In the future, everything may be ‘connected’ and ‘smart’ in the built environment. The concept of smart lighting has emerged over the past decade in commercial and industrial environments, predominantly focusing on energy saving. From a broader perspective, smart lighting in the residential environment is a part of the ‘smart home’ concept where the main goal is to provide and promote user comfort, convenience, and security, and to satisfy residents’ needs [1
]. Rossi [2
] (p. 179) defined smart lighting systems (SLS) as ‘lighting systems with the ability to control
, communicate and interconnect data
, able to provide new ways of interacting with the luminous performances in new luminaires
, equipped in turn to offer additional service
’. Schubert and Kim [3
] (p. 1277) defined smart lighting as ‘solid-state sources—in particular
, Light Emitting Diodes (LED)-based sources—offer what was inconceivable with conventional sources: controllability of their spectral, spatial, temporal
, and polarisation properties as well as their colour temperature
’. In general, smart lighting systems consist of energy-efficient light sources such as LEDs, a wireless communication network including software, and (optional) sensors aiming to provide optimal lighting solutions integrated with a control system [2
]. A smart light bulb is an illumination source (LED system or another light source) in a housing configured to fit a conventional light fixture. Additionally, the housing contains a processor, which controls the light source’s intensity and colour, and a transmitter and/or receiver to enable signal exchange with other smart devices, such as another smart light bulb or another device. The type of protocol used for (wireless) communication within the SLS is reported to have varying power use, as there are several communication protocols. The ZigBee protocol, frequently used these days, consumes less power than other protocols like Wi-Fi [4
]. Hence, a network based on Wi-Fi communication only consumes more energy than if part of the data communication is done via a ZigBee protocol (see Figure 1
). Protocols like Bluetooth Low Energy (BLE) have the advantage of consuming an even lower amount of energy but are limited in communication distance and the number of connected devices.
Recent literature reviews on smart lighting and controls showed that the application of smart lighting systems is mainly conducted in non-residential environments, focusing on energy savings [6
]. Studies in office environments exhibited potentials for energy saving varying from 17 to 94% over traditional (manual) control systems, depending on user behaviour, activity patterns, and different types of control systems, such as daylight harvesting and occupancy control systems [6
]. Control systems based on occupancy-sensing are commonly used for energy saving by detecting the user’s motion in the targeted environment. This control system can potentially result in energy savings of 3 to 60% depending on user behaviour and activity patterns [6
]. Daylight-integrated lighting control systems can be used to turn off or dim down the electric lights automatically based on the available natural light in the room to achieve a target illumination level. Studies have shown that this type of control system can typically achieve over 40% of energy saving [11
]. However, their effectiveness highly depends on orientation, latitude, and window characteristics. Other types of control systems, such as schedule-based control systems, are useful when occupancy patterns are predictable and set [7
]. The use of the different lighting control systems may result in significant energy savings, but occupants’ behaviour, building or room properties (geometry), daylight entrance, and type of activity have substantial effects on a system’s performance [7
]. Even though many smart lighting studies have focused on energy saving issues, recently, studies were conducted to investigate promoting and supporting user well-being [18
]. The importance of lighting design and its effect on well-being in the built environment was discussed by Altomonte et al. [23
], as it affects and is affected by, for instance, aesthetic aspects of the environment, visual comfort, visual performance, and light effects beyond vision.
For commercial buildings, innovative luminaires with daylight-dependent dimming and wirelessly controlled occupancy sensors have already been on the market for decades. Available residential studies mainly focus on computational modelling (and validation) of control and behaviour (e.g., [24
]). Wasted energy consumption by lighting in scenarios where light is on in unoccupied rooms at home may relate to behavioural goals and social needs. In this regard, Gerhardsson et al. [26
] investigated various reasons behind electricity consumption by lighting in Swedish homes and concluded that keeping the lights on in unoccupied rooms serves a purpose such as preventing visual and aesthetic discomfort, providing safety, and making the home more inviting. The use of smart bulbs in homes is expected to increase from just over 2% in 2020 to nearly 8% in 2025 [27
]. Even though statistical analysis predicts an increase in smart products in homes, it does not predict user acceptance and long-term usability. To benefit from lighting products’ smart features and opportunities, households must accept, value, and use the products. According to Juric and Lindenmeier [28
]), especially if the goal is to improve users’ moods and their satisfaction with the lighting and the indoor environment [32
Even though the number of smart lighting bulbs in homes is increasing, the opportunities for smart lighting systems in the residential environment and their effects on well-being and energy performance have not been widely explored. Therefore, this study aimed to review the literature regarding the effect of smart lighting systems on energy consumption and well-being in residential environments.
