Enhancing energy efficiency through user engagement and behaviour change: A review on gamification approaches and serious games in energy systems

Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy Enhancing energy efficiency through user engagement and behaviour change: A review on gamification approaches and serious games in energy systems Nermina Abdurahmanovic ∗ , Anna Cadenbach Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Germany A R T I C L E I N F O Keywords: Gamification District heating systems User engagement Behaviour change Energy efficiency A B S T R A C T Purely technical solutions are insufficient to optimise district heating systems (DHS) and end-user behaviour significantly impacts the system and its performance. Thus, prioritising customer engagement strategies and fostering cooperation with consumers are crucial for realising the full potential of DHS. This paper explores the potential of gamification as an effective method for engaging end-users. While gamification has demonstrated success in various fields, its potential in DHS requires further investigation. To address this, the paper conducts a comprehensive literature review on gamification, focusing on its application in smart energy systems and DHS. The study examines various gamification elements and techniques, analysing case studies and examples of gamification implementation in energy systems. It evaluates the potential impact and benefits on user engagement and energy consumption patterns in heating systems, including increased energy efficiency, reduced costs, and enhanced user satisfaction. Reported energy savings from gamified solutions vary significantly, ranging from 4 % to 42 %, highlighting their potential to transform energy consumption behaviour. Additionally, the study identifies challenges and limitations associated with implementing gamification in DHS and explored the use of technologies like artificial intelligence and machine learning to overcome them. These technologies enhance user engagement by analysing and predicting user behaviour and preferences. By doing so, this research identifies key gaps and potential advancements in gamification techniques for DHS, aiming to optimise system performance and enhance digitalisation. 1. Introduction The Paris Agreement is established to address the urgent challenges of climate change, setting a global goal to limit temperature rise to below 2 ◦C [1]. It underscores the necessity for transformation across various sectors, particularly in energy systems. These systems are pivotal in implementing effective climate change measures, as they directly influence greenhouse gas emissions and energy consumption patterns. Within energy systems, the thermal sector accounts for 50% of Europe’s energy demand [2], with district heating systems (DHS) playing a critical role in promoting sustainable energy practices [3]. Furthermore, taking into account other energy sectors, the integration of sector-coupled systems presents significant opportunities for enhancing the overall efficiency and resilience of energy systems [4]. Recent investigations into sector-coupled DHS highlight the importance of technological advancements [5], storage solutions [6], the role of prosumers [7], and the potential benefits of low-temperature heating solutions [8]. While technological advancements are critical, ∗Corresponding author. E-mail address: [email protected] (N. Abdurahmanovic). the active involvement of consumers is equally important for realising the full potential of DHS [9]. Consumers can influence system as users and contributors, influencing energy demand patterns and system efficiency [10]. Engaging consumers in energy systems is essential for maximising efficiency and achieving sustainability goals. Different strategies have been proposed and used to enhance consumer involvement, including demand-responsive control strategies, smart home technology [11] or cooperative ownership of DHS [12]. However, they all rely on understanding and motivating user behaviour. Gamification and serious games are emerging as innovative methods to motivate and educate users [13] successfully applied in fields like medicine [14], education [15], tourism [16], computer science [17] and others. However, their application in energy systems remains limited, with most examples focusing on electricity or building-level interventions [18]. The potential of gamification in DHS, particularly its broader benefits and challenges, remains unexplored. https://doi.org/10.1016/j.energy.2025.137496 Received 20 December 2024; Received in revised form 15 May 2025; Accepted 8 July 2025 Energy 334 (2025) 137496 Available online 19 July 2025 0360-5442/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). N. Abdurahmanovic and A. Cadenbach Abbreviations DH District heating DHS District heating system 4GDHS 4th generation district heating systems AI Artificial intelligence ML Machine learning ICT Information and Communication Technologies IoT Internet of Things Therefore, this paper aims to contribute to the research by addressing key questions to advance this field. •What are the most effective gamification elements and techniques for engaging end users in DHSs, and how do they influence energy consumption patterns and user behaviour? •What are the measurable benefits of gamification in DHSs and what are the limitations and existing research gaps in this area? •How can digitalisation and smart technologies be applied in DHS to enhance the effectiveness of gamification? To address these research questions, a comprehensive literature review is conducted using a structured methodology (Section 2), followed by an analysis of gamification elements (Section 3.2) and their impact (Section 3.3). The challenges and limitations are then identified (Section 3.4), along with proposed solutions to address them (Section 4). 2. Methodology To address the research questions, the literature review has been conducted following a structured methodology. The process is divided into three main stages: literature collection, filtering and analysis. An overview of the methodology is given on Fig. 1. 