami ied use p omp s h ough a mobile app. The au ho s sugges
ha his amewo k could be expanded o o he domains. Table 1 shows ha he e a e 14% ene gy
sa ing es ima ed in his s udy, howe e i is no explici ly s a ed how much impac comes om AI
echniques.
Pane u and Ta igan [57], The s udy discusses he in eg a ion o AI and ML in sma ene gy apps o lea n household
consump ion pa e ns and deli e pe sonalised eedback aimed a in luencing use beha iou . While
he po en ial o beha iou change is acknowledged, he s udy emphasises he need o u he
esea ch o e alua e he ac ual e ec i eness o hese echnologies.
•Anomaly de ec ion in ol es using AI o iden i y unusual pa e ns
in da a ha de i a e om expec ed beha iou [78].
•Beha iou al modelling e e s o using ML algo i hms o unde -
s and and p edic use beha iou based on pas ac ions.
•Clus e ing is an ML echnique ha g oups use s based on sha ed
cha ac e is ic o beha iou s [79].
•Rein o cemen lea ning is he p oblem aced by an agen ha
mus lea n beha iou h ough ial-and-e o in e ac ions wi h a
dynamic en i onmen [80].
•P edic i e analy ics in ol es using AI and ML o o ecas u u e
e en s o beha iou s based on his o ical and eal- ime da a.
4.2. S a egies o implemen ing and op imising gami ica ion in DHS
4.2.1. Technological ocus
Technological ocus emphasises he in eg a ion o AI and ML wi h
gami ica ion o op imise sys em pe o mance. These echnologies ac
as he backbone o gami ica ion, enabling ene gy-e icien ope a ions
o DHS.
P edic i e demand esponse op imisa ion: Demand side managemen
has shown o be a success ul echnique o peak sha ing, educ ion
o p ima y ene gy needs, emissions and cos educ ion [81–84]. To
ensu e e ec i e demand esponse mechanisms, his o ical da a, wea he
o ecas s, and cu en usage pa e ns mus be analysed o p edic peak
demand pe iods in DHS. In his p ocess, a signi ican amoun o da a is
p ocessed, and AI can op imise his p ocess. A he same ime, gami ied
incen i es, such as poin s, can encou age use s o shi hei hea ing
usage du ing high-demand pe iods. This means ha AI can p edic peak
pe iods while also gene a ing pa e ns o gami ica ion, mo i a ing
use s o pa icipa e in demand educ ion e o s.
P edic i e main enance and sys em op imisa ion: A signi ican chal-
lenge aced by ene gy sys ems, pa icula ly DHS, is he ime equi ed
o iden i y and esol e ailu es and aul s [85]. In his case AI can
be employed o ack he pe o mance o hea ing equipmen ac oss
he sys ems, enabling p edic ions on when main enance migh be e-
qui ed [86]. A he same ime, use s can epo issues wi h hea ing
sys ems o de ices and gami ica ion can encou age hem by ewa ding
poin s o incen i es o p oac i e epo ing. Wi h AI-d i en p edic i e
main enance, which includes gami ica ion, sys em down ime can be
minimised, and eliable hea ing supply can be ensu ed. Use s bene i
bo h om incen i es bu also om secu e and eliable sys em.
Ene gy e iciency inc ease: The esul s show ha gami ica ion can
signi ican ly in luence on ene gy e iciency and esul s in inc easing i .
Howe e , his is shown o be sho e m, a iable and o cause use -
a igue o e some ime. To u he enhance i , AI can be used o analyse
da a om sma me e s and IoT de ices and o p o ide use s wi h
pe sonalised insigh s. on how o imp o e ene gy e iciency can help he
sys em. These insigh s, deli e ed h ough gami ied ecommenda ions,
o e ac ionable s eps o use s, while simul aneously imp o ing o e all
sys em pe o mance.
