A Systematic Review of the Impact of Real-Time Energy Feedback on Social Housing

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Gomez, E., Carter, K., Lee, W.V. (2025). A Systematic Review of the Impact of Real-
Time Energy Feedback on Social Housing. In: GhaffarianHoseini, A. et al. (eds) Pro-
ceedings of the International Conference on Smart and Sustainable Built Environment
(SASBE 2024). Springer: https://doi.org/10.1007/978-981-96-4051-5_155
Abstract. Energy feedback systems are becoming increasingly common in homes, of-
fering real-time insights into consumption patterns through in-home displays, web por-
tals, and mobile apps. While these systems have shown potential in raising awareness
and encouraging energy-efficient behaviors, their impact within social housing—where
fuel poverty and energy insecurity are significant concerns—remains relatively under-
explored. This review examines the potential economic and social benefits of Real-
Time Energy Feedback (RTEF) systems in social housing, identifying both opportuni-
ties and challenges. Existing literature tends to emphasize direct outcomes such as
energy savings and awareness. However, our findings suggest that these tools have
untapped potential beyond utilitarianism. When combined with renewable energy
sources like solar photovoltaics (PV), RTEF systems not only increase energy savings
but also foster greater household participation in the energy transition. This positions
them as key enablers of energy citizenship in decentralized energy systems. We rec-
ommend further research to explore the broader social impacts of RTEF systems, es-
pecially within the context of renewable energy integration in social housing, and to
assess their critical role in facilitating the energy transition and shifting energy con-
sumption paradigms.
Keywords: Social Housing, Energy Savings, Energy Awareness, Real-Time Energy
Feedback (RTEF), Renewable Energy Systems (RES).
1 Introduction
Evidence suggests that Real-time energy feedback systems can promote energy
conservation, with some studies reporting reductions as high as 20% (Darby, 2006;
Karlin et al., 2015; McAndrew et al., 2021; Chatzigeorgiou et al., 2021; Gupta et al,
2023). However, the effectiveness of these systems is highly variable, influenced by a
range of factors that are both internal and external to the interventions. This paper
explores how various factors—ranging from user engagement to technological
design—affect the success of these interventions. It also highlights the role of RTEF
systems as a flexible and scalable solution, while acknowledging the limitations and
challenges that affect their success. Current research predominantly focuses on the
economic aspects and resource generation capabilities of RTEF systems, often over-
looking the broader social benefits. We propose that the emphasis should expand be-
yond energy savings to explore the transformative potential of RTEF, particularly within
decentralized energy systems. Our findings suggest that when RTEF is combined with
renewable energy sources, such as solar photovoltaics, households not only achieve
greater energy savings but also gain opportunities to actively participate in the energy
transition, obtaining additional benefits. While there is no direct correlation between the
technological novelty of RTEF systems and enhanced outcomes, our research
indicates that these tools have yet to establish a strong presence in social housing
environments. The inconsistent results observed across different contexts highlight the
need for caution in interpreting the impact of RTEF, as its implementation does not
always yield quantifiable energy savings, although it may influence other social
dimensions. The impact of Real-Time Energy Feedback (RTEF) systems extends

