Abstract
Purpose
This study aims to examine the challenges in the implementation of energy management systems in residential buildings to lower the running cost and achieve a better energy-efficient building.
Design/methodology/approach
This study adopted a mixed research method. Quantitative data was gathered by issuing a research questionnaire to 20 Delphi experts, while qualitative data was acquired through a Systematic Literature Review. Data received was analyzed using the descriptive analysis method.
Findings
The findings revealed that the main barriers to incorporating energy management systems (EMSs) in residential buildings consist of a lack of awareness of energy management systems, lack of management commitment to energy management, lack of knowledge about energy management systems, lack of funds for energy management systems, resistance to energy management technology by the property owners and property managers, distrust and resistance to energy management technology by the property owners, high initial cost of energy management technologies, shortage of technicians for energy management technologies, the nonexistence of local manufacturers of energy management equipment, lack of incentives for efficient energy management and high repair costs of energy management technologies.
Research limitations/implications
The specific focus on residential buildings may limit the applicability of findings to commercial or industrial sectors. Further research is warranted to accommodate other energy-consuming sectors.
Practical implications
People’s perceptions, either wrong or correct, affect their ability to make an informed decision to adopt energy management systems, denying them the opportunity to reap the associated benefits. Therefore, there is an urgent need for the residential industry stakeholders and the government to increase educational opportunities for property owners, managers and property tenants on the importance of energy management systems.
Originality/value
This research presents the potential obstacles and problematic areas that residents may encounter while using these energy management systems. Consequently, they will be able to make a well-informed choice when installing energy management systems. Moreover, the research elucidates the identification of novel perspectives and also unexamined obstacles that impede the widespread use of energy management systems in residential buildings.
Keywords
Citation
Khafiso, T., Aigbavboa, C. and Adekunle, S.A. (2024), "Barriers to the adoption of energy management systems in residential buildings", Facilities, Vol. 42 No. 15/16, pp. 107-125. https://doi.org/10.1108/F-12-2023-0113
Publisher
:Emerald Publishing Limited
Copyright © 2024, Thabo Khafiso, Clinton Aigbavboa and Samuel Adeniyi Adekunle.
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The escalating apprehension over climate change and the surging need for energy has emphasized the pivotal significance of energy efficiency in residential structures. Energy Management Systems (EMS) are now recognized as crucial instruments for improving energy efficiency, decreasing consumption and minimizing the environmental impact of residential areas (Smith et al., 2022; Rathor and Saxena, 2020). These systems use technology to oversee, manage and enhance the utilization of energy inside buildings, providing substantial advantages in terms of financial savings and ecological preservation (Yang et al., 2018). Although residential structures can embrace EMS, they encounter many obstacles. Multiple studies have found obstacles to the general use of EMS in residential settings. The constraints include substantial upfront expenses, limited homeowner knowledge and information, technological complexities and regulatory impediments (Camarasa et al., 2021; Samarakoon and Rajini, 2013; Hannan et al., 2018; Yousuf et al., 2024). In addition, cultural and behavioral aspects are significant, as they contribute to resistance to change and skepticism about new technologies, which further hinder the adoption process (Kyere et al., 2024).
The introduction of EMS in residential structures has been filled with difficulties throughout history. High initial expenses, intricate technology and limited customer knowledge pose significant obstacles to the implementation of this technology (He et al., 2024). In addition, regulatory frameworks and energy regulations often lack the required assistance or incentives for families to make investments in EMS (Gajdzik et al., 2024). These obstacles hinder the adoption of energy-efficient technology and the advancement toward national and international objectives for energy sustainability and climate change mitigation. The findings of a worldwide study on the adoption of residential EMS indicate that there are common obstacles to widespread implementation (Zheng et al., 2024). These difficulties reflect the variations in technological infrastructure and economic conditions among different countries (Bahloul et al., 2024).
Within the African context, specifically in South Africa, additional obstacles worsen the implementation of EMS in residential buildings. These challenges include economic inequalities, limitations in infrastructure and the lack of reliable electricity supply (Maheshwari et al., 2024). Although there are obstacles to overcome, the potential advantages of EMS in terms of energy preservation, financial savings and environmental sustainability make addressing these hurdles a crucial field of study. This study seeks to examine the obstacles that hinder the implementation of EMSs in residential buildings, based on South African perspectives. The research aims to contribute to the development of strategies and policies that might promote the broader use of EMS in residential settings. The study contributes to worldwide efforts toward energy sustainability and climate change mitigation by identifying and comprehending these hurdles.
2. Theoretical background
2.1 South African residential energy overview
The residential sector has a markedly elevated level of energy consumption in comparison to other sectors, which is a matter of concern. Hughes and Larmour (2021) discovered that the residential sector contributes to 20% of global energy usage. Within the particular context of South Africa, it is documented that nearly 86% of residents in the nation possess the ability to use electricity, equating to a total of roughly 16.9 million homes. According to the research conducted by Bohlmann and Inglesi-Lotz (2018), the housing sector in South Africa has a major role in the high levels of energy consumption. Bohlmann and Inglesi-Lotz (2018) state that energy use in domestic settings includes several activities such as heating, cooking, lighting and water heating. Due to the observed population boom, the Residential sector in South Africa has consistently shown an increase in energy demand.
Energy conservation has been a significant concern in recent decades owing to the substantial increase in population, scarcity of resources and climatic fluctuations, resulting in excessive energy use (Bohlmann et al., 2016). Bipongo and Adonis (2022) project that the world population will reach 9.2 billion by 2040, leading to a significant increase in the demand for energy in the future years. The energy industry is now facing substantial challenges due to increased energy consumption, driven by the alarming pace at which energy demand is rising (Christou, et al., 2018). The majority of energy is used in the residential sector, necessitating the implementation of energy management systems in residential structures to mitigate excessive energy usage (Jonban et al., 2021).
The present global movement toward sustainability emphasizes the urgent need to implement energy management systems in residential buildings, which are generally acknowledged for their substantial energy efficiency and consequent mitigation of adverse environmental impacts (Perez-Lombard et al., 2008). Empirical evidence has shown that the property industry in South Africa has a significant environmental footprint, accounting for up to 50% of carbon dioxide (CO2) emissions (Atkinson, 2007; Brown et al., 2007). Currently, South Africa is confronting significant energy supply challenges, with residential structures responsible for around 17.2% of the nation’s overall electricity use. This scenario emphasizes the immediate need for the adoption of effective energy management tactics (Department of Energy, 2017). Bloemfontein, a city renowned for its varied residential population and a fusion of distinct socio-economic strata, has a dearth of comprehensive studies that investigate the intricacies, efficacy and ramifications of energy management strategies in its residential structures (Kumirai, 2010). The lack of research on this topic hinders our ability to accurately describe and get a deep knowledge of energy consumption and management, particularly in residential structures. Considering the diversified population and the present load-shedding difficulties in South Africa, it is vital to address the energy consumption demands.