2.1. Literature collection The first stage involved formulating a search string designed to capture key terms relevant to the study. The search string included terms: gamification, smart energy systems, district heating, customer engagement, demand-side management, and serious games. These keywords were selected based on their strong relevance to the research objective, aiming to cover the key concepts of user engagement strategies and energy systems. The terms were used in combination (using Boolean operator OR) and queries were performed on major academic databases, including ScienceDirect, IEEE Xplore, Google Scholar, and SpringerLink. The studies underwent a preliminary screening based on their titles to assess their relevance to the research objectives and 184 studies were identified for further evaluation. 2.2. The filtering stage The filtering stage refined the selection by eliminating duplicates and applying a set of inclusion and exclusion criteria. This ensured that only high-quality, relevant studies were retained for analysis. The criteria were as follows: •Peer-reviewed content: Only peer-reviewed articles and book chapters were included to ensure academic rigour. •Recent developments: Studies published within the last 10 years were prioritised to represent the recent developments in the field. •Relevance to gamification: Selected studies had to address gamification, serious games, or both. •Focus on energy systems and DHS: Studies were required to have a primary focus on energy systems, with particular emphasis on DHS where possible. Fig. 1. Methodology overview including all steps. •Technological and behavioural insights: Studies that explored either technological advancements (e.g. smart meters, AI, machine learning and energy management systems), behavioural aspects (e.g., user engagement, motivation and behaviour change), or their intersection were considered. This criterion was important to ensure that selected studies contributed insights into both the technological potential and the human engagement aspects of gamification and serious games in energy systems. Applying these criteria narrowed the pool to 98 studies relevant for further analysis. 2.3. Analysis The selected studies were systematically analysed using a structured template, given on Fig. 2, to ensure consistency. The analysis focused on multiple aspects of the studies. First, general information was extracted, such as title, authors, journal and publication year. Next, the studies were categorised based on their focus into the studies including serious games, gamification or combination of both. The focus area of each study was identified: general energy systems and building automation, the electricity sector, or the heating sector, with a specific subcategory for district heating (DH) to align with the research objectives. The detailed analysis included capturing objectives and motivation behind each study, and documenting the methods used to develop gamification concept or serious game. To address second and third research question, special attention was paid to identification of elements and techniques employed, field test validation, documented benefits, and any smart technologies implemented. In addition to the Energy 334 (2025) 137496 2 N. Abdurahmanovic and A. Cadenbach Fig. 2. Process for analysing the studies. structured review, a keyword co-occurrence network was generated using VOSviewer [19] software by extracting keywords from the titles and abstracts of the selected studies. Minor cleaning was performed by removing general, non-specific terms. A threshold was applied to include only keywords that appeared at least three times across the dataset. The outcomes of this keyword analysis are further discussed in Section 3.4, where they contribute to the identification of research gaps and future directions. Finally, the results were synthesised to provide insights into the current state of gamification and serious games in energy systems, with a specific emphasis on DH and customer engagement. The results of this analysis are presented in Section 3 and serve as the foundation for proposed advancements in gamification techniques to enhance user engagement and optimise DHS performance. 3. Review results This section presents the synthesised review results. It begins by introducing key terms and their definitions (Section 3.1), highlighting both the overlaps and differences between gamification and serious games. It then examines the elements, techniques, and methods used in gamification within energy systems (Section 3.2), followed by a discussion of the measured impacts and benefits (Section 3.3). Lastly, it explores the challenges and limitations identified in the analysed studies (Section 3.4). 3.1. Terms definition Gamification is a broad concept applied across various disciplines. This discussion focuses specifically on its definitions as presented in the referenced studies. The most commonly cited definition [20–38] is from Deterding et al. who describe gamification as ‘‘the use of game design elements in non-game contexts’’. Several studies have adapted or expanded this definition to better suit the context of energy systems. For instance, Galli et al. [39] extend the concept by defining gamification as ‘‘the use of game mechanics to enhance traditional applications and influence user behaviour’’, emphasising its effectiveness in addressing Energy 334 (2025) 137496 3 N. Abdurahmanovic and A. Cadenbach specific challenges. Building on this, Iria et al. [40] define gamification as ‘‘the use of game elements to motivate occupants to adopt energyefficient practices’’. Beyond motivation, gamification should contribute to overall value creation for users, as Lounis et al. [41] highlight, while enabling playful experiences [42] and fostering behavioural changes, particularly in energy-efficient habits. An extended definition comes from Chui and Wai [43], who emphasise