beyond economic savings, playing a crucial role in fostering energy behaviours
essential for sustainability. While integrating RTEF into comprehensive retrofit
interventions may not always result in significant immediate savings, these systems
contribute to long-term behavioural changes and help mitigate rebound effects. With
global energy consumption projected to rise over the next three decades (EIA, 2021),
it is crucial to recognize RTEF's potential in promoting sustainable and efficient energy
usage, even in retrofitted environments. By focusing on social housing, we aim to
address a significant gap in the literature where RTEF systems are under-researched
but could provide substantial benefits. Additionally, this study offers insights into how
RTEF can contribute to renewable energy integration and decentralized energy
systems, reflecting its growing role in the energy transition.
2 Methodology
This review assesses the implementation and effectiveness of real-time energy feed-
back (RTEF) systems in social housing by systematically evaluating existing literature.
It follows the PRISMA guidelines, to ensure a rigorous and reproducible approach to
identifying and analyzing relevant studies. The PICO framework was used to help refine
the research question. The PRESS checklist was used to optimize the search strategy
for accuracy and comprehensiveness, while the Cochrane Handbook established ro-
bust review criteria.
Table 1. Stages in the systematic review process.
Stages
Details
1. Framing the question
The clear question to be answered and/or hypotheses to be
tested
2. Search strategy
Use PICO and break into concepts, use Boolean logic, con-
trolled vocabulary and keywords, identify major bibliographic
Database.
3. Inc./Exclusion criteria
Select studies that respond to the research question consider-
ing masked information to limit bias.
4. Quality assessment
Conduct a sensitivity analysis, Criteria for Risk of BIAS, Re-
view results.
5. Synthesis
Assessment of body of evidence that goes beyond factual de-
scriptions to base conclusions.
2. 1 Research question
The main research question is: "Does real-time energy feedback lead to economic and
social benefits for social housing residents across different housing associations?" The
study uses the PICO framework to structure its analysis:
P (Population)
Te na n ts in s o ci a l ho u si n g, in cl u di n g al l d e mo g ra ph i cs .
I (Intervention)
Implementations of RTEF systems via web portals, mobile apps, or
in-home displays, offering real-time or near-real-time feedback.
C (Comparison)
Approaches by various housing associations in the UK and Europe.
O (Outcome)
Primary outcomes are economic benefits (e.g., reduced energy
costs), with secondary outcomes including social benefits (e.g.,
increased energy awareness)
To address this question, three objectives were established:
1. Evaluate the effectiveness of RTEF interventions.
2. Analyze the frameworks and factors influencing their design and implementation.
3. Review methods used to measure outcomes in different interventions.

2.2 Search strategy
The search was conducted between May 2023 and August 2024 across multiple data-
bases, starting with Google Scholar, Web of Science, and Scopus, and later including
JSTOR, IEEE Xplore, ScienceDirect, and SSRN. Boolean logic was employed to refine
the search, using synonyms and related concepts. Key search terms included: (‘real-
time feedback’ OR ‘energy monitoring’ OR ‘energy feedback’ OR ‘visual feedback’ OR
‘energy visualization’ OR ‘smart technologies’) AND (‘social housing’ OR ‘public hous-
ing’ OR ‘affordable housing’ OR ‘low-income housing’ OR ‘home automation’) AND
(‘impact’ OR ‘effect’ OR ‘reduction’ OR ‘saving’ OR ‘behavior’ OR ‘awareness’ OR ‘ef-
ficiency’). Controlled vocabulary and acronyms were also used, such as (‘In-home dis-
play’ OR ‘IDH’) AND (‘energy management systems’ OR ‘EMS’).
From 251 potentially relevant papers, 13 duplicates were removed, and 8 studies fo-
cused solely on policy or technical aspects were excluded during the initial screening.
2.3 Inclusion and exclusion criteria
A significant number of articles (n=205) were excluded due to the specific focus on
household-level interventions in social housing. Exclusions mainly involved studies on
non-residential settings like offices, commercial buildings, student accommodations,
hotels, and schools. Articles that did not explicitly mention social housing in their title or
abstract were also excluded. Additionally, studies on 'energy feedback interventions' or
'tailored feedback interventions' that relied on in-person feedback, rather than ICT-
based solutions, were omitted, as the review focused on the impact of technological
innovations. Studies using devices without real-time data or monitoring non-energy
metrics (e.g., gas or water) were similarly excluded. To systematically categorize the
remaining studies, a taxonomy based on five themes—general information, energy sav-
ings, effectiveness, design, and technical aspects—was developed. This framework,
shown in Fig. 1, ensured that only studies meeting the inclusion and exclusion criteria
(outlined in Table 2) were included in the final review. Studies with significant missing
data were excluded, while those with minimal gaps were retained if they were relevant
to the research question. Ultimately, this process resulted in a final sample of twenty-
five papers (n=25) for the systematic review.
Table 2. Inclusion and exclusion criteria
Focus
Inclusion
Exclusion
Household group
Social housing
Private households, retirement homes, ho-
tel rooms, commercial, offices, etc.
RTEF type
All mediums consid-
ered including in-home
displays (IDH), web-
based displays, mobile
apps, text messages.
Automation devices triggered by feedback
mechanisms or the absence of a techno-
logical device.
Duration
Long-term, and Short-
term
NA
Energy savings
Demonstrate % sav-
ings electricity
Water consumption
Locations
NA
NA
Paper types
Journal articles
Conference papers or proceedings, if
missing data is too high and does not
gather the PICO elements of the question.
Study types
Longitudinal, Random-
ized Controlled Trials,
Observation.
Theoretical models, simulations, lab exper-
iments, simulations.