2.2 Concept of energy management systems in residential buildings
EMSs prioritize the optimization of energy use to enhance efficiency and reduce costs. These systems are essential in many energy consumption situations, such as industrial, commercial and residential environments. EMSs often consist of a variety of protocols and tools specially designed to monitor, control and save energy inside an organization or facility (Kaur et al., 2020). The main objective of these systems is to collect and analyze data related to energy use via the use of sensors, meters and other monitoring devices. The data is then used to ascertain repetitive patterns, identify regions of inefficiency and precisely locate feasible pathways for energy saving (Kaur et al., 2020). EMSs can independently control and manage several systems, including HVAC, lighting and equipment operation, to optimize performance and enhance energy efficiency (Bohlmann and Inglesi-Lotz, 2018). Implementing this proactive approach reduces energy costs and plays a vital role in promoting environmental sustainability by decreasing carbon footprints (Jonban et al., 2021).
Mac Nulty (2015) defines EMS as a notion that goes beyond the mere act of saving energy. For example, the process of making important decisions related to energy usage is impacted by the essential activities of the organization. EMSs enable organizations to make educated decisions on energy use, investment in energy-efficient technologies and long-term strategic planning by providing complete insights and current data. According to Wilson (2019), advanced EMSs may include renewable energy sources like solar and wind, hence aiding in the development of a more ecologically friendly and resilient energy infrastructure. Integrating renewable energy sources allows organizations to adhere to regulatory requirements and accomplish their environmental goals. According to Shamseldein et al. (2019), EMSs often include features like predictive maintenance, which allows for the forecast of potential system faults and the suggestion of preemptive actions. As a result, this strategy reduces times of idleness and reduces maintenance costs. Energy management systems are comprehensive solutions that include more than simply energy conservation. They also address economic, environmental and operational objectives (Wilson, 2019).
Residential buildings use Energy Management Systems to maximize energy utilization, guaranteeing effectiveness and long-term viability in home energy consumption. These systems use a range of elements, including sensors, smart meters and intelligent controllers, to oversee and regulate the energy usage of appliances, heating, ventilation, air conditioning (HVAC) systems and lights (Kaur et al., 2020). Residential EMS uses sophisticated algorithms and machine learning approaches to forecast energy requirements, make immediate adjustments to settings and provide practical advice to homeowners on how to save energy (Hanafi et al., 2024; Manivannan, 2024).
The main objective of EMS in residential buildings is to minimize energy use and expenses while maintaining the comfort and convenience of the residents (Alghassab, 2024). This is accomplished by the ongoing monitoring of energy consumption trends and the automated adjustment of system and appliance operations. Smart thermostats can acquire knowledge about a household’s schedule and temperature preferences (Gravert et al., 2024). They may then make adjustments to the heating and cooling systems, ensuring that they only run when necessary. Similarly, intelligent lighting systems can automatically reduce the brightness or switch off lights in rooms that are not being used (Abdolhosseini and Abdollahi, 2024). These ingenious adaptations provide a substantial contribution to energy conservation, often with minimum effort from the occupants.
Residential energy management systems are vital for improving the incorporation of renewable energy sources, such as solar panels, into household energy systems (Gunmi et al., 2024). EMS may optimize the use of produced renewable energy and decrease dependence on the grid by effectively controlling the energy flow between these sources, storage devices (such as batteries) and the home’s energy usage (Rezk and Fathy, 2024). This reduces energy costs and enhances the self-reliance of residential structures, contributing to a reduced carbon impact. In addition, some EMSs have the potential to engage in demand-response programs (Balasubramanian and Singh, 2024). These programs include adjusting energy use per grid needs, providing homes with financial incentives and contributing to the stability of the grid.
EMS in residential buildings encourages homeowners to take a more mindful and environmentally friendly approach toward energy consumption. EMS offers homeowners the ability to get immediate feedback and access in-depth energy consumption information, enabling them to pinpoint energy-intensive equipment and detect wasteful energy use trends (Yuvaraj et al., 2024). This awareness may result in making more knowledgeable choices, such as upgrading to energy-efficient equipment or altering consumption patterns. Furthermore, the incorporation of IoT (Internet of Things) technology into EMS enables a smooth and user-friendly experience, enabling homeowners to monitor and regulate their energy use remotely (Gozuoglu et al., 2024). This promotes increased involvement in energy management.
2.3 Requirements for energy management systems
Successful deployment and efficient functioning of Energy Management Systems (EMS) need a thorough set of criteria that include technological, operational and organizational factors. These standards guarantee that EMS can optimize energy utilization, decrease operating expenses and uphold sustainability objectives (Aman et al., 2013). Thus, Table 1 explores the essential criteria for an EMS across many aspects.
3. Methods
Research methodology is a means of structuring investigations aimed at identifying variables and their relationship to one another (Ivankova and Creswell, 2009). This study adopted a mixed research methodology. Mixed-method research, which integrates both quantitative and qualitative methodologies, is gaining recognition for its strength and comprehensiveness in investigating intricate phenomena. This study adopted a combination of systematic reviews with the Delphi method. A systematic review offers a thorough and impartial analysis of current research results, guaranteeing a strong empirical basis. Furthermore, the Delphi technique, which involves an iterative process and input from a panel of experts, provides a qualitative depth that may enhance and broaden the insights obtained from the systematic review. This combination enables academics to consolidate current information via a systematic review and examine emergent themes while achieving a consensus on future research areas or practical applications using the Delphi approach. For example, Pape et al. (2022) utilized this method to detect and give priority to areas of research that are lacking in environmental science. The study utilizes both approaches to offer an in-depth understanding of intricate problems.
3.1 Systematic literature review
This study used a qualitative research methodology adopting a systematic literature review (SLR) research design. SLR utilizes a rigorous and systematic approach to identify, assess and integrate important concepts, methods and findings from the selected journal articles. The search keywords adopted were: “energy management” and “residential” and “barriers”. The databases used in this paper were Energies, Emerald Insight, ScienceDirect, IEEE Xplore and SpringerLink. Figure 1 shows the publication retrieval process. Title, abstract and full-text screening was performed on the retrieved papers and 26 publications were critically assessed, and descriptive analysis was adopted.
3.2 Delphi technique
According to Abbato (2009) and, Ivankova and Creswell (2009), quantitative research employs a methodology that involves explaining the progression of a phenomenon using numerical data, while also highlighting the variations among these data points. According to Polit et al. (2001), the quantitative research technique is considered an objective and formal strategy for gathering information from a specific group of individuals through self-reporting. This study, retrieved quantitative data by utilizing the Delphi technique conducted by distant communication method, through distributing questionnaires via mail, rather than in-person group discussions. This facilitated the ability of all participants to provide individual responses, mitigating the influence of group dynamics on consensus.
The Delphi approach as adopted was characterized by many fundamental aspects, including anonymity, iterative processes with controlled feedback and the use of statistical response. The identities of the panel members are undisclosed, and they individually complete a series of questions. The repetitive nature of the approach enables the experts to revise their judgments and extend them beyond their subjective perspectives. Therefore it achieves the most optimal prediction that is derived from the collective agreement of knowledgeable individuals (Corotis et al., 1981). The procedure is iterated until an agreement is attained or until it becomes apparent that no more consensus can be obtained among the experts. Typically, the number of rounds ranges between two to seven, while the number of experts spans from three to 15 (Rowe and Wright, 1999; Adnan and Morledge, 2003).