that ‘‘gamification is not about creating a real game but rather using game elements to enhance user experience and engagement’’, underscoring its role in sustaining user interest and participation. In addition to motivation and behaviour change, gamification plays a critical educational role. Morton et al. [44] note that gamification seeks to be both entertaining and educational, helping users understand both their choices and their consequences. Synthesising these perspectives, gamification can be defined as: The use of game elements and design, implemented through web platforms, applications, and mobile devices, to communicate with users, motivate engagement, and educate them in adopting behaviours and habits that promote energy efficiency. In contrast, serious games were less represented in the studies and are defined as digital games with educational or training objective [21]. Wee and Choong [45] state that they can be seen as any form of interactive computer-based game software with intention to be more that entertainment. Others claim that serious games are used for educational purposes and can prompt behavioural change [46] or that they have ‘‘explicitly educational objective’’ [47]. A more detailed definition by Huseynli [48] describes serious games as virtual recreations of realworld scenarios that ‘‘aim to change human behaviour (or attitudes and cognitions) via the use of intrinsically compelling elements found in well-made digital games’’. Considering these definitions, serious games are defined as: Games simulating real energy systems and activities that educate, entertain, and motivate behaviour change while ensuring a balance between these aspects, enabling users to effectively explore energy-related challenges. With both terms defined and their characteristics clarified, it can be concluded that gamification and serious games share a common goal: motivating and influencing real-world behaviour. Both approaches also pursue educational objectives by engaging users in learning processes that promote behavioural change. The key difference is in their implementation and scope. Serious games are fully developed games designed with an educational, training, or motivational objective. They offer interactive experiences that often simulate real-world scenarios to achieve their goals. In contrast, gamification is not a game itself but rather the integration of game design elements into non-game contexts to enhance engagement and drive specific behaviours. While serious games create a complete game experience, gamification applies only selected game-inspired strategies to make existing applications or systems more engaging and effective. They can be used individually or in combination to enhance energy systems. 3.2. Elements and techniques Since previously published reviews, such as [29,32,33,36,43,49–53] have extensively covered gamification and serious games elements, techniques, and case studies, this section provides only a brief overview and a visual representation of subcategories (see Fig. 3). This inclusion aims to enhance the paper’s coherence without reiterating content that has already been thoroughly addressed, while placing emphasis on the novelty of this research, which is presented later (Section 4). Different challenges and quests within a gamified system or interface can yield varying point values, allowing for a weighted system where more significant actions earn more points [43]. This structure provides instant feedback, motivating users to engage more actively with the system. Points can then translate into badges, which can be earned and upgraded, fostering a sense of progression and achievement [23,52]. Similarly, leaderboards introduce a competitive element, encouraging users to improve their scores and achieve higher rankings [43]. Higher scores are often the result of personalised challenges which may lead to rewards and incentives. These can take various forms, including monetary rewards, non-monetary gifts, and social recognition, all designed to encourage specific behaviours within a community or system [54]. While competitiveness can drive users to earn more rewards, fostering social interaction and collaboration plays an equally important role. This approach builds a sense of community and shared responsibility, leading to higher motivation [55]. In addition to connecting with others, gamification benefits from connecting users to the real world. This is achieved through storytelling, which creates engaging narratives that mirror real-life situations and enhance user involvement [56]. 3.3. Impact and benefits of gamification and serious games The previous subsection (Section 3.2 partially addressed the first research question by defining gamification and serious games and introducing their elements and techniques. However, the second part of the question: ‘‘how do these approaches influence energy consumption patterns and user behaviour?’’ remains to be explored. This section bridges the gap by synthesising literature findings on the impacts and benefits of gamification and serious games. Measuring behavioural change and user satisfaction in the context of gamification and serious games is challenging, but various approaches have been developed to assess these outcomes. One widely used method is the implementation of questionnaires to collect user feedback. Nasrollahi et al. [32] identify questionnaires as the most used tool for evaluating user behaviour and satisfaction. Mylonas et al. [37] implemented this approach and demonstrated positive outcomes, including enhanced user satisfaction and engagement, indicating that gamification can effectively capture user interest. Further evidence comes from Castri et al. [38], who used preand post-test surveys to assess behavioural changes. Their results show that both competitive and collaborative games successfully promoted user engagement and behaviour change, although the differences between these approaches may be slight. Paneru and Tarigan [57] highlight the effectiveness of energy