Fig. 1. Taxonomy for feedback characteristics used for analysis.
2.4 Quality Assessment and Risk of Bias (ROB)
The GRADE (Grading of Recommendations, Assessment, Development, and Evalua-
tions) system was employed to assess evidence quality in this review. Downgrading
factors included study design (observational, longitudinal, or RCTs), risk of bias (e.g.,
intervention length, data completeness), inconsistency (e.g., heterogeneity, confound-
ing variables), imprecision (e.g., small sample sizes), and publication bias (e.g., selec-
tive reporting). Upgrading factors included large effect (significant outcome impacts),
detailed system description (comprehensive system information), directness (rele-
vance to the target population), and transparency and replicability (clear, replicable
study descriptions). Based on these factors, evidence quality was categorized into four
levels: Very Low, Low, Moderate, and High. Among the 25 papers reviewed, two were
classified as high quality due to their comprehensive information and the use of ran-
domized controlled trials (RCTs), which are considered the highest level of evidence
(Guyatt et al., 2000). The remaining 23 papers were rated as moderate quality based
on the evaluation criteria outlined in Table 3. This classification primarily resulted from
issues related to potential bias and incomplete information, which complicated the as-
sessment. For example, it was often unclear whether participants were fully informed
about the range of actions they could take or if the focus was solely on tool usage,
which could impact the perceived effectiveness of interventions.
Moreover, several studies were conducted during specific periods, such as winter, with-
out comparing behavioral changes across different seasons, raising concerns about
the generalizability of the findings. Some studies also included distributed energy re-
sources (DERs), such as solar panels and energy storage systems, which complicated
the evaluation of the specific contribution of real-time energy feedback (RTEF) sys-
tems. Recent studies might also be influenced by external factors like energy crises or
fluctuating fuel prices, which were less prevalent in earlier research. These factors must

be considered when interpreting results and assessing their relevance to current con-
ditions.
2.5 Sensitivity analysis
The review initially included only 25 full-text articles, raising concerns about the sample
size's adequacy for drawing reliable conclusions and whether expanding it could
enhance the analysis's comprehensiveness and reduce bias. Several factors limited
the sample size. First, while there is extensive research on residential energy use,
much does not focus specifically on social housing. Second, many identified
interventions in social housing lacked real-time solutions for feedback, leading to the
exclusion of numerous papers. Third, some studies focused on solar energy strategies
from a policy perspective rather than on real-time energy feedback (RTEF) systems or
management tools, such as the Varesina District project and the Smart Energy Building
Strategy, which emphasized broader strategies rather than RTEF systems. To address
these limitations, the review included studies with mixed-segment populations, where
social housing was part of the study but not the primary focus. This approach was
justified by the overlap in challenges between social housing and low-income housing,
such as fuel poverty and energy affordability. Including these studies (n=10) broadened
the scope and strengthened the review by covering a wider range of housing
environments relevant to RTEF. Further, the search strategy was revised to include a
broader set of terms and an AI tool (Connected Papers) to find related works. This led