2. Materials and Methods
A literature review was conducted using two scientific literature databases: Scopus and Web of Science. Both databases are known for multidisciplinary peer-reviewed scientific literature and cover subject areas within the field of social, physical, health, and life science. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [33
] was used for finding, reporting, and evaluating records. A PRISMA flow diagram was used to show the flow of information in the different stages of the review process, indicating the number of identified records, included and excluded, as well as the reasons for exclusion.
2.1. Eligibility Criteria
The eligibility criteria were based on the publication year (2001 onwards), subject area, document type (limited to peer-reviewed journal articles and reviews), and language (English). The publication year was set to studies published after 2001, as this was the year in which information about the additional functionality of photoreceptors in humans, largely responsible for light effects beyond vision, was published (e.g., [35
]). Specific subject areas were excluded, as they resulted in finding irrelevant papers (material, economy, agriculture, earth/planetary, arts, pharmacology, chemistry, chemical engineering, bionic, immunology, business).
2.2. Search Strategy
The search process started by defining and categorising search terms, which were divided into four categories (see Table 1
). The category ‘Lighting system’ was the main interest of this study and included all terms related to smart lighting. The synonyms for the term within this category were expanded numerous times during the search process. The category ‘Context’ provided the focus on residential environments. The category focusing on the outcomes of the studies, ‘Light effects’, was divided into ‘Light effect on energy’ and ‘Light effect on well-being’. The latter two categories resulted in two search strings: (1) ‘Lighting system’ and ‘Context’ and ‘Effects on energy’ (2) ‘Lighting system’ and ‘Context’ and ‘Effects on well-being’.
2.3. Study Selection
The search strings were applied within both databases, the results were merged, and duplicates were removed. Subsequently, three reviewers (the authors of the paper) screened the papers on title/keyword/abstract (TKA) individually by checking the relevance of the studies regarding smart lighting in a residential context on either well-being and/or energy. Papers were selected for a full paper review if two out of three reviewers assessed the paper as relevant. During the TKA screening process, papers were included in case they investigated the impact of a smart lighting system on energy and/or user well-being in a residential environment. Consequently, papers were excluded when they were conducted or proposed for a non-residential environment, were review papers, designed conceptual frameworks without reporting energy or well-being related issues, used non-smart systems, developed algorithms exclusively, focussed on user interaction with smart home appliances, focussed on user behaviour exclusively, or were conducted with a very specific user group (e.g., patients suffering from severe dementia). In case the results were technical papers or if the full paper was not available, the article was not included in the full-paper review. Additionally, the references of the found papers were screened to find more relevant studies.
2.4. Data Collection Process
Eligible studies were reviewed, and information was collected regarding the definition of a smart lighting system, the intended study aim, study characteristics, which included methodological characteristics (i.e., study type, measurement method(s), sample size, study duration, target group, and main outcome) and smart lighting system characteristics (i.e., the composition of the discussed/investigated system regarding the type of light source, communication protocol, control input, and sensor type).
2.5. Developed Research Strategy
The research strategy focused first on the documentation of smart lighting systems’ definition since the search terms were adapted multiple times during the search process. Secondly, it documented the studies’ aims as defined in the papers, as searching for studies in two extensive domains (energy, well-being) may result in studies that fulfil the search criteria but serve a different purpose. Hence, categorisation was applied to enable the assessment and/or comparison of results from the topic/domain as well as the methodological perspective. Thirdly, the data collection process, as described in Section 2.4
, was executed and documented, and, eventually, the results were discussed per category.
This literature search resulted in finding 13 studies, which were difficult to compare for two main reasons. First, the definition of the ‘smart lighting’ studies was not comparable and the studies varied in required system components. Currently, no unambiguous definition of ‘smart lighting system’ exists, as various synonyms were used for describing an SLS, and its components are not extensively characterised. Disputably, a smart lighting system does not need to contain solid-state lighting or sensory technology for occupancy or daylight harvesting to function. Secondly, the 13 studies had varying aims that were categorised, focusing on ‘component performance’, ‘system design/development’, and ‘application evaluation/implementation’.