applications in fostering user engagement and behaviour change. However, they noted that these results were derived from small-scale testing and recommended larger studies to confirm broader applicability. In public buildings, gamification has also shown significant benefits. Kotsopoulos et al. [28] demonstrated that employees not only actively engage with gamified systems but also develop a heightened awareness of the importance of energy conserving. This proves the relevance of gamification in workplace environments where collaborative efforts can significantly contribute to energy-saving goals. In addition to promoting engagement, gamification provides educational benefits. For instance, Peham et al. [35] evaluated the educational impact of games, noting that the design and implementation of gamified systems are heavily dependent on the characteristics and preferences of the target group. This suggests that successful gamification requires a user-cantered approach, where the priorities and needs of the users are considered during the design phase to ensure satisfaction and sustained engagement. Overall, the literature indicates that gamification and serious games are effective in motivating and engaging diverse user groups, including employees [49,58], students [37] and domestic building residents [59]. However, while these approaches are successful in fostering engagement and motivating behaviour change, quantifying these changes remains a complex challenge. As noted in [58], user decision-making processes are multifactorial and context-dependent, making it difficult to attribute behavioural changes solely to gamification interventions. Although direct quantification of behavioural change and user engagement may be challenging, the impact of gamification on energy efficiency and energy savings is more measurable. To address this, Table 1 summarises quantified improvements reported in the reviewed studies, categorised into two groups: Energy 334 (2025) 137496 4 N. Abdurahmanovic and A. Cadenbach Fig. 3. Gamification and serious games elements: categories and subcategories. Fig. 4. Benefits measured in terms of energy usage reduction, energy savings and peak shaving: more details in Table 1. •Reviews that present general findings on user engagement and energy savings. •Individual studies that detail specific measured benefits, providing context for the associated gamification interventions. The measurable benefits of gamification and serious games in energy systems clearly demonstrate their potential as impactful tools for energy savings and behavioural change. However the reported range of energy savings, from 4% to 44% (see Fig. 4), reflects substantial variation in how gamification and serious games are implemented across different studies. These interventions differ not only in context (residential households, office environments, and educational settings) but also in their technological integration and intensity. Column ‘‘Methodologies and interventions’’ in Table 1 suggests that while certain elements, such as feedback, rewards, social comparison, and personalisation, appear across multiple studies, most interventions rely on a combination of techniques rather than a single mechanism. As a result, it is not possible to directly associate energy savings with any one gamification element. However, the repeated presence of these elements suggests that their combination, rather than any individual component, contributes to effectiveness. Furthermore, higher savings are reported in studies that combine gamification strategies with real-time feedback systems, intelligent energy control and management platform. In contrast, studies using more basic approaches, such as static feedback or stand-alone gamified elements, tend to show more modest results. This variability suggests that while gamification and serious games are promising strategies, their effectiveness is not universal and requires careful tailoring to the intended audience and context. This conclusion leads directly to Section 3.4 where the limitations and challenges associated with gamification and serious games will be explored. 3.4. Limitations of existing studies and research gap identification One of the primary challenges of gamification and serious games is sustaining user motivation and engagement over time. Multiple studies [49,58,59] highlight that while initial participation rates may be high, many users tend to disengage over time due to repetitive tasks that lead to boredom. Furthermore, gamification elements do not resonate equally with all users [38], emphasising the need for adaptive strategies tailored to individual preferences. Personalisation remains a significant obstacle, as different groups of users show varied preferences for gamification elements and app functionalities [35]. This represents a challenge due to the limited understanding of how to tailor persuasive technologies to diverse user profiles, as explained by Böckle et al. [42]. Factors such as age and user type can significantly affect how individuals engage with gamified systems. Without a personalised approach, many interventions lack Energy 334 (2025) 137496 5 N. Abdurahmanovic and A. Cadenbach Table 1 Summary of studies on gamification and serious games and their impact. Category Reference Study description Described benefit Methodologies and interventions Reviews Nasrollahi et al. [32] Review on serious games in energy systems. Measured energy savings typically range from 4% to 12%, with peak savings occasionally exceeding 20%. In-home energy displays — 4%–12% savings, personalised measures up to 12%, smart thermostats — up to 17% Paone and Bacher [58] Review on eco-feedback, social interaction, and gamification impact on building occupant behaviour. Energy savings can reach up to 55% with eco-feedback, while behavioural impacts can save 62%–86% in specific dwellings. Eco-feedback — up to 17%, social interaction — up to 55. % Delemere and Liston [60] Review on serious games in energy systems. Average reductions