For this study, two rounds of pertinent inquiries about barriers to the adoption of energy management systems in residential buildings was conducted. Each expert provided perspective, and the responses gathered during the first round were evaluated. Afterward, the aggregated data was sent to the experts while ensuring anonymity or non-attribution in conjunction with the follow-up inquiries. Subsequently, each expert had the opportunity to consider the viewpoints of the other participants when formulating their responses in the second round. Subsequently, this procedure was iterated for each consecutive iteration, with the expectation that by the ultimate iteration, a consensus about the presented barriers would have been attained. This was accomplished via two rounds with the anticipation that the panel of experts would eventually reach a point of saturation, resulting in a convergence of their perspectives (Aigbavboa, 2013). Hence, the study underwent a Delphi process as shown in Figure 2.
3.2.1 Expert selection.
The participants in this study consisted of researchers specializing in residential energy, property owners and property managers. A summary of their criteria may be found in Table 2. These conditions were implemented and deemed adequate for selecting an expert for the study. It offers a well-rounded and diverse group of professionals from both the industry and academics. Individuals who met these criteria were contacted and selected to participate in the research, while those who did not meet the criteria were eliminated. The study included a group of 20 experts who took part in two rounds of the Delphi, as shown in Table 3.
3.2.2 Delphi adoption by the study.
A survey consisting of 10 barriers was sent to a total of 20 experts on July 15, 2023. The experts were requested to evaluate the 10 variables using a 10-point Likert scale based on their level of agreement. The results of the first round were received by July 20, 2023. Upon receiving the first round of findings, they were examined and the experts were provided with the overall ratings from the first round. They were then asked whether they wanted to modify their original ratings to reach a consensus. None of the experts made any modifications during the first round, indicating that early consensus was reached. The questionnaire for the second round was sent on July 25, 2023, and the expert responses were received by August 5, 2023. It is important to mention that none of the experts altered, revised or adjusted their initial evaluations during the second round of the Delphi process. No experts withdrew from the research, and all experts participated in both rounds of the Delphi. Participants in the first round had already achieved a firm agreement, which limited the possibility of change in the following rounds. This outcome may have been influenced by their shared backgrounds, experiences and opinions. Continuing with the third round was unnecessary at this stage since the research had already hit data saturation.
4. Qualitative findings
Energy management systems are becoming increasingly important in the current industrial revolution; however many property owners, residents and property managers struggle to adopt them due to a variety of reasons. The identified barriers provide insight into the various hurdles that have hampered the adoption of EMSs through SLR. These hurdles make it impossible for stakeholders to adopt EMS, it also prolongs the adoption period if left unresolved. Table 4 provides the various barriers and the source as identified in the literature (compiled by the author).
5. Quantitative findings
5.1 Delphi round one
The level of agreement regarding the barriers to the adoption of energy management systems for residential buildings seemed to be positive among the experts as shown in Table 5. This is because of “the nonexistence of local manufacturers of energy management equipment” and the shortage of technicians for energy management technologies as the factors, the experts seemed to have a good positive consensus while the remaining variables, the experts seemed to have a strong positive impact as their mean were all above 7.
5.2 Delphi round two
Based on Table 6, the experts strongly agreed that these were major obstacles, as evidenced by the mean scores for “Lack of knowledge about energy management systems” and “Lack of awareness of energy management systems,” which are 9.05 and 8.65, respectively. With means of 8.6 and 8.3, respectively, the items “Lack of funds for energy management systems” and “The high initial cost of energy management technologies” also receive high scores, showing financial limitations as a key barrier. Both the absence of managerial commitment and the lack of incentives receive high ratings, indicating the need for organizational and policy-level adjustments. Despite having a high mean, the standard deviation is considerable (about 8 for most variables), indicating a broad variety of viewpoints. This may imply that although there is overall agreement, there are differences in respondents’ levels of agreement. Since the responses are closely clustered around the median and the interquartile range (IQR) for “Lack of knowledge” and “Lack of awareness” is quite low (1.75 and 3.5), this supports the notion that there is widespread agreement about these barriers. The IQR for “The high initial cost” is 4.5, while the IQR for “Lack of funds” is 3.5, indicating a more considerable range in how strongly experts feel about these difficulties. Most barriers have a mode of 0, which appears to indicate that there is not a particular value that is repeated the most often. The “absence of local manufacturers” and the “paucity of technicians,” on the other hand, have a mode of 1 and 3, respectively. Among all the variables, the percentile is above 80%, highlighting a very strong consensus on all barriers by the experts.
6. Discussion of the findings
6.1 Qualitative findings
For energy management systems to be implemented in residential buildings, research revealed that there are barriers that need to be addressed. The barriers consist of the following lack of awareness of energy management systems to the property managers, owners and even the end users and this finding is supported by the findings of the study conducted by Hassan et al. (2017); Qazi et al. (2019); Shrouf and Miragliotta (2015); and Schulze et al. (2016). This hinders EMSs due to a lack of knowledge by the implementers of EMS in residential spaces. According to the literature, lack of management commitment to energy management seems to also act as a barrier to the adoption of EMS in residential buildings (Kaipainen, 2015; Arinaitwe et al., 2023). The absence of firm commitment from management toward energy management may be a major obstacle to its adoption. This is because it often results in an inadequate allocation of resources, absence of strategic planning and ineffective execution of energy efficiency programs within organizations.
Lack of knowledge about energy management systems is also a barrier determined by the study undertaken by Hillary (2004), Rastegar et al. (2018) and Chai and Yeo (2012). Insufficient understanding of energy management systems might impede their adoption by giving rise to misunderstandings about expenses, advantages and operational prerequisites, eventually leading to resistance or apathy toward installing these systems. Findings by Balta-Ozkan et al. (2013) and Gajdzik et al. (2024) outline that lack of funds for energy management systems is a barrier to EMS adoption in the residential sector. The absence of sufficient money for energy management systems might hinder their adoption since the initial cost of these technologies may be seen as excessively costly, dissuading organizations or people from investing in them despite the potential for long-term savings.
Chai and Yeo (2012) and Sooriyaarachchi et al. (2015) argue that the shortage of technicians for energy management technologies is the factor preventing EMS implementation. This is because the finding limits the availability of expert installation, maintenance and troubleshooting services, critical for the efficient operation and optimization of these systems. The nonexistence of local manufacturers of energy management equipment also plays a huge role in preventing EMS adoption in the residential building according to the study by Boadu and Otoo (2024) and Mengelkamp et al. (2018), since there is not available EMS equipment within the reach of the end users.
The implementation of energy management systems encounters substantial obstacles as a result of a confluence of economic and policy-related impediments. The absence of incentives, such as subsidies or tax advantages, for effective energy management discourages prospective users from investing in these technologies, reducing their financial appeal (Brunke et al., 2014; Zipperer et al., 2013). Moreover, the substantial initial expense associated with energy management technology is a significant barrier according to Ilojianya et al. (2024), Rawa et al. (2024) and Kumar and Jain (2024). The upfront investment needed may discourage people and organizations from embracing these systems despite their potential for long-term cost savings and environmental advantages. Moreover, the elevated expenses associated with repairing energy management technology worsen the problem by augmenting the overall cost of ownership, hence diminishing the attractiveness of using such systems according to Elkholy et al. (2024) and Tie and Tan (2013). These issues lead to a challenging environment for the general acceptance and implementation of energy management systems, highlighting the need for specific financial incentives and support mechanisms to reduce obstacles to adoption.