Secu e and p i acy-p ese ing da a analy ics: Sma me e s and IoT a e
o g ea impo ance o sys ems because hey enable DHS o become
p oac i e and in eg al pa o he ene gy sec o and could acili a e
modelling and o ecas ing [87]. O e all, da a collec ion and analy ics
suppo digi alisa ion p ocess and can help wi h many di e en as-
pec s o ene gy sys ems. This is also impo an o gami ica ion, since
consump ion pa e ns analysis and incen i es adjus men a e based on
collec ed da a. Howe e , an issue ha a ises he e is secu e and p i acy-
p ese ing da a analysis. The e o e, ML can use p i acy p ese ing
echniques, such as ede a ed lea ning, o analyse da a locally on use
de ices. Only agg ega ed insigh s a e sha ed, p o ec ing indi idual use
da a. Fu he mo e, gami ied elemen s can be used o ensu e ans-
pa ency by showing use s how hei da a is used and allowing hem
o op in o speci ic challenges.
4.2.2. Economic ocus
AI and ML, combined wi h gami ica ion can ha e signi ican eco-
nomic bene i and his can be seen om bo h om supplie ’s (demand
managemen and enewable ene gy inco po a ion) and consume ’s (in-
cen i es and eedback) pe spec i e.
Dynamic p icing models: Dynamic p icing models a e commonly
used in elec ici y sec o o demand shi ing. They ha e p o en e -
ec i e in incen i ising he in eg a ion o a iable enewable ene gy
and peak consump ion educ ion.Howe e , dynamic p icing is slowly
e ol ing in DHS [88] bu could be equally bene icial o 4 h gene a ion
sec o -coupled DHS. They allow p ice adjus men s based on enewable
hea a ailabili y and peak demand. AI-based demand p edic ion can
op imise his by dynamically adjus ing hea ing p ices in eal- ime. Com-
bined wi h gami ica ion, use s a e incen i ised o shi consump ion,
wi h AI calcula ing ewa ds based on sa ings o usage iming. This
enables lexible, use - esponsi e p icing s a egies.
Real- ime eedback and immedia e incen i es: The challenges o sho -
e m e ec s and use a igue iden i ied in he p e ious sec ion can
be add essed wi h immedia e incen i es [89]. This way consume is
ac i ely engaged and supplie has be e o e iew o incen i es dis i-
bu ion. To maximise he bene i o such mechanisms, AI-d i en da a
analy ics can p o ide eal- ime eedback and ewa ds. Fo example, i
a use educes hea ing du ing peak imes, hey ecei e ins an eedback
showing hei con ibu ion o sys em e iciency and an immedia e
ewa d.
4.2.3. Beha iou al o psychological ocus
This ca ego y emphasises he possibili ies o combining sma ech-
nologies wi h gami ica ion o os e ing use pa icipa ion and encou -
aging sus ainable beha iou in DHS.
Pe sonalised challenges and ewa ds: Toge he wi h eal- ime eed-
back, one mo e app oach o keep use s mo i a ed o e longe pe iods
Ene gy 334 (2025) 137496
8
N. Abdu ahmano ic and A. Cadenbach
o ime is o p o ide pe sonalised challenges and ewa ds [90]. This
means ha , based on use s’ hea ing pa e ns, p e e ences, and pas
beha iou , AI can gene a e and sugges pe sonalised goals, such as
educing hea ing use by 10% du ing peak hou s o main aining a
speci ic empe a u e ange. Addi ionally, ewa ds can be ailo ed based
on use s’ eac ions o p e ious incen i es, ensu ing sa is ac ion upon
ask comple ion.
Dynamic adap a ion and di icul y adjus men : ML can be used o ack
use and comple ion a e o challenges. Based on his da a, he di icul y
o challenges and equency o ewa ds can be dynamically adjus ed
o main ain engagemen wi hou o e whelming use s. By adap ing o
use beha iou , a igue is p e en ed, and mo i a ion can be main ained,
ensu ing long- e m pa icipa ion in he gami ica ion p og am.