of 15.2% in residential settings, 18.4% in commercial environments, and 9.9% in educational contexts. Serious games and gamification (general, methods not specified). Iweka et al. [51] Review on interventions such as feedback, economic incentives, community-based interventions, and gamification. Social interventions have reported energy savings ranging from 0.4% to 24.5%, while community-wide initiatives have achieved carbon footprint reductions of 17% to 27%. Gamification efforts have resulted in average energy savings of 4% to 24%, and goal-setting approaches have demonstrated an average energy savings of 20.7%. Feedback — up to 24.5%, goal setting — up to 20.7%. Gamification, serious games, IoT platforms Zehir et al. [24] Gamification approach for demand management in electricity sector. Monthly consumption differences reached up to 20%, with peak period consumption differences reaching as high as 16%. On average, peak consumption variations were below 8%. Goal settings, points, leaderboards, teams, monthly report. Casals et al. [46] Serious game to reduce overall energy consumption. Average electricity saving was 3.46% while average gas saving was 7.4%. Storytelling, points, challenges bonus rewards (virtual). Xu et al. [59] Feedback framework with focus on electricity consumption reduction, tested in Singapore. Achieved home energy reduction of 8.18%, with energy saving goal of 5%, and 12.56% with the energy saving goal of 10%. Tailored informational feedback, goal-setting, monetary rewards. Gomes et al. [61] ICT gamification approach applied in office buildings in Lisbon. Overall energy savings varied by location, with the library achieving a 42% reduction, offices recording a 12% reduction, and Amphitheatre realising a 3.5% reduction. Real-time information systems, intelligent energy management, feedback, integrated tech report. Van Der Neut et al. [62] Gamification concept to reduce energy consumption in households. Energy savings range from 4% to 24%. User feedback, personalisation, comparison, challenges, ranking. Sintov et al. [63] Gamification approach with focus on electricity consumption reduction. Study showed 14% energy savings with disaggregated feedback. Points, competition, ranking, push notifications. Gamification, serious games, IoT platforms Gangolells et al. [64] Energy game enerGAware tested in United Kingdom. The game has the potential to save over 48.9 TWh annually, contributing approximately 8% toward emissions reduction targets, with estimated yearly savings of 0.009 GWh and 4 tons of CO2. Challenge, educational content, narrative, feedback, social sharing. Méndez et al. [65] Multiple approaches (gamification, control of thermostats, and feedback) investigated in a study in Mexico. Gamification strategies can reduce energy by 22% while connected thermostats reduce peak load by 10% to 35%. Feedback approaches can reduce energy by 5% to 12%. Rewards, interactive interface, competition, community engagement, smart technologies (thermostats). Carreira et al. [66] ICT-based engagement of users. Study indicated a 5.81% energy usage decrease. Feedback notifications Konstantakopoulos et al. [67] Gamification framework for electricity usage reduction in residential dorm buildings. Ceiling light usage decreased by 23.6% while desk light usage reduced by 60.8% on weekdays. Ceiling fan usage decreased by 19.0% on weekdays. Rewards, points, feedback, lottery mechanisms, gift cards. the ability to create meaningful and lasting impacts. Additionally, providing clear and personalised feedback is essential [68] but this has been shown to be challenging [61]. Effective gamified systems must offer timely, goal-oriented feedback to guide users toward desired behaviours. On the technical side, high implementation costs and installation requirements for IoT systems and other data collection methods present barriers to widespread adoption [69]. While data collection is invaluable for gamification, it also raises privacy and security concerns, further complicating the implementation of gamified systems [53]. Energy 334 (2025) 137496 6 N. Abdurahmanovic and A. Cadenbach Fig. 5. Keywords from studies connected to gamification and serious games in energy systems: electricity sector focus visible. Addressing these concerns requires robust solutions to build trust and encourage participation. Another relatively unexplored area is the application of gamification in demand response programs [39]. Since demand response programs are often connected to dynamic tariffs and incentives, a question arises: how can gamification be effectively integrated, given that studies [59] show monetary rewards often have limited effectiveness? Many users struggle to link in-game actions to real-life behaviours [46], and the rebound or drawback effects can lead to a reversion to old habits. To overcome these challenges, systems must prioritise intrinsic motivation over extrinsic rewards to achieve lasting change. Even when these challenges are addressed, measuring the effectiveness of gamification remains a critical issue. Quantifying the impact of user behaviour on outcomes such as energy efficiency is often difficult, and there is a lack of long-term studies to validate the sustained benefits of these systems [69]. In addition to these limitations, a significant research gap exists in the context of DHS. The conducted studies are primarily focused on the electricity sector or energy systems in general, as illustrated by the visualisation of available literature based on the keywords (see Fig. 5 created with VOSViewer [19]). In this review, and to the best of the authors’ knowledge, no gamification approach has been identified that analyses the impact of gamification on DHS as a whole, with only one study on serious games in this area available [18]. These limitations underscore the need for thoughtful design and continuous adaptation in gamification and serious games to maximise their effectiveness with a special focus on DHS. This section highlights the need for adaptive