The lack of trust and reluctance toward energy management technology among property owners, along with the restricted accessibility of these systems, provide substantial obstacles to the implementation of energy management systems (EMS) in residential buildings, according to Barai (2024), Mayer and Parks (2024) and Gutierrez-Martinez et al. (2019). The skepticism of property owners is often based on worries about the intricacy, dependability and initial expenses of EMS. Additionally, Aman et al. (2013) state that there are apprehensions about data security and privacy because to the considerable data collecting and monitoring required for these systems to operate well. These concerns are exacerbated by the restricted selection of energy management solutions offered on the market, which might limit options for customers and discourage them from using such systems. In addition, concerns over security and privacy, such as the unauthorized retrieval of energy consumption data, intensify the opposition, as property owners fear the possible abuse of their personal information. The combination of these problems creates a difficult environment for the general adoption and use of EMS technology, requiring specific actions to address these concerns and advocate for the advantages of energy management systems.
6.2 Quantitative findings
The results show that the biggest obstacles to implementing energy management systems in residential buildings are ignorance and lack of understanding. Tuomela et al. (2021) assert that EMSs are a recent development, and the market is still in the early stages of establishment. The adoption of EMSs is hindered by the limited dissemination of these systems, as well as by householders’ lack of awareness, ignorance and knowledge (Tuomela et al., 2021). Financial limitations, like little accessible finances and significant upfront fees, provide other vital difficulties. The research conducted by Balta-Ozkan et al. (2013) and Hassan et al. (2017) suggests that a shortage of financial resources is a significant obstacle to the implementation of energy management systems (EMSs) in residential buildings.
Operational and technical impediments, such as a lack of management commitment, a lack of incentives and high maintenance costs, are significant but are thought to have a little less influence than awareness and financial issues. The study by Tuomela et al. (2021) supports the notion that operational and technical obstacles, such as insufficient managerial dedication, lack of incentives and expensive maintenance expenses, are relevant. However, they are believed to have less impact than awareness and financial concerns. Using this information, stakeholders may create targeted interventions that can hasten adoption. The adoption of energy management systems in the residential sector is hindered by several significant financial difficulties, including the high cost of energy management systems and high repair costs of energy management technologies, as Ndeke and Adonis (2020) asserted.
The adoption of energy management systems within properties is significantly hindered by economic and psychological barriers. On the one hand, the lack of incentives for efficient energy management, such as financial rebates, tax deductions or utility discounts, means that property owners often fail to see a tangible, immediate benefit from investing in these systems, making the initial cost seem unjustifiable despite potential future savings. This finding is supported by the study undertaken by Backlund et al. (2012), which states that incentivizing energy-efficient practices can enhance energy management adoption in the residential sector. On the other hand, some property owners have a palpable distrust and resistance toward energy management technology. Koirala et al. (2018) state that about 29% of property managers and owners do not rust energy management systems. This resistance can stem from various sources, including skepticism about the technology’s effectiveness, concerns over privacy and data security and the discomfort associated with adapting to new systems and changing long-standing energy usage behaviors. Together, these factors create a formidable barrier to the wider adoption of energy management systems, as they challenge the economic rationale for such an investment and tap into deeper issues of trust and behavioral change.
The adoption of energy management is heavily impacted by the industrial capacity and workforce skills in a given area. One key hurdle to adoption is the lack of local manufacturers of energy management equipment and a paucity of technicians with expertise in energy management technology. According to Shrestha et al. (2019), the lack of local energy management system manufacturing facilities might result in higher prices and limited availability of these technologies. This is due to the cost of importing them and the logistical difficulties involved. The scarcity of proficient specialists who can install, maintain and optimize these systems worsens the problem. This increases the expenses of services and impacts the dependability and effectiveness of energy management solutions in use. These obstacles highlight the urgent need for funding in local manufacturing capacities and vocational training initiatives to facilitate the extensive implementation and efficient utilization of energy management technology, as recommended by industry experts (Thollander and Palm, 2012).
These elements contribute to a thorough knowledge of the many challenges that impede advancement. The implication of these findings have broader significance beyond the realm of academic discourse. They provide valuable insights for policymakers, industry experts and technology developers, shedding light on the significant challenges that must be overcome to facilitate the widespread implementation of energy-efficient practices in residential structures. The results of this study have the potential to inspire specific initiatives, such as modifications to policies, implementation of financial incentives or launch of educational programs, to address and overcome the identified barriers. In essence, this study serves as a valuable addition to the existing body of academic knowledge while simultaneously making a significant contribution to the broader conversation around sustainable living and the urgent need to improve energy efficiency in residential buildings. It has the potential to inspire constructive transformations in both policy-making and practical implementation.
7. Conclusion
One notable obstacle that has been observed pertains to the limited knowledge and comprehension among homeowners about the advantages and functionality of Energy Management Systems (EMS). The lack of understanding in this area contributes to hesitancy in embracing these systems despite their capacity for energy conservation and enhanced effectiveness. One significant challenge that arises is the initial financial investment required for the installation and integration of Energy Management Systems (EMS), which residents sometimes see as being excessively costly. The economic aspect, in conjunction with a prevailing absence of incentives or official backing, serves as an additional deterrent for citizens to adopt these systems. Furthermore, the study highlights the presence of technological intricacies and the lack of localized assistance and experience as further obstacles.
To surmount these obstacles, the study has put several suggestions forward. To begin with, it is essential to implement extensive awareness campaigns and educational initiatives to enlighten communities about the enduring economic and environmental advantages associated with the adoption of EMSs. Local government and energy authorities must extend support to these projects to preserve their credibility and maximize their impact. Additionally, implementing financial incentives, like subsidies, tax refunds or low-interest loans, may effectively mitigate the economic constraints faced by homeowners, hence enhancing the viability of adopting EMSs. Furthermore, establishing and cultivating indigenous knowledge and assistance networks to the implementation and upkeep of EMSs may mitigate technical obstacles and foster a more conducive atmosphere for end-users. In conclusion, the participation of community leaders and influencers in promoting EMSs may cultivate a more responsive and favorable disposition toward these systems, hence encouraging a community-oriented approach to sustainable energy management in residential structures within the Bloemfontein region. This study results in assisting policymakers in achieving efficiency, sustainability, clean energy and sustainability goals. Thereby contributing to a resilient future.
Figures
Figure 1.
Publication retrieval
Figure 2.