4.1. Component Performance
Related to the intended aim of the study, only one study that fulfilled the search criteria, targeted system performance at a component level. Dikel, Li, Vuotari and Mancini [44
] investigated the standby power consumption of different commercially available smart LED bulbs. Their results showed a standby consumption of (less than) 0.5 W per bulb. As mentioned in the introduction, components also used for (wireless) communication within an SLS are reported to have varying power use. Since residential environments have different (room) occupancy patterns and often a higher number of light sources compared to non-residential environments, consumption investigation at a component level can reveal relatively high saving percentages. This includes the type of light source, which is a crucial component when investigating energy performance. LED sources have higher efficacy compared to, for example, CFL sources. For commercial buildings, replacing CFLs with LEDs shows a reduction of energy demand by 43–52% [51
]. The substitution of CFLs with LEDs resulted in 25–40% electric energy savings per year in Scandinavian and German residential studies (e.g., [52
]). For studies that focused on well-being, the type of light source seems less relevant if only the illuminance level is investigated. However, other qualities, like differences in the spectral power distribution of sources, have different effects on well-being-related variables, such as sleep or mood (e.g., [54
4.2. System Design or Development
There were nine of the 13 investigated studies that aimed to design or develop an SLS. Five of these studies [38
] had an energy-related focus and used LED-light sources, whereas one study [48
] focused on energy but did not specify the used light sources. Two studies focused on well-being; Izsó, Láng, Laufer, Suplicz and Horváth [41
] used CFL sources for their investigations, and Wang [42
] did not specify the used light sources. One study took both energy and well-being into account and used halogen, CFL, and LED sources. Its reported energy saving potential was 75–93%, potentially being high due to the change from a (conventional) halogen/CFL system to a smart system, including LED sources [49
The five exclusively energy-related studies with broadly comparable components showed a large variety of energy saving potentials ranging from 2 to 55%. All five studies used LED light sources, but their power use was not always specified. Looking at the variety of communication protocols and control input variables, all five studies used the ZigBee protocol for data communication but get their input based on ‘daylight levels’ [47
], ‘occupancy’ [43
], ‘daylight levels combined with activity recognition’ [38
], ‘daylight levels combined with occupancy’ [40
], and ‘occupancy combined with activity recognition’ [39
]. Tang, Kalavally, Ng and Parkkinen [47
] controlled the smart lighting using daylight levels and reported an energy saving potential of 55%, while the studies by Kwon and Lim [38
] and Byun, Hong, Lee and Park [40
] combined controlling by daylight with either activity recognition or occupancy. Both studies reported lower saving potentials. However, the geographical locations of the three studies and hence, their daylight climate, were different, with the study by Tang, Kalavally, Ng and Parkkinen [47
] being executed close to the equator (Malaysia, ~3° N), which has equal daylengths year-round. Kwon and Lim [38
]’s study was executed in South Korea (~37° N), with larger seasonal temperature, daylight, and daylength differences compared to Malaysia. Local climatic conditions have an influence on the occupancy of buildings, as levels of outdoor activity vary due to seasonality and weather conditions (e.g., [55
]). Unfortunately, as two [40
] of the five studies focusing on design or development did not specify a geographical location, a potential explanation for the difference in the reported energy saving potential related to the geographical location and the local daylight could not be explored.
Two studies with an aim related to SLS design or development [41
] used CFL sources, which may be less crucial since their focus was predominantly on creating a visually comfortable environment or testing the impact of illuminance levels on well-being. Frascarolo, Martorelli and Vitale [49
] investigated the impact of different light sources in a simulated house with a simulated user evaluation, but the study by Izsó, Láng, Laufer, Suplicz and Horváth [41
] was a laboratory study in a simulated living room involving 30 participants varying both the illuminance and CCT, and it tested the performance during a cognitive and visual task. The extremely different conditions and outcome variables make any comparison impossible. A third field study using CFL sources, focusing on well-being outcomes and involving 32 participants, aimed for application evaluation rather than the design or development of an SLS [50
]. SLS development with simulated user evaluation was used in two studies [42
]. Frascarolo, Martorelli and Vitale [49
] aimed for visual comfort ‘when and where it is needed
(p. 217)’ and Wang [42
] simulated 10 potential user locations in a room. Both studies checked the agreement with visual comfort or performance criteria, but the results were inconclusive.