to additional papers, though many were duplicates or focused on different aspects of
the same projects, such as the Horizon projects SINFONIA, BECA, and 3eHouses.
Grey literature, including conference papers and non-peer-reviewed sources, was also
reviewed. However, many were excluded due to insufficient information or
misalignment with the analysis framework. This review ultimately incorporated seven
additional articles, expanding the sample size to 42 papers.
Additionally, mapping housing associations in the UKI region provided insights into
interventions not documented in academic papers, offering valuable evidence on
current practices. These findings were compared with the results from the reviewed
academic articles, as illustrated in Fig. 2.
Fig. 2. Technical approach adapted from PRISMA workflow.
3 Results
3.1 Overall evidence of the effectiveness of RTEF interventions
The review identified 42 relevant case studies with significant variability on energy sav-
ings, ranging from ≤ 5% to ≥ 20%. This wide range reflects differences in contextual
factors from 19 geographic locations, each of which employed varying methodologies
and sample sizes. Interventions that combined multiple strategies—such as social com-
parisons, eco-feedback, and monetary incentives—were found to be the most effective.
Six studies that conducted simultaneous pilot interventions in multiple countries re-
vealed further variability based on cultural and environmental contexts. The study du-
ration and population size were also critical factors. Short-term interventions showed
higher immediate savings, often due to economic incentives, while long-term studies
offered better insights into the sustainability of energy savings. Additionally, the Real-
Time Energy Feed-back (RTEF) systems studied differed in features, functionalities,
and user interfaces, affecting user engagement and intervention effectiveness.

3.2 Determinants of RTEF Effectiveness
Our analysis identified five key determinants influencing the effectiveness of RTEF
systems in promoting energy-saving behaviours: Physical, Cultural, Strategic,
Onboarding, and External factors. A holistic approach is essential for RTEF success,
yet many studies focus on isolated factors. Future research should consider the com-
plex interplay of these determinants.
Determinant
Influence
Physical
Building characteristics like insulation limits the potential for energy
savings. Even with effective RTEF, poorly insulated buildings reduce
energy reduction potential.
Cultural Factors
Socio-economic status, energy consumption habits, and behavioural
norms, play a crucial role. For example, social housing occupants in
different regions demonstrated varying degrees of responsiveness to
RTEF, often linked to their economic constraints or cultural attitudes
toward energy conservation.
Strategic
Approach
Integrating eco-feedback and social comparisons were consistently found
to enhance long-term engagement. These strategies can turn short-term
behavioural changes into sustainable habits by creating a sense of
competition or communal effort.
Onboarding
Early user involvement in the design process, is essential for maximizing
engagement, especially among vulnerable groups like the elderly or those
with limited technological skills. For instance, the 'EnergyCat' project
showed that insufficient resident engagement led to limited behavioural
changes, highlighting the importance of inclusive design strategies
(Hafner et al., 2020).
External Factors
security concerns, environmental issues, and market forces, can impede
energy-saving behaviors. Malik et al. (2020) reported that privacy con-
cerns hindered the use of natural ventilation in low-income housing in In-
dia, while Elsharkawy et al. (2015) noted that rising energy prices in some
regions undermined energy-saving efforts, despite increased awareness.
3.3 Key findings and literature gaps
A) Long-Term vs. Short-Term Focus: Existing studies predominantly focuses on
short-term outcomes, with only 55% extending beyond one year. This makes it difficult
to assess the sustainability of energy-saving behaviors, as short-term interventions
may be influenced by economic incentives or observational biases like the Hawthorne
effect, where participants alter behaviour due to awareness of observation. Further re-
search is needed to assess long-term impacts and rebound effects, which according to
the UK Energy Research Centre, could offset 10–30% of energy savings.
B) Utilitarian Focus: RTEF systems are primarily designed to reduce energy
consumption and costs, but their broader social and environmental impacts are often
overlooked. Future studies should explore how RTEF can enhance not only energy
savings but also social interactions, environmental awareness, build agency and
community resilience.
C) Novelty vs. Effectiveness: There is no clear correlation between the novelty of
RTEF technology and its effectiveness. The impact of RTEF depends more on its im-
plementation context and user engagement rather than technological sophistica-
tion. Studies showing significant savings, up to 50%, were linked to local energy
production and active participant engagement. This is an important finding, suggesting
that future research should focus on user-centric design and real-world applications,
particularly in decentralized energy systems and renewable energy integration.