Framework for the Delphi study
Criteria for the energy management systems
| Criteria | Description | Author(s) |
|---|---|---|
| Real-time monitoring | Data on energy consumption must be provided by the system at various temporal intervals (for instance every 15 minutes, every hour, every day and every week). This enables the end-users to relate near real-time data with their energy usage behaviors | Arboleya et al. (2015); Zhou et al. (2016) |
| Disaggregation of data | Providing disaggregated data for various appliances can assist energy consumers who frequently have misconceptions about the amount of energy used by specific appliances. Information about the effects of turning on or off a specific appliance in real-time can be very helpful to consumers. The provision of disaggregated data also accentuates the effects of long-term adjustments, such as switching to an energy-efficient appliance. Numerous EM systems employ indirect load sensing techniques to deliver disaggregated data based on unique current and voltage waveform “signatures” of individual appliances | Aman et al. (2013) |
| Availability and accessibility | The system must provide the consumer with constant access to the information via an intuitive interface, whether it be a physical device or a web or mobile portal that also allows remote access to the data. EM systems may also employ push technology to deliver urgent notifications to users’ mobile devices or computer screens | Elzabadani et al. (2005); Sekhar et al. (2022) |
| Data integration | In addition to current energy usage, EM systems must incorporate additional types of data, including ambient temperature, humidity, acoustics and light, as well as consumer historical data, usage information for various appliances and peer consumption information | Majdi et al. (2022) |
| Affordability | It should be simple to configure and maintain. It ought to be inexpensive to operate and use little energy. These elements facilitate widespread adoption by lowering the entry barrier to the system | Shah et al. (2013; Zhou et al. (2016) |
| Control | Devices should be under the system’s control remotely, automatically and according to programming. In general, the consumer must carry out necessary control operations manually. However, automatic actions or a digital control option are more efficient | Beaudin and Zareipour (2015); Kusakana (2017); Zhou et al. (2016) |
| Cyber security and privacy | Security issues arise when EM systems transmit data and control signals. The disclosure of consumer personal consumption profiles raises privacy concerns as well. The system must authenticate all transactions to guarantee the security of user data and control functions and prevent unauthorized access by third parties | Sayed and Gabbar (2018) |
| Data intelligence and analytics | The intelligence component is a desirable trait in modern EM systems. In addition to having little time, consumers frequently lack a thorough understanding of electrical systems. The system should take intelligent actions to balance energy consumption and consumer comfort. For this, it may be necessary to use methods from machine learning, human-computer interaction and big data analytics to identify usage patterns and suggest possible courses of action. By doing this, consumers are relieved of the constant burden of directly operating and controlling every appliance | Aman et al. (2013) |
Source: Authors’ own creation
Expert selection requirements
| S/N | Researchers | Property managers/owners |
|---|---|---|
| 1 | Published more than four energy management-related papers | Having managed/owned the property for more than 3 years with more than 100 residents per year |
| 2 | Having an energy/electrical engineering qualification | Capacity and willingness to participate |
| 3 | Capacity and willingness to participate | Sufficient time to participate |
| 4 | Sufficient time to participate | Having sufficient records of energy usage for previous years |
| 5 | Having more than 3 years of experience in the energy industry |
Authors’ own creation
List of the panel of experts who participated in the Delphi study
| Round 1 experts | Round 2 experts | ||
|---|---|---|---|
| Expert | No. | Expert | No. |
| Researchers | 5 | Researchers | 5 |
| Property managers | 9 | Property managers | 9 |
| Property owners | 6 | Property owners | 6 |
| Total | 20 | Total | 20 |
Source: Authors’ own creation
Barriers to energy management systems in residential buildings
| Barriers | References |
|---|---|
| Lack of awareness of energy management systems | Hassan et al. (2017); Qazi et al. (2019); Shrouf and Miragliotta (2015); Schulze et al. (2016) |
| Lack of management commitment to energy management | Kaipainen (2015); Arinaitwe et al. (2023) |
| Lack of knowledge about energy management systems | Hillary (2004); Rastegar et al. (2018); Chai and Yeo (2012) |
| Lack of funds for energy management systems | Balta-Ozkan et al. (2013); Gajdzik et al. (2024) |
| Shortage of technicians for energy management technologies | Chai and Yeo (2012); Sooriyaarachchi et al. (2015) |
| The nonexistence of local manufacturers of energy management equipment | Boadu and Otoo (2024); Mengelkamp et al. (2018) |
| Lack of incentives for efficient energy management | Brunke et al. (2014); Zipperer et al. (2013) |
| The high initial cost of energy management technologies | Ilojianya et al. (2024); Rawa et al. (2024); Kumar and Jain (2024) |
| High repair costs of energy management technologies | Elkholy et al. (2024); Tie and Tan (2013) |
| Distrust and resistance to energy management technology by the property owners | Barai (2024); Mayer and Parks (2024) |
| Limited availability of energy management systems | Gutierrez-Martinez et al. (2019) |
| Lack of security and privacy | Aman et al. (2013) |
Authors’ own creation
Delphi round one findings
| Barriers | Mean | SD | Interquartile range (IQR) | Mode | % Disagree | % Agree |
|---|---|---|---|---|---|---|
| Lack of knowledge about energy management systems | 9.05 | 8.65 | 1.75 | 0 | 5 | 95 |
| Lack of awareness of energy management systems | 8.65 | 8.24 | 3.5 | 0 | 0 | 100 |
| Lack of funds for energy management systems | 8.6 | 8.24 | 3.5 | 0 | 30 | 70 |
| Lack of management commitment to energy management | 8.5 | 8.12 | 2.75 | 0 | 5 | 95 |
| The high initial cost of energy management technologies | 8.3 | 7.93 | 4.5 | 0 | 10 | 90 |
| Lack of incentives for efficient energy management | 8.25 | 7.88 | 4.5 | 0 | 5 | 95 |
| Distrust and resistance to energy management technology by the property owners | 8.2 | 7.94 | 3.75 | 0 | 0 | 100 |
| High repair costs of energy management technologies | 7.75 | 7.50 | 2.5 | 0 | 5 | 95 |
| The nonexistence of local manufacturers of energy management equipment | 6.9 | 6.68 | 1.75 | 1 | 5 | 95 |
| Shortage of technicians for energy management technologies | 6.65 | 6.59 | 1.75 | 3 | 20 | 80 |
Authors’ own creation
Delphi round two findings
| Barriers | Mean | SD | Interquartile range (IQR) | Mode | % Disagree | % Agree |
|---|---|---|---|---|---|---|
| Lack of knowledge about energy management systems | 9.05 | 8.65 | 1.75 | 0 | 5 | 95 |
| Lack of awareness of energy management systems | 8.65 | 8.24 | 3.5 | 0 | 0 | 100 |
| Lack of funds for energy management systems | 8.6 | 8.24 | 3.5 | 0 | 30 | 70 |
| Lack of management commitment to energy management | 8.5 | 8.12 | 2.75 | 0 | 5 | 95 |
| The high initial cost of energy management technologies | 8.3 | 7.93 | 4.5 | 0 | 10 | 90 |
| Lack of incentives for efficient energy management | 8.25 | 7.88 | 4.5 | 0 | 5 | 95 |
| Distrust and resistance to energy management technology by the property owners | 8.2 | 7.94 | 3.75 | 0 | 0 | 100 |
| High repair costs of energy management technologies | 7.75 | 7.50 | 2.5 | 0 | 5 | 95 |
| The nonexistence of local manufacturers of energy management equipment | 6.9 | 6.68 | 1.75 | 1 | 5 | 95 |
| Shortage of technicians for energy management technologies | 6.65 | 6.59 | 1.75 | 3 | 20 | 80 |
Authors’ own creation
References
Abbato, S. (2009), “Book review: foundations of mixed methods research: integrating quantitative and qualitative approaches in the social and behavioral sciences”, Evaluation Journal of Australasia, Vol. 9 No. 2, pp. 60-63, doi: 10.1177/1035719x0900900213.