strategies, personalisation, effective feedback mechanisms, demand response integration with dynamic tariffs, and solutions for security and data privacy issues. Furthermore, it points to the necessity of investigating the application of gamification in DHS. Therefore, the next section offers proposals for improvement alongside application possibilities for DHS. 4. Gamification (and) optimisation in DHS The identified benefits of gamification in energy systems, particularly in DHS, can be enhanced by using smart technologies such as machine learning (ML) and artificial intelligence (AI) [70–74]. Table 2 summarises examples of how AI and ML have been applied in previous energy-related studies. Although the reviewed studies apply AI and ML technologies in energy systems, they do not explicitly quantify the direct impacts of these implementations. Nevertheless, given that these technologies can both amplify existing benefits and address challenges associated with the implementation of gamification in energy systems, further research is necessary to explore their potential, particularly in the context of DHS. However, the terms AI and ML are very broad, so this section will be structured in two parts: •Defining key terms, their connection to DHS, and introducing methods within AI and ML (Section 4.1). •Strategies for implementing and optimising gamification in DHS using AI and ML (Section 4.2), categorised by focus areas. –Technological focus –Economic focus –Behavioural or psychological focus –Educational and acceptance focus 4.1. Terms definition AI refers to systems that display intelligent behaviour by analysing their environment and taking actions to achieve specific goals [75]. This enables machines to perform tasks such as learning, problem solving and decision making. In the context of DHS, AI can process and analyse large amounts of data to optimise gamification strategies and improve system efficiency. ML is a subset of AI focused on algorithms and statistical models that enable machines to learn from data and improve their performance over time. For example, in gamification for DHS, ML can be used for predictive analytics based on historical and real-time data. The subcategories which could be used are: •Adaptive learning systems use AI to modify their behaviour and outputs based on new data over time [76]. •Federated learning is a privacy-preserving ML approach where model learns from decentralised data stored on local devices without transferring it to a central server [77]. Energy 334 (2025) 137496 7 N. Abdurahmanovic and A. Cadenbach Table 2 AI and ML implementation in reviewed studies. Reference AI and ML implementation Avila et al. [27] The study uses an adaptive neuro-fuzzy inference system (ANFIS) to classify electricity consumption levels and tailor gamification strategies by providing personalised user feedback. While the approach is suggested to be effective, no measurable impact is reported. Méndez et al. [65] Two artificial neural networks are used to link household consumption data and personality traits with gamified elements. An AI-based decision system adapts interface design and gamification strategies to user profiles. No measurable impact is reported. Sintov et al. [63] AI techniques for anomaly detection and user behaviour analysis enable personalised appliance monitoring, real-time feedback, and gamified user prompts through a mobile app. The authors suggest that this framework could be expanded to other domains. Table 1 shows that there are 14% energy saving estimated in this study, however it is not explicitly stated how much impact comes from AI techniques. Paneru and Tarigan [57], The study discusses the integration of AI and ML in smart energy apps to learn household consumption patterns and deliver personalised feedback aimed at influencing user behaviour. While the potential for behaviour change is acknowledged, the study emphasises the need for further research to evaluate the actual effectiveness of these technologies. •Anomaly detection involves using AI to identify unusual patterns in data that derivate from expected behaviour [78]. •Behavioural modelling refers to using ML algorithms to understand and predict user behaviour based on past actions. •Clustering is an ML technique that groups users based on shared characteristic or behaviours [79]. •Reinforcement learning is the problem faced by an agent that must learn behaviour through trial-and-error interactions with a dynamic environment [80]. •Predictive analytics involves using AI and ML to forecast future events or behaviours based on historical and real-time data. 4.2. Strategies for implementing and optimising gamification in DHS 4.2.1. Technological focus Technological focus emphasises the integration of AI and ML with gamification to optimise system performance. These technologies act as the backbone of gamification, enabling energy-efficient operations of DHS. Predictive demand response optimisation: Demand side management has shown to be a successful technique for peak shaving, reduction of primary energy needs, emissions and cost reduction [81–84]. To ensure effective demand response mechanisms, historical data, weather forecasts, and current usage patterns must be analysed to predict peak demand periods in DHS. In this process, a significant amount of data is processed, and AI can optimise this process. At the same time, gamified incentives, such as points, can encourage users to shift their heating usage during high-demand periods. This means that AI can predict peak periods while also generating patterns for gamification, motivating users to participate in demand reduction efforts. Predictive maintenance and system optimisation: A significant challenge faced by energy systems, particularly DHS, is the time required to identify and resolve failures and faults [85]. In this case AI can be employed to track the performance of heating equipment across the systems, enabling predictions on when maintenance might be required [86]. At the same time, users can report issues