4 Discussion
Our review revealed that the top 10% of cases with the highest energy savings (up to
50%) involved micro-generation with solar PV, indicating that RTEF is increasingly
playing a role in the energy transition. However, these successful interventions are pre-
dominantly from Italy and Belgium, with none reported in the UK. This highlights a sig-
nificant gap in the literature on the implementation of RTEF systems in the UK, partic-
ularly regarding their role in decentralized energy generation within social climates. It
is crucial to consider the non-feedback aspects of these technologies—specifically,
their alignment with emerging energy market developments. For instance, while decen-
tralized energy generation and low-carbon technologies are becoming central to energy
strategies, their adoption in social housing remains limited. This is concerning given the
potential of RTEF in these contexts not only to drive significant energy savings but also
to strengthen social capital and foster community involvement. We conducted an addi-
tional search of energy efficiency projects among UK Social Housing Associations, and
we identified only one out of twenty (Brixton Energy) that actively promotes the integra-
tion of renewable energy. This association reported higher reductions in energy bills,
increased energy awareness, knowledge transfer, local employment, and enhanced
energy resilience compared to others. In 2022, the UK Government announced a solar
PV scheme for 20,000 social housing properties. This initiative offers a critical oppor-
tunity for future research to explore how social landlords are supporting tenants in this
transition—whether by providing real-time monitoring tools for solar power or sharing
knowledge on self-generated electricity. As the solar industry expands, it is essential to
assess its inclusivity in retrofitted social housing scenarios. This review identifies a new
context where RTEF systems could play a pivotal role in promoting renewable energy
adoption in social housing. However, several questions remain about their role in
retrofitted environments with energy generation and storage. For example, how do
households adapt to RTEF systems after installing solar PV? Do landlords provide
support by offering these tools for energy management? Does this integration add
complexity? What are the social and psychological impacts? Additionally, how does
user engagement fluctuate with seasonal changes, such as increased motivation to
use RTEF in the summer when energy production is higher compared to winter? Mov-
ing beyond the current focus on energy feedback displays and awareness, it is neces-
sary to reflect on the broader impact of RTEF in the evolving landscape of electricity
supply and new pricing.
5 Conclusion
Our study shows that while RTEF systems have limitations, they offer significant po-
tential for energy savings in social housing. However, to fully realize their benefits, there
must be a shift from a utilitarian focus on immediate energy savings to a broader un-
derstanding of their role in reshaping energy cultures, particularly as energy landscapes
evolve. A myopic focus risks overlooking the broader contributions of RTEF in energy
transitions. Our research suggests that integrating RTEF with renewable energy sys-
tems allows households to engage more directly in the energy transition, not only re-
ducing energy costs but also increasing energy literacy and social capital. While much
of the literature emphasizes RTEF's role in energy savings and awareness, our findings
highlight its true value in fostering active energy participation and contributing to energy
transitions. Moreover, our study highlights a significant gap in the understanding of the
social benefits of RTEF, emphasizing that our initial question has only been partially
answered, as most research has focused primarily on the utility of these systems. This
presents a valuable opportunity for future research, especially in understanding how
RTEF can empower residents and contribute to sustainable growth.
As the energy systems transform, it’s crucial to expand RTEF research to examine its
potential as an enabler of energy citizenship and its capacity to foster a more engaged

and environmentally conscious society. While RTEF alone cannot achieve comprehen-
sive energy efficiency, its integration with renewable energy sources and decentralized
energy systems could significantly impact social housing's role in the broader energy
transition. Future studies should explore the integration of RTEF in retrofitted contexts
that incorporate solar PV or battery storage technologies, as well as investigate the
lived experiences of households using these systems, which remain underexplored.
Additionally, research should examine whether these renewable energy resources in-
troduce complexities in the use of RTEF for managing energy resources in social hous-
ing.
Acknowledgments. This review is part of an on-going research of Ph.D. student Edgar
Gomez, under the supervision of Dr. Kate Carter and Dr. W. Victoria Lee.
Disclosure of Interests. The authors have no competing interests to declare that are
relevant to the content of this article.
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