Abdolhosseini, M. and Abdollahi, R. (2024), “Designing and implementing a lighting control system based on constrained info-fuzzy, to save energy and satisfy users”, IEEE Transactions on Industrial Informatics, Vol. 20 No. 4.
Adnan, H. and Morledge, R. (2003), “Application of Delphi method on critical success factors in joint venture projects in the Malaysian construction industry”, 1st Scottish Conference for Postgraduate Researchers of the Built and Natural Environment, Glasgow Caledonian University, Glasgow, Scotland, pp. 41-49.
Aigbavboa, C.O. (2013), “An integrated beneficiary-centred satisfaction model for publicly funded housing schemes in South Africa”, University of Johannesburg (South Africa).
Alghassab, M.A. (2024), “Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: a comparative study”, Energy Reports, Vol. 11, pp. 1212-1224.
Aman, S., Simmhan, Y. and Prasanna, V. (2013), “Energy management systems: state of the art and emerging trends”, IEEE Communications Magazine, Vol. 51 No. 1, pp. 114-119, doi: 10.1109/MCOM.2013.6400447.
Arboleya, P., Gonzalez-Moran, C., Coto, M., Falvo, M.C., Martirano, L., Sbordone, D., Bertini, I. and Pietra, B.D. (2015), “Efficient energy management in smart micro-grids: zero grid impact buildings”, IEEE Transactions on Smart Grid, Vol. 6 No. 2, pp. 1055-1063, doi: 10.1109/TSG.2015.2392071.
Arinaitwe, A., Bagire, V., Tukamuhabwa, B. and Sulait, T. (2023), “Energy management in small and medium manufacturing firms: examining the enhancing role of top management commitment in a developing country context”, International Journal of Energy Sector Management, Vol. 18 No. 5.
Atkinson, P. (2007), Ethnography: Principles in Practice, Routledge, New York.
Backlund, S., Thollander, P., Palm, J. and Ottosson, M. (2012), “Extending the energy efficiency gap”, Energy Policy, Vol. 51, pp. 392-396.
Bahloul, M., Trivedi, R., Cardo-Miota, J., Papadimitriou, C., Efthymiou, V., Nouri, A. and Khadem, S. (2024), “Analysis of barriers and key enablers toward citizen ESS successful integration”, Journal of Energy Storage, Vol. 86, p. 111166.
Balasubramanian, C. and Singh, R.L.R. (2024), “IOT based energy management in smart grid under price based demand response based on hybrid FHO-RERNN approach”, Applied Energy, Vol. 361, p. 122851.
Balta-Ozkan, N., Davidson, R., Bicket, M. and Whitmarsh, L. (2013), “Social barriers to the adoption of smart homes”, Energy Policy, Vol. 63, pp. 363-374, doi: 10.1016/j.enpol.2013.08.043.
Barai, V. (2024), “Blockchain in land registry for transforming land administration”, Journal of Theoretical and Applied Information Technology, Vol. 102 No. 3.
Beaudin, M. and Zareipour, H. (2015), “Home energy management systems: a review of modelling and complexity”, In Renewable and Sustainable Energy Reviews, Vol. 45, pp. 318-335, doi: 10.1016/j.rser.2015.01.046.
Bipongo, C.N. and Adonis, M. (2022), “An approach of battery energy management systems for dc micro-grids considering stateflow controller”, Journal on Electrical Engineering, Vol. 15 No. 3.
Boadu, S. and Otoo, E. (2024), “A comprehensive review on wind energy in Africa: challenges, benefits and recommendations”, Renewable and Sustainable Energy Reviews, Vol. 191, p. 114035.
Bohlmann, J.A. and Inglesi-Lotz, R. (2018), “Analysing the South African residential sector’s energy profile”, Renewable and Sustainable Energy Reviews, Vol. 96, pp. 240-252, doi: 10.1016/j.rser.2018.07.052.
Bohlmann, J., Bohlmann, H., Inglesi-Lotz, R. and van Heerden, J. (2016), “An economy-wide evaluation of new power generation in South Africa: the case of Medupi and Kusile”, Energy Policy, Vol. 97, pp. 450-460, doi: 10.1016/j.enpol.2016.07.020.
Brown, D.J., Ferris, D.L., Heller, D. and Keeping, L.M. (2007), “Antecedents and consequences of the frequency of upward and downward social comparisons at work”, Organizational Behavior and Human Decision Processes, Vol. 102 No. 1, pp. 59-75.
Brunke, J.C., Johansson, M. and Thollander, P. (2014), “Empirical investigation of barriers and drivers to the adoption of energy conservation measures, energy management practices and energy services in the Swedish iron and steel industry”, Journal of Cleaner Production, Vol. 84 No. 1, pp. 509-525, doi: 10.1016/j.jclepro.2014.04.078.
Camarasa, C., Kalahasthi, L.K. and Rosado, L. (2021), “Drivers and barriers to energy-efficient technologies (EETs) in EU residential buildings”, Energy and Built Environment, Vol. 2 No. 3, pp. 290-301.
Chai, K.H. and Yeo, C. (2012), “Overcoming energy efficiency barriers through systems approach—a conceptual framework”, Energy Policy, Vol. 46, pp. 460-472.
Christou, C., Hadjipaschalis, I. and Poullikkas, A. (2018), “Assessment of integrated gasification combined cycle technology competitiveness”, In Renewable and Sustainable Energy Reviews, Vol. 12 No. 9, pp. 2459-2471, doi: 10.1016/j.rser.2007.06.010.
Corotis, R.B., Harris, J.C. and Fox, R.R. (1981), “Delphi methods: theory and design load application”, Journal of the Structural Division, Vol. 107 No. 6, pp. 1095-1105.
Department of Energy (2017), “Annual Report 2016/2017”, Department of Energy, Pretoria, available at: www.energy.gov.za/files/annualreports/2016-2017/DOE-Annual-Report-2016-17.pdf (Accessed 25 July 2024).
Elkholy, M.H., Senjyu, T., Metwally, H., Farahat, M.A., Irshad, A.S., Hemeida, A.M. and Lotfy, M.E. (2024), “A resilient and intelligent multi-objective energy management for a hydrogen-battery hybrid energy storage system based on MFO technique”, Renewable Energy, Vol. 222, p. 119768.
Elzabadani, H., Sumi, A., Abdulrazak, B. and Jansen, E. (2005), “Self-sensing spaces: smart plugs for smart environments”, available at: www.researchgate.net/publication/236877433
Gajdzik, B., Jaciow, M., Wolniak, R., Wolny, R. and Grebski, W.W. (2024), “Diagnosis of the development of energy cooperatives in Poland—a case study of a renewable energy cooperative in the upper Silesian region”, Energies, Vol. 17 No. 3, p. 647.