with heating systems or devices and gamification can encourage them by rewarding points or incentives for proactive reporting. With AI-driven predictive maintenance, which includes gamification, system downtime can be minimised, and reliable heating supply can be ensured. Users benefit both from incentives but also from secure and reliable system. Energy efficiency increase: The results show that gamification can significantly influence on energy efficiency and results in increasing it. However, this is shown to be short term, variable and to cause userfatigue over some time. To further enhance it, AI can be used to analyse data from smart meters and IoT devices and to provide users with personalised insights. on how to improve energy efficiency can help the system. These insights, delivered through gamified recommendations, offer actionable steps for users, while simultaneously improving overall system performance. Secure and privacy-preserving data analytics: Smart meters and IoT are of great importance for systems because they enable DHS to become proactive and integral part of the energy sector and could facilitate modelling and forecasting [87]. Overall, data collection and analytics support digitalisation process and can help with many different aspects of energy systems. This is also important for gamification, since consumption patterns analysis and incentives adjustment are based on collected data. However, an issue that arises here is secure and privacypreserving data analysis. Therefore, ML can use privacy preserving techniques, such as federated learning, to analyse data locally on user devices. Only aggregated insights are shared, protecting individual user data. Furthermore, gamified elements can be used to ensure transparency by showing users how their data is used and allowing them to opt into specific challenges. 4.2.2. Economic focus AI and ML, combined with gamification can have significant economic benefit and this can be seen from both from supplier’s (demand management and renewable energy incorporation) and consumer’s (incentives and feedback) perspective. Dynamic pricing models: Dynamic pricing models are commonly used in electricity sector for demand shifting. They have proven effective in incentivising the integration of variable renewable energy and peak consumption reduction.However, dynamic pricing is slowly evolving in DHS [88] but could be equally beneficial for 4th generation sector-coupled DHS. They allow price adjustments based on renewable heat availability and peak demand. AI-based demand prediction can optimise this by dynamically adjusting heating prices in real-time. Combined with gamification, users are incentivised to shift consumption, with AI calculating rewards based on savings or usage timing. This enables flexible, user-responsive pricing strategies. Real-time feedback and immediate incentives: The challenges of shortterm effects and user fatigue identified in the previous section can be addressed with immediate incentives [89]. This way consumer is actively engaged and supplier has better overview of incentives distribution. To maximise the benefit of such mechanisms, AI-driven data analytics can provide real-time feedback and rewards. For example, if a user reduces heating during peak times, they receive instant feedback showing their contribution to system efficiency and an immediate reward. 4.2.3. Behavioural or psychological focus This category emphasises the possibilities of combining smart technologies with gamification for fostering user participation and encouraging sustainable behaviour in DHS. Personalised challenges and rewards: Together with real-time feedback, one more approach to keep users motivated over longer periods Energy 334 (2025) 137496 8 N. Abdurahmanovic and A. Cadenbach of time is to provide personalised challenges and rewards [90]. This means that, based on users’ heating patterns, preferences, and past behaviour, AI can generate and suggest personalised goals, such as reducing heating use by 10% during peak hours or maintaining a specific temperature range. Additionally, rewards can be tailored based on users’ reactions to previous incentives, ensuring satisfaction upon task completion. Dynamic adaptation and difficulty adjustment: ML can be used to track user and completion rate of challenges. Based on this data, the difficulty of challenges and frequency of rewards can be dynamically adjusted to maintain engagement without overwhelming users. By adapting to user behaviour, fatigue is prevented, and motivation can be maintained, ensuring long-term participation in the gamification program. Community-based gamification and social comparison: The results of review show the importance of community collaboration and social comparison in gamification. AI can further enhance this by grouping users based on specific parameters or shared interests, and encouraging participation in group challenges to boost engagement. Seasonal and event-based gamification and engagement balancing: One more measure to tackle the issue of fatigue and loss of motivation is to use AI for user fatigue monitoring. This is possible by analysing engagement data such as reduced participation or inactivity. Based on these insights, AI can reduce the frequency of challenges, offer smaller goals or introduce new elements. AI analyses seasonal patterns in heating use to create timely challenges and events (e.g. a ‘‘Winter Savings Challenge’’ to reduce heating usage during winter months. By monitoring fatigue and offering additional elements it is possible to create renewed interest in users. All techniques tackling behavioural aspect can further be improved by incorporating user segmentation. This means that ML or AI could cluster users into segments based on their heating behaviour and preferences (e.g. high-energy users, energy savers) and set specific gamified experiences for each group, such as more challenging goals for highenergy users and educational tips for energy savers. Such targeted gamification increases relevance, making it more effective for each segment and promoting behavioural change across diverse user types. 