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This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

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.

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.

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.

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.

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.

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.

The Heating, Ventilation and Air Conditioning (HVAC) system is a significant energy consumer in built environments, and the building energy consumption could be minimised by optimising HVAC controls. Hence, this paper aims to investigate the applicability of Variable Refrigerant Flow (VRF) air conditioning systems for optimising the indoor comfort of buildings in Sri Lanka.

To address the research aim, the quantitative approach following the survey research strategy was deployed. Data collected through questionnaires were analysed using descriptive statistical tools, including Mean Rating (MR), Relative Important Index (RII) and Standard Deviation (SD).

The findings revealed that VRF systems are popularly used in Sri Lankan apartment buildings. Furthermore, energy efficiency and comfort were recognised as the most significant top-ranked benefits, while ventilation issues and initial cost were recognised as significant challenges. Moreover, the allocation of trained technicians and provision of proper ventilation through a Dedicated Outdoor Air System (DOAS) were highlighted as applicable mitigation strategies for the identified challenges in VRFs.

The study recommends VRF systems as a suitable technology to ensure energy efficiency, reduce GHG emissions and achieve climate performance within the built environment. The opportunities for adopting VRF systems for developing countries could be explored based on the research findings. The identified challenges would assist the design engineers and facilities professionals to devise suitable strategies to mitigate issues of VRF systems in developing countries.

This research provides empirical proof of the energy efficiency and comfort aspects of VRFs. The study has explored and recommended VRF technology as a beneficial application to overcome the persistent energy crisis in developing countries.

The purpose of this paper that will address in this paper are: how to implement and test a reliable Block-chain-based energy system for the EU energy payments and logistic? What are the main results, impacts and implications of a Block-chain-based energy system for the EU energy payments and logistic?

The main aim of this study is to propose a Block-chain-based method to offer a secure, reliable and transparent method for energy logistic and payments for European Energy Transactions. To accomplish that aim, the research method of the study follows the design science research approach of smart contracts in the EU Energy logistic and payments. This research approach mainly consists of five sequential steps which are (1) problem and motivation identification, (2) solution objectives definition, (3) design and development and (4) demonstration and evaluation.

Results of data model demonstrate that the Block-chain-based energy system here can be used for any EU energy payments and logistics. Based on the data results of the model that mentioned above enhance the security and the integrity of the energy payments and logistic by using cryptographic techniques and consensus mechanisms to prevent and detect any tampering or falsification of the data on the Block-chain.

This paper represents a genuine piece of research that contributes to the field by providing new insights and understanding. The findings presented are the result of rigorous analysis and have not been previously published or submitted elsewhere for consideration.

The residential buildings sector has a high priority in the climate change adaptation process due to significant CO₂ emissions, high energy consumption and negative environmental impacts. The article investigates how, conversely speaking, the residential buildings will be affected by climate change, and how to improve existing structures and support long-term decisions.

The climate dataset was created using the scenarios determined by the Intergovernmental Panel on Climate Change (IPCC), and this was used in the study. Different building envelope and Heating, Ventilating and Air Conditioning (HVAC) systems scenarios have been developed and simulated. Then, the best scenario was determined with comparative results, and recommendations were developed.

The findings reveal that future temperature-increase will significantly impact buildings' cooling and heating energy use. As the outdoor air temperatures increase due to climate change, the heating loads of the buildings decrease, and the cooling loads increase significantly. While the heating energy consumption of the house was calculated at 170.85 kWh/m² in 2020, this value shall decrease significantly to 115.01 kWh/m² in 2080. On the other hand, the cooling energy doubled between 2020 and 2080 and reached 106.95 kWh/m² from 53.14 kWh/m² measured in 2020.

Single-family houses constitute a significant proportion of the building stock. An in-depth analysis of such a building type is necessary to cope with the devastating consequences of climate change. The study developed and scrutinised energy performance improvement scenarios to define the climate change adaptation process' impact and proper procedure. The study is trying to create a strategy to increase the climate resistance capabilities of buildings and fill the gaps in this regard.

Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.

This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.

Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.

The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.

This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

As one of the most energy-intensive sectors, the manufacturing industry is strongly affected by current economic, ecological and political issues and is increasingly looking to reduce its energy consumption through effective management. One important component of energy management is monitoring, which can be improved by using the Internet of Things (IoT). This study aims to identify the factors behind the adoption of IoT energy monitoring systems in manufacturing industries and IoT’s impact on consumption in this context. Moreover, this study explores what constitutes successful implementation and develops recommendations on how companies can best use the generated energy-saving potential.

For this purpose, an exploratory, inductive research approach is taken in which five semi-structured interviews with IoT energy-monitoring platform providers located in Germany are conducted and additional documents about these platforms are analysed.

Based on these interviews and documents, this study defines the relevant factors involved in the increased adoption of IoT energy monitoring systems as desired cost reduction, regulatory requirements and customer expectations. Once implemented, a company’s energy efficiency potential strongly depends on its individual situation; however, the transparency characteristic of IoT supports managers in deriving effective energy-saving measures. Additionally, the used efficiency potential is influenced by the level of data collection and organisational capabilities.

To the best of the authors’ knowledge, as the first study, this research combines findings from the energy management and IoT research streams and places them into an organisational context. The application of the fit-viability model as an overarching framework enhances the theoretical contributions of this study. Moreover, focusing on Germany and its substantial industrial sector enables the gathering of important insights and the analysis of the specific use case of industrial IoT, which yields new findings for practitioners as well.

This study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location.

Three operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance.

The obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG.

The value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.

The purpose of this study is to develop a set of parameters universally acceptable for assessing design and construction strategies for reducing operational energy usage and its associated greenhouse gas (GHG) emission. Also, the parameters are intended to estimate the quantity of energy and its associated GHG emission reduction over the assessment period.

This study used five steps framework comprising definition of purpose, selecting the candidate parameters, criteria selection and description, selecting proposed parameters and defining the proposed parameters. The criteria used were the parameter’s prevalence, measurability, preference and feasibility toward adaptability to the relevant stakeholders.

This study consolidated 11 parameters. Seven cover designs and construction strategies comprising energy monitoring, natural lighting and ventilation design. Others are building thermal performance, efficient equipments, renewable energy and energy policy. The remaining four consider operational energy consumption, GHG emission quantification and their reduction over time.

Providing suitable indicators for assessing direct and indirect GHG emission with easily accessible data is essential for assessing built environment. The consolidated parameters can be used in developing rating systems, monitoring GHG inventories and activities of building related industries.

This study was conducted at the CEIES UTHM and used 11 existing rating systems open for research purposes, International Panel for Climate Change reports and GHG protocol report and guides and several other standards.

Nuclear energy is a contested topic, requiring trade-offs in energy independence, ethicality and uncertainty. Anthropogenic climate change complicates these decisions further, with nuclear energy competing with other low-carbon and sustainable energy sources. Decisions about nuclear energy’s role, as part of a sustainable energy system, must be made in cooperation with all stakeholders. However, it is unclear how the public is involved in these decisions in the UK. This study aims to address this gap, exploring the degree to which public participation has occurred in the UK.

This paper conducted a scoping review of public participation in UK nuclear energy decision-making in the context of sustainable energy transitions, where the government retains and promotes nuclear energy as part of a sustainable energy system. Following a systematic literary search, this paper reviewed 28 academic and grey literature documents.

Public participation has primarily been conducted as consultations rather than active participation. There is limited evidence that consultations have meaningfully contributed to politically and socially responsible (i.e. individuals and groups working together for community benefit) decision-making, with public opinion on nuclear energy’s role being divided and is influenced by how it is framed.

Social aspects of nuclear energy development have historically received less attention than environmental and economic elements; the role of engagement and participation is relatively rare. Modern literature reviews in this context are largely absent, a gap this paper originally contribute to. This paper suggest ways in which how effective, inclusive engagement process could contribute to a fairer, responsible decision-making process and energy system in the UK.