Gozuoglu, A., Ozgonenel, O. and Gezegin, C. (2024), “CNN-LSTM based deep learning application on Jetson Nano: estimating electrical energy consumption for future smart homes”, Internet of Things, Vol. 26, p. 101148.
Gravert, K., Poleacovschi, C., Ballesteros, L., Cetin, K., Passe, U., Kimber, A., Malekpour Koupaei, D. and Douglass, F. (2024), “Homeowners’ motivations to invest in energy-efficient and renewable energy technologies in rural Iowa”, ASCE OPEN: Multidisciplinary Journal of Civil Engineering, 2, p.04024002, Vol. 2.
Gunmi, M.A., Hu, F., Abu-Ghunmi, D. and Abu-Ghunmi, L. (2024), “A smart home energy management system methodology for techno-economic optimal sizing of standalone renewable-storage power systems under uncertainties”, Journal of Energy Storage, Vol. 85, p. 111072.
Gutierrez-Martinez, V.J., Moreno-Bautista, C.A., Lozano-Garcia, J.M., Pizano-Martinez, A., Zamora-Cardenas, E.A. and Gomez-Martinez, M.A. (2019), “A heuristic home electric energy management system considering renewable energy availability”, Energies, Vol. 12 No. 4, p. 671.
Hanafi, A.M., Moawed, M.A. and Abdellatif, O.E. (2024), “Advancing sustainable energy management: a comprehensive review of artificial intelligence techniques in building”, Engineering Research Journal (Shoubra), Vol. 53 No. 2, pp. 26-46.
Hannan, M.A., Faisal, M., Ker, P.J., Mun, L.H., Parvin, K., Mahlia, T.M.I. and Blaabjerg, F. (2018), “A review of internet of energy based building energy management systems: issues and recommendations”, IEEE Access, Vol. 6, pp. 38997-39014.
Hassan, M.T., Burek, S. and Asif, M. (2017), “Barriers to industrial energy efficiency Improvement – manufacturing SMEs of Pakistan”, Energy Procedia, Vol. 113, pp. 135-142, doi: 10.1016/j.egypro.2017.04.040.
He, H., Meng, X., Wang, Y., Khajepour, A., An, X., Wang, R. and Sun, F. (2024), “Deep reinforcement learning based energy management strategies for electrified vehicles: recent advances and perspectives”, Renewable and Sustainable Energy Reviews, Vol. 192, p. 114248.
Hillary, R. (2004), “Environmental management systems and the smaller enterprise”, Journal of Cleaner Production, Vol. 12 No. 6, pp. 561-569.
Hughes, A. and Larmour, R. (2021), Residential electricity consumption in South Africa research report.
Ilojianya, V.I., Usman, F.O., Ibekwe, K.I., Nwokediegwu, Z.Q.S., Umoh, A.A. and Adefemi, A. (2024), “Data-driven energy management: review of practices in Canada, USA”, And Africa. Engineering Science and Technology Journal, Vol. 5 No. 1, pp. 219-230.
Ivankova, N.V. and Creswell, J.W. (2009), Mixed Methods. Qualitative Research in Applied Linguistics: A Practical Introduction, Vol. 23, pp. 135-161.
Jonban, M.S., Romeral, L., Akbarimajd, A., Ali, Z., Ghazimirsaeid, S.S., Marzband, M. and Putrus, G. (2021), “Autonomous energy management system with self-healing capabilities for green buildings (microgrids)”, Journal of Building Engineering, Vol. 34, doi: 10.1016/j.jobe.2020.101604.
Kaipainen, I. (2015), “Employee commitment to energy management: a qualitative interview study about employee commitment to energy management in industrial companies and public real estates” (Master's thesis).
Kaur, A., Kaur, P. and Ahuja, S. (2020), “Förster resonance energy transfer (FRET) and applications thereof”, Analytical Methods, Vol. 12 No. 46, pp. 5532-5550.
Koirala, B.P., Araghi, Y., Kroesen, M., Ghorbani, A., Hakvoort, R.A. and Herder, P.M. (2018), “Trust, awareness, and independence: insights from a socio-psychological factor analysis of citizen knowledge and participation in community energy systems”, Energy Research and Social Science, Vol. 38, pp. 33-40.
Kumar, V. and Jain, V. (2024), “In‐depth analysis of the power management strategies in electric vehicles”, Energy Storage, Vol. 6 No. 2, p. e611.
Kumirai, T. (2010), “Energy efficiency interventions for residential buildings in Bloemfontein using passive energy techniques”, Doctoral Thesis, CUT.
Kusakana, K. (2017), “Energy management of a grid-connected hydrokinetic system under time of use tariff”, Renewable Energy, Vol. 101, pp. 1325-1333, doi: 10.1016/j.renene.2016.10.019.
Kyere, F., Dongying, S., Bampoe, G.D., Kumah, N.Y.G. and Asante, D. (2024), “Decoding the shift: assessing household energy transition and unravelling the reasons for resistance or adoption of solar photovoltaic”, Technological Forecasting and Social Change, Vol. 198, p. 123030.
Mac Nulty, H. (2015), “Energy efficiency in the steel sector: why it works well, but not always foreword”.
Maheshwari, A., Jain, Y. and Chopra, S.S. (2024), “Spatial inequity index for essential facility access at building-level in highly dense urban areas”, Cities, Vol. 149, p. 104926.
Majdi, A., Dwijendra, N.K.A., Muda, I., Chetthamrongchai, P., Sivaraman, R. and Hammid, A.T. (2022), “A smart building with integrated energy management: steps toward the creation of a smart city”, Sustainable Energy Technologies and Assessments, Vol. 53, p. 102663.
Manivannan, R. (2024), “Research on IoT-based hybrid electrical vehicles energy management systems using machine learning-based algorithm”, Sustainable Computing: Informatics and Systems, Vol. 41, p. 100943.
Mayer, A. and Parks, P. (2024), “Media and partisanship in energy transition: towards a new synthesis”, Energy Research and Social Science, Vol. 108, p. 103368.
Mengelkamp, E., Gärttner, J., Rock, K., Kessler, S., Orsini, L. and Weinhardt, C. (2018), “Designing microgrid energy markets”, A Case Study: The Brooklyn Microgrid. Applied Energy, Vol. 210, pp. 870-880, doi: 10.1016/j.apenergy.2017.06.054.
Ndeke, C. and Adonis, M. (2020), “Energy efficiency and energy management in South Africa-standards, barriers and policies”, AIUE Proceedings of the 18th Industrial and Commercial Use of Energy Conference.
Pape, S., Snijders, R.J., Gevers, T.J., Chazouilleres, O., Dalekos, G.N., Hirschfield, G.M., Lenzi, M., Trauner, M., Manns, M.P., Vierling, J.M. and Montano-Loza, A.J. (2022), “Systematic review of response criteria and endpoints in autoimmune hepatitis by the international autoimmune hepatitis group”, Journal of Hepatology, Vol. 76 No. 4, pp. 841-849.
Perez-Lombard, L., Ortiz, J. and Christine, P. (2008), “A review on buildings energy consumption”, Energy and Buildings, Vol. 40 No. 3, pp. 394-398.