Communi y-based gami ica ion and social compa ison: The esul s o
e iew show he impo ance o communi y collabo a ion and social
compa ison in gami ica ion. AI can u he enhance his by g ouping
use s based on speci ic pa ame e s o sha ed in e es s, and encou aging
pa icipa ion in g oup challenges o boos engagemen .
Seasonal and e en -based gami ica ion and engagemen balancing: One
mo e measu e o ackle he issue o a igue and loss o mo i a ion is
o use AI o use a igue moni o ing. This is possible by analysing
engagemen da a such as educed pa icipa ion o inac i i y. Based
on hese insigh s, AI can educe he equency o challenges, o e
smalle goals o in oduce new elemen s. AI analyses seasonal pa e ns
in hea ing use o c ea e imely challenges and e en s (e.g. a ‘‘Win e
Sa ings Challenge’’ o educe hea ing usage du ing win e mon hs. By
moni o ing a igue and o e ing addi ional elemen s i is possible o
c ea e enewed in e es in use s.
All echniques ackling beha iou al aspec can u he be imp o ed
by inco po a ing use segmen a ion. This means ha ML o AI could
clus e use s in o segmen s based on hei hea ing beha iou and p e -
e ences (e.g. high-ene gy use s, ene gy sa e s) and se speci ic gami ied
expe iences o each g oup, such as mo e challenging goals o high-
ene gy use s and educa ional ips o ene gy sa e s. Such a ge ed
gami ica ion inc eases ele ance, making i mo e e ec i e o each
segmen and p omo ing beha iou al change ac oss di e se use ypes.
4.2.4. Educa ional and accep ance ocus
Gami ica ion has been shown as success ul ool in enhancing ene gy
li e acy and accep ance (discussed in Sec ion 3.3). Howe e , i can be
u he imp o ed by using sma echnologies.
Educa ional con en and gami ied lea ning modules: Educa ional mod-
ules can be cus omised using AI based on use knowledge le el and
beha iou pa e ns. Inc eased ene gy e iciency can esul om edu-
ca ing use s abou i s impo ance, such as lessons on e icien hea ing
use, p esen ed as mini-games o quizzes. Rewa ds o comple ing hese
modules can be p o ided, encou aging use s o engage wi h educa ional
con en .
Enhanced decision-making o sys em ope a o s: Using gami ica ion is
o signi ican bene i o he sys em ope a o s since i enables be e
ope a ional schedule, imp o ed ene gy e iciency and be e sa is ied
cus ome s. Wi h AI his can be u he imp o ed since sys em ope a o s
a e p o ided wi h eal- ime insigh s in o use beha iou and demand
pa e ns, allowing hem o make da a-d i en decisions on hea ing
dis ibu ion. Gami ica ion can also ex end o ope a o s, ewa ding hem
o achie ing e iciency goals ac oss he DH
Rega dless o ocus, AI-powe ed eedback loops a e a signi ican
ad an age. ML algo i hms con inuously analyse use in e ac ion da a
and sys em pe o mance, e ining gami ica ion s a egies dynamically.
Fu he mo e, i is impo an o poin ou ha he e is some o e -
lapping be ween he ca ego ies and me hods and ha i is possible
o achie e maximum esul wi h hei combina ion. Fig. 6 isually
ep esen s an example o such combina ion.
This chap e has so a shown ha AI and ML combined wi h
gami ica ion could lead o signi ican bene i s o di e en aspec s o
he sys em. Howe e , an impo an ques ion is how hese can be
implemen ed in exis ing sys ems whe e no sma me e s a e a ailable
and such da a collec ion is no aking place.
While inco po a ing such de ices is ecommended o digi alisa ion
and sec o coupling [87], al e na i e app oaches a e also possible wi h
he help o use s. By engaging use s o epo hei hea ing beha iou s
o ene gy-sa ing ac ions h ough gami ied apps, da a can be p o ided
o AI o agg ega e and analyse pa e ns and gene a e necessa y gami i-
ca ion app oaches. Fu he mo e, apps can be da a collec o s o indi ec
senso s, since hey can use geoloca ion, wea he da a and manual
inpu s om use s.