4.2.4. Educational and acceptance focus Gamification has been shown as successful tool in enhancing energy literacy and acceptance (discussed in Section 3.3). However, it can be further improved by using smart technologies. Educational content and gamified learning modules: Educational modules can be customised using AI based on user knowledge level and behaviour patterns. Increased energy efficiency can result from educating users about its importance, such as lessons on efficient heating use, presented as mini-games or quizzes. Rewards for completing these modules can be provided, encouraging users to engage with educational content. Enhanced decision-making for system operators: Using gamification is of significant benefit to the system operators since it enables better operational schedule, improved energy efficiency and better satisfied customers. With AI this can be further improved since system operators are provided with real-time insights into user behaviour and demand patterns, allowing them to make data-driven decisions on heating distribution. Gamification can also extend to operators, rewarding them for achieving efficiency goals across the DH Regardless of focus, AI-powered feedback loops are a significant advantage. ML algorithms continuously analyse user interaction data and system performance, refining gamification strategies dynamically. Furthermore, it is important to point out that there is some overlapping between the categories and methods and that it is possible to achieve maximum result with their combination. Fig. 6 visually represents an example of such combination. This chapter has so far shown that AI and ML combined with gamification could lead to significant benefits to different aspects of the system. However, an important question is how these can be implemented in existing systems where no smart meters are available and such data collection is not taking place. While incorporating such devices is recommended for digitalisation and sector coupling [87], alternative approaches are also possible with the help of users. By engaging users to report their heating behaviours or energy-saving actions through gamified apps, data can be provided to AI to aggregate and analyse patterns and generate necessary gamification approaches. Furthermore, apps can be data collectors or indirect sensors, since they can use geolocation, weather data and manual inputs from users. In the absence of real-time data, ML algorithms can create simulation models of the heating system which can predict heating usage patterns for different buildings or user groups. This enables virtual gamification where users interact with modelled data, but also can learn more about system and technical properties which can result in acceptance increase. Users can also be motivated and gamified by AI-provided challenges based on general system performance, rather than individual usage data. Though the impact of AI and ML is limited without smart technologies, it remains viable. Gradual integration of smart meters and IoT can progressively optimise gamification in DHS. Technical limitations and considerations: The strategies outlined above demonstrate that AI and ML technologies can significantly enhance gamification within DHS. Through adaptive learning, dynamic feedback, user segmentation, and predictive analytics, they enable more targeted, responsive, and context-aware strategies that boost system efficiency and user satisfaction. They also support automated challenge personalisation, anomaly detection, and real-time alignment of incentives with system-wide goals. However, the integration of these technologies is not without technical and implementation challenges. Real-time processing of data from smart meters and IoT devices can be computationally intensive, especially in large-scale systems with high data volume. This requires adequate digital infrastructure and may not be feasible in all existing DHS setups. Additionally, the performance of ML models depends heavily on the quality, frequency, and consistency of input data. Missing values, measurement errors, or delays can significantly reduce model reliability and produce misleading feedback for users. Latency issues can further limit the immediacy and relevance of gamified responses which is an essential component for maintaining user engagement. Similar technical limitations have been identified in real-time edge AI for IoT devices [91]. The computational demands of advanced models also lead to higher energy use, which may conflict with system-wide efficiency goals. Finally, maintaining up-to-date models over time requires continuous re-training and monitoring, adding long-term operational burdens. Beyond these technical aspects, smart technologies can also unintentionally amplify existing issues. For example, more complex interfaces and adaptive systems may exclude less digitally literate users or those without access to smart devices, reinforcing digital inequalities. There is also a risk of over-dependence on high-resolution data and automated decision-making, which can reduce transparency and user control. Thus, while AI and ML hold strong potential to enhance DHS gamification, their deployment must be carefully planned. Solutions should balance sophistication with usability, ensuring accessibility, transparency, and privacy. Further research is needed to examine how AI-supported gamification can be effectively implemented across varied DHS contexts, particularly regarding data quality, scalability, and inclusiveness. 5. Conclusions and outlook This paper has explored the potential of gamification and serious games through a structured literature review, synthesising insights from various studies and highlighting measurable benefits, limitations, and future opportunities. By defining gamification and serious games and comparing their applications, it is evident that both share a common Energy 334 (2025) 137496 9