Polit, D.F., Widom, R., Edin, K., Bowie, S., London, A.S., Scott, E.K. and Valenzuela, A. (2001), “Is work enough. The experiences of current and former welfare mothers who work”, pp. 31-33.
Qazi, A., Hussain, F., Rahim, N.A., Hardaker, G., Alghazzawi, D., Shaban, K. and Haruna, K. (2019), “Towards sustainable energy: a systematic review of renewable energy sources, technologies, and public opinions”, IEEE Access, Vol. 7, pp. 63837-63851.
Rastegar, M., Fotuhi-Firuzabad, M. and Moeini-Aghtai, M. (2018), “Developing a two-level framework for residential energy management”, IEEE Transactions on Smart Grid, Vol. 9 No. 3, pp. 1707-1717, doi: 10.1109/TSG.2016.2598754.
Rathor, S.K. and Saxena, D. (2020), “Energy management system for smart grid: an overview and key issues”, International Journal of Energy Research, Vol. 44 No. 6, pp. 4067-4109.
Rawa, M., Abusorrah, A., Al-Turki, Y., Sindi, H., Bassi, H. and Mostafa, M.H. (2024), “Efficient energy management framework for enhancing the techno-economic-environmental performance of grid-connected microgrids under uncertain conditions”, Ain Shams Engineering Journal, Vol. 15 No. 1, p. 102336.
Rezk, H. and Fathy, A. (2024), “Hydrogen reduction-based energy management strategy of hybrid fuel cell/PV/battery/supercapacitor renewable energy system”, Journal of Energy Storage, Vol. 86, p. 111316.
Rowe, G. and Wright, G. (1999), “The Delphi technique as a forecasting tool: issues and analysis”, International Journal of Forecasting, Vol. 15 No. 4, pp. 353-75.
Samarakoon, S. and Rajini, P.A.D. (2013), “Enablers and barriers of implementing ISO 50001-energy management systems (EnMS) in Sri Lankan context”.
Sayed, K. and Gabbar, H.A. (2018), “Building energy management systems (BEMS)”, Energy Conservation in Residential, Commercial, and Industrial Facilities, pp. 15-81, doi: 10.1002/9781119422099.ch2.
Schulze, M., Nehler, H., Ottosson, M. and Thollander, P. (2016), “Energy management in industry–a systematic review of previous findings and an integrative conceptual framework”, Journal of Cleaner Production, Vol. 112, pp. 3692-3708.
Sekhar, D.C., Rao, P.V.V.R. and Kiranmayi, R. (2022), “Conceptual review on demand side management, optimization techniques for the improvement of power quality in smart grids”, Proceedings of the 2nd, International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, pp. 1640-1647, doi: 10.1109/ICAIS53314.2022.9742779
Shah, N., Tsai, C.F., Chao, K.M. and Lo, C.C. (2013), “Intelligent household energy management recomender system”, INNOV 2013- Proceedings of the Multi-Conference on Innovative Developments in ICT, pp. 51-56, doi: 10.5220/0003046200510056.
Shamseldein, M.Z., Abdelaziz, A.Y. and Eissa, M.M. (2019), “2—Energy management for medium-voltage direct current networks”, Medium Voltage Direct Current Grid, Academic, pp. 43-57.
Shrestha, A., Bishwokarma, R., Chapagain, A., Banjara, S., Aryal, S., Mali, B., Thapa, R., Bista, D., Hayes, B.P., Papadakis, A. and Korba, P. (2019), “Peer-to-peer energy trading in micro/mini-grids for local energy communities: a review and case study of Nepal”, IEEE Access, Vol. 7, pp. 131911-131928.
Shrouf, F. and Miragliotta, G. (2015), “Energy management based on internet of things: practices and framework for adoption in production management”, Journal of Cleaner Production, Vol. 100, pp. 235-246.
Smith, K.M., Wilson, S. and Hassall, M.E. (2022), “Barriers and drivers for industrial energy management: the frontline perspective”, Journal of Cleaner Production, Vol. 335, p. 130320.
Sooriyaarachchi, T.M., Tsai, I.T., El Khatib, S., Farid, A.M. and Mezher, T. (2015), “Job creation potentials and skill requirements in, PV, CSP, wind, water-to-energy and energy efficiency value chains”, In Renewable and Sustainable Energy Reviews, Vol. 52, pp. 653-668, doi: 10.1016/j.rser.2015.07.143.
Thollander, P. and Palm, J. (2012), Improving Energy Efficiency in Industrial Energy Systems: An Interdisciplinary Perspective on Barriers, Energy Audits, Energy Management, Policies, and Programs, Springer Science and Business Media.
Tie, S.F. and Tan, C.W. (2013), “A review of energy sources and energy management system in electric vehicles”, In Renewable and Sustainable Energy Reviews, Vol. 20, pp. 82-102, doi: 10.1016/j.rser.2012.11.077.
Tuomela, S., Iivari, N. and Svento, R. (2021), “Drivers and barriers to the adoption of smart home energy management systems–users’ perspective”.
Wilson, E. (2019), Patriotic Gore, Straus and Giroux, Farrar.
Yang, N., Amini, M.R., Johnson-Roberson, M. and Sun, J. (2018), “Real-time model predictive control for energy management in autonomous underwater vehicle”, 2018 IEEE Conference on Decision and Control (CDC), IEEE, pp. 4321-4326.
Yousuf, M.U., Irshad, M.A. and Umair, M. (2024), “Identifying barriers and drivers for energy efficiency in steel and iron industries of Karachi, Pakistan: insights from executives and professionals”, Energy Nexus, Vol. 14, p. 100284.
Yuvaraj, T., Devabalaji, K.R., Kumar, J.A., Thanikanti, S.B. and Nwulu, N. (2024), “A comprehensive review and analysis of the allocation of electric vehicle charging stations in distribution networks”, IEEE Access, Vol. 12.
Zheng, Z., Shafique, M., Luo, X. and Wang, S. (2024), “A systematic review towards integrative energy management of smart grids and urban energy systems”, Renewable and Sustainable Energy Reviews, Vol. 189, p. 114023.
Zhou, B., Li, W., Chan, K.W., Cao, Y., Kuang, Y., Liu, X. and Wang, X. (2016), “Smart home energy management systems: concept, configurations, and scheduling strategies”, In Renewable and Sustainable Energy Reviews, Vol. 61, pp. 30-40, doi: 10.1016/j.rser.2016.03.047.
Zipperer, A., Aloise-Young, P.A., Suryanarayanan, S., Roche, R., Earle, L., Christensen, D., Bauleo, P. and Zimmerle, D. (2013), “Electric energy management in the smart home: perspectives on enabling technologies and consumer behavior”, In Proceedings of the IEEE, Vol. 101 No. 11, pp. 2397-2408, doi: 10.1109/JPROC.2013.2270172.
Further reading
Cabanis, K. (2002), “Computer-related technology use by counselors in the new millennium- A Delphi study”, Journal of Technology in Counseling, Vol. 2 No. 2, pp. 3-34, available at: http://jtc.colstate.edu/Vol2_2/cabanis/cabanis.htm