In he absence o eal- ime da a, ML algo i hms can c ea e simu-
la ion models o he hea ing sys em which can p edic hea ing usage
pa e ns o di e en buildings o use g oups. This enables i ual
gami ica ion whe e use s in e ac wi h modelled da a, bu also can
lea n mo e abou sys em and echnical p ope ies which can esul
in accep ance inc ease. Use s can also be mo i a ed and gami ied by
AI-p o ided challenges based on gene al sys em pe o mance, a he
han indi idual usage da a. Though he impac o AI and ML is limi ed
wi hou sma echnologies, i emains iable. G adual in eg a ion o
sma me e s and IoT can p og essi ely op imise gami ica ion in DHS.
Technical limi a ions and conside a ions: The s a egies ou lined abo e
demons a e ha AI and ML echnologies can signi ican ly enhance
gami ica ion wi hin DHS. Th ough adap i e lea ning, dynamic eed-
back, use segmen a ion, and p edic i e analy ics, hey enable mo e
a ge ed, esponsi e, and con ex -awa e s a egies ha boos sys em
e iciency and use sa is ac ion. They also suppo au oma ed chal-
lenge pe sonalisa ion, anomaly de ec ion, and eal- ime alignmen o
incen i es wi h sys em-wide goals.
Howe e , he in eg a ion o hese echnologies is no wi hou ech-
nical and implemen a ion challenges. Real- ime p ocessing o da a
om sma me e s and IoT de ices can be compu a ionally in ensi e,
especially in la ge-scale sys ems wi h high da a olume. This equi es
adequa e digi al in as uc u e and may no be easible in all exis ing
DHS se ups. Addi ionally, he pe o mance o ML models depends hea -
ily on he quali y, equency, and consis ency o inpu da a. Missing
alues, measu emen e o s, o delays can signi ican ly educe model
eliabili y and p oduce misleading eedback o use s. La ency issues
can u he limi he immediacy and ele ance o gami ied esponses
which is an essen ial componen o main aining use engagemen . Sim-
ila echnical limi a ions ha e been iden i ied in eal- ime edge AI o
IoT de ices [91]. The compu a ional demands o ad anced models also
lead o highe ene gy use, which may con lic wi h sys em-wide e i-
ciency goals. Finally, main aining up- o-da e models o e ime equi es
con inuous e- aining and moni o ing, adding long- e m ope a ional
bu dens.
Beyond hese echnical aspec s, sma echnologies can also unin en-
ionally ampli y exis ing issues. Fo example, mo e complex in e aces
and adap i e sys ems may exclude less digi ally li e a e use s o hose
wi hou access o sma de ices, ein o cing digi al inequali ies. The e
is also a isk o o e -dependence on high- esolu ion da a and au oma ed
decision-making, which can educe anspa ency and use con ol.
Thus, while AI and ML hold s ong po en ial o enhance DHS
gami ica ion, hei deploymen mus be ca e ully planned. Solu ions
should balance sophis ica ion wi h usabili y, ensu ing accessibili y,
anspa ency, and p i acy. Fu he esea ch is needed o examine how
AI-suppo ed gami ica ion can be e ec i ely implemen ed ac oss a -
ied DHS con ex s, pa icula ly ega ding da a quali y, scalabili y, and
inclusi eness.
5. Conclusions and ou look
This pape has explo ed he po en ial o gami ica ion and se ious
games h ough a s uc u ed li e a u e e iew, syn hesising insigh s om
a ious s udies and highligh ing measu able bene i s, limi a ions, and
u u e oppo uni ies. By de ining gami ica ion and se ious games and
compa ing hei applica ions, i is e iden ha bo h sha e a common
Ene gy 334 (2025) 137496
9
