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real-time data analytics, and automation capabilities to provide accurate insights into energy consumption patterns (Iris and Lam 2019). According to Bohlmann and Inglesi-Lotz (2018), homeowners may use this information to make educated decisions, such as adjusting thermostat settings, optimising lighting, and scheduling equipment operation during periods of lower power consumption. Implementing EMSs leads to financial gains for homes via reduced energy costs and contributes to broader environmental sustainability goals by reducing overall energy use and minimising carbon emissions (Sulaiman et al. 2020).
Various studies highlight the advantageous impacts of including EMSs in residential structures. Carreiro et al. (2017) emphasise the significance of integrating EMSs as they lead to significant decreases in energy use and encourage an eco-friendly lifestyle. Furthermore, Siano and Sarno (2016) emphasise the significance of EMSs in enhancing the stability and reliability of the electricity grid via the active involvement of residential customers in demand- response programs. The adoption of EMSs is further expedited by government incentives, increased awareness of environmental concerns, and the growing availability of user-friendly smart home devices. The growing incorporation of intelligent technology in residential structures is anticipated to influence advancing energy efficiency and sustainability substantially (Bohlmann et al. 2016). Within this framework, the extensive use of EMSs is expected to have a crucial impact.
Materials and methods
This study used a qualitative research approach adopting a systematic literature review. This approach comprehensively analyses and incorporates up-to-date information on EMSs, with a special focus on residential construction. The systematic literature review utilises a rigorous and systematic approach to identify, assess, and integrate important concepts, methods, and findings from selected journal articles (Kitchenham 2004). The search keywords adopted were ‘energy management’ and ‘residential’. The databases used were Energies, Emerald Insight, ScienceDirect, IEEE Xplore, and SpringerLink.
Figure 1 shows the publication retrieval process. Adhering to the standards outlined by Kitchenham (2004), a data extraction procedure was used to discover pertinent information from the 112 chosen papers. The data extraction method included the following stages: initially, the researchers used the Mendeley reference manager to extract a database of articles. They then meticulously documented the pertinent details of the concepts, contributions, and discoveries of each of the 112 studies in a spreadsheet. Data gathered from each article included the review date, title, authors, references, database, and relevance to the subject. The researcher carefully examined the publication and eliminated studies that did not meet the specific exclusion criteria outlined in Figure 1. Ultimately 112 papers were chosen for a thorough and critical evaluation. Data retrieved from these papers was analysed using thematic analysis.
Identification of studies via databases and registers
records screened (n=19, 149) records excluded (n=9,000) reportescsoorudgshsctrfeoernretdri(env=a1l9(n, 1=4190), 149) records not retrieved (n=5, 149)
reporrtsecaocrcdessecdrefeonredlig(nib=i1li9ty, 1(n49=)5,000)
Figure 1: Data retrieval process
Proceedings of the conference on Public innovation, develoPment and sustainability | 77
Identification
Screening
records identified from thorough Scopus database searching energy management and residential (n=9,329)
records identified from thorough google Scholar (gS) database searching energy management and residential (n=9,820)
reports excluded: reason 1 (2,118 = not relevant to study)
reason 2 (2,770 = non-peer- reviewed sources)
Included
Studies included in review (n=112) reports of included studies (n=112)