4.3. Application Evaluation and Implementation
Even though six studies [38
] indicated via their study aim that they predominantly focused on system development, the evaluation by users or the impact on users was included using outcome variables ranging from a subjective visual comfort assessment to physiological measurements. None of the studies was able to draw firm conclusions, and this was also the case for the studies that aimed for application evaluation and implementation. Plischke, Linek and Zauner [50
] evaluated an SLS and its application area in three nursing homes. Kumar, Kar, Warrier, Kajale and Panda [45
] focused on well-being outcomes and used LED sources, while Ringel, Laidi and Djenouri [46
] focused on energy saving and used/compared regular and smart LED bulbs. The former combined laboratory experiments with computational modelling, while the latter simulated one year of energy consumption, and thus, with limited to no human interaction. Only two studies performed real-world field studies with human beings involved. The duration of the studies varied from one day [50
] to 14 days [48
], and this is seen, from both energy and well-being perspectives, too short a timeframe. If the effect of smart lighting on sleep quality—one significant marker of human well-being—is investigated, light exposure, together with differences in social schedules (workdays versus days off), may only manifest itself after taking multiple cycles of one working week.
4.4. Study Limitations
A literature review is suitable to provide an overview regarding a research area and state of knowledge. In this case, there were even two research areas, and ‘energy’ and ‘well-being’ are broad fields. Multiple studies were expected but only 13 papers indicated that published studies regarding the effects of smart lighting systems on energy consumption and well-being in residential environments were relevant.
One of the eligibility criteria was the limitation to peer-reviewed journal articles and reviews, excluding conference contributions. Even though the study characteristics of the currently included studies were already often incomplete, including conference papers would most likely not increase the methodological value but may have covered ongoing initiatives, such as pilot studies or the testing of prototypes.
The search concentrated on complete smart lighting systems, and only one study focused on the performance at the component level. Broadening the search strategy, including specific components such as ‘communication protocol’ or ‘occupancy sensor’ was not in the scope of this review.
The documentation of characteristics in general, and for lighting systems in particular, was limited to general study characteristics, including light source and control type. Neither the used instrumentalism for light and lighting measurements nor the documentation regarding the completeness of an energy saving potential calculation was considered.
The literature search was conducted using two databases. Even though Scopus and Web of Science are recognised as two major multidisciplinary literature databases and used by many research studies to find peer-reviewed articles, including more (topic-specific) databases could potentially have led to a (slightly) broader result.
5. Conclusions and Recommended Action
This study demonstrated that there is a need for an unambiguous definition of a ‘smart lighting system’ and its required components. The benefit of using an equivocal definition makes research results more accessible, and less confusion would be made with synonyms. A clear definition of the system composition enables comparison between components, systems, and full applications. The technical performance of a smart lighting system is essential to enable the quality rating of the system. Clear documentation of the type of light sources, communication protocols, control inputs, sensor types, and algorithms are necessary to conclude the quality and performance of the tested system. In situations involving information related to daylight, fundamental documentation of the geographical study location and the date and time of the execution are required.
Aside from the technological quality assessments, high-quality (controlled) intervention studies on human performance and interaction measures enable the corroboration of possibilities and light effects regarding visual performance and visual comfort as well as effects beyond vision in personal environments, such as a residence. In particular, for investigating how light beyond vision is affected by a smart lighting system, a more detailed methodology is needed, including the documentation of light amount (level/intensity), light directionality, spectral power distribution, exposure duration, the timing of light exposure, and prior light exposure (e.g., [56
]). It is crucial to follow available protocols for the proper communication of light exposure [57
In parallel to cause–effect studies, studies focusing on acceptance of the technology or investigating user interactions with smart lighting systems are needed. This would allow for relating system performance to dimensions common in day-to-day situations and would deliver input for an optimal design of the residential smart lighting system. It may find the answer to questions regarding whether all rooms or only specific rooms should be equipped; whether smart lighting-related monitoring should be completely sensor-based. smartly balanced between system-controlled and human-controlled, or only task-specific (i.e., waking up), and in what way a smart lighting system should optimally interact with the local climate (weather) and situation (built environment).