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This purpose of this paper is to address the research problem of optimizing photovoltaic (PV) panel placement on building facades to maximize solar energy generation.

The study examines the significance of various design configurations and their implications for PV system performance. The research involves analysis of relevant literature and energy simulations. An exemplary case study is conducted in a hot climate zone to quantify the impacts of PV panel placement on energy generation. Various application scenarios are developed, resulting in 28 scenarios for PV on building facades. Energy simulations using Grasshopper Rhino software and Ladybug plugin components are performed.

The paper identifies key factors influencing PV panel placement and energy generation through qualitative analysis. It introduces an appropriateness matrix as a decision-making framework to evaluate placement options. The study identifies design configurations and external features impacting PV location selection and performs a qualitative classification to determine their impact on energy generation.

The results and decision-making framework enable informed choices based on solar radiation levels, shading conditions, and building requirements. Optimizing PV panel placement enhances solar energy harvesting in buildings, benefiting architects and engineers.

The novel contributions of this paper include practical insights and guidance for strategically placing PV panels on building facades.

Unmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.

To address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.

The analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.

A multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

This study aims to develop two piezoelectric textile devices formed from different weft knitted fabric rapports (Jersey and Pique) to be applied in the renewable energy’s (RE) area.

Two different weft knitted rapports were produced with polyester (PES). The device developed has five layers: a central of poly(vinylidene fluoride) (PVDF) nonwoven, involved by two insulating layers of PES knitted fabric; and two conductive external layers, made of polypyrrole-coated PES knitted fabric. The piezoelectric textile devices were joined by sewing the five layers of the device.

The FTIR technique confirmed the β-phase in the PVDF nonwoven. This study produced and tested two different textiles devices with piezoelectric behavior, confirmed by the correlated pattern of voltage and tensile stress difference curves, showing the potential application in RE’s and sustainable energies field as smart textiles, such as devices incorporated in garments in the areas of high movement (elbow, knee, foot, fingers and hands, among others), and as an energy generator device

Textile materials with piezoelectric properties promise to advance RE’s developments due to their high material flexibility and sensitivity to the electrical response. The knitted fabric technology presents flexibility due to its construction process. Comparative studies analyzing the electrical response between knitted and woven fabrics have already been realized. However, there is a gap in terms of research scientific research regarding the comparison of the piezoelectric effect in a material that presents different knitted fabric rapports.

Renewable energy sources and smart devices are options for those wishing to lessen their reliance on fossil fuels. Smart devices in the home also allow energy providers to remotely control energy use (RCEU). However, little is understood about consumer’s perceptions of RCEU programs. Based on the theory of planned behavior (TPB), it is proposed that environmental attitudes, environmental self-identity, green history, subjective norms and perceived behavioral control will predict differences in both purchase intentions and RCEU.

Data from 692 participants was collected via an online survey of energy consumers. The relationship between study variables was examined using regression analyses.

The results indicate that environmental attitude, environmental identity, green history and perceived behavioral control are positively related to both purchase intentions and RCEU. The results could have important implications for energy providers, practitioners, energy consumers and citizens interested in environmental issues.

As energy providers consider ways to better manage consumer energy use, RCEU has been used more frequently. However, understanding customer perceptions of RCEU is not well-established in the green energy literature. This paper contributes a first step towards the understanding of RCEU perceptions.

This paper aims to investigate the optimization of window and shading designs to reduce the building energy consumption of a standard office room while improving occupants' comfort in Tehran and Auckland.

The NSGA-II algorithm, as a multi-objective optimization method, is applied in this study. First, a comparison of the effects of each variable on all objectives in both cities is conducted. Afterwards, the optimal solutions and the most undesirable scenarios for each city are presented for architects and decision-makers to select or avoid.

The results indicate that, in both cities, the number of slats and their distance from the wall are the most influential variables for shading configurations. Additionally, occupants' thermal comfort in Auckland is much better than in Tehran, while the latter city can receive more daylight. Furthermore, the annual energy use in Tehran can be significantly reduced by using a proper shading device and window-to-wall ratio (WWR), while building energy consumption, especially heating, is negligible in Auckland.

To the best of the authors' knowledge, this is the first study that compares the differences in window and shading design between two cities, Tehran and Auckland, with similar latitudes but located in different hemispheres. The outcomes of this study can benefit two groups: firstly, architects and decision-makers can choose an appropriate WWR and shading to enhance building energy efficiency and occupants' comfort. Secondly, researchers who want to study window and shading systems can implement this approach for different climates.

This paper aims to conduct a detailed analysis of the industrial practices currently being used in the geothermal energy industry and to determine whether they are contributing to any limitations. A HAZOP-based upgradation model for improvement in existing industrial practices is proposed to ensure the removal of inefficient conventional practices. The HAZOP-based upgradation model examines the setbacks, identifies its causes and consequences and suggests improvement methods comprising of modern-day technology.

This paper proposed a HAZOP-based upgradation model for improvement in existing industrial practices. The proposed HAZOP model identifies the drawbacks brought on by conventional practices and suggests improvements.

The study reviewed the challenges geothermal power plants currently face due to conventional practices and suggested a total of 22 upgradation recommendations. From those, a total of 11 upgradation modules comprising modern digital technology and Industry 4.0 elements were proposed to improve the existing practices in the geothermal energy industry. Autonomous robots, augmented reality, machine learning and Internet of Things were identified as useful methods for the upgradation of the existing geothermal energy system.

If proposed recommendations are incorporated, the efficiency of geothermal energy generation will increase as cumulating setbacks will no longer degrade the work output.

The proposed recommendation by the study will make way for Industry 4.0 integration with the geothermal energy sector.

The paper uses a proposed HAZOP-based upgradation model to review issues in existing industrial practices of the geothermal energy sector and recommends solutions to overcome operability issues using Industry 4.0 technologies.

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.

The study aims to tackle Egypt's rising electricity consumption due to climate change and population growth, focusing on the building sector, which accounts for up to 60% of the issue, by developing new energy-efficient design guidelines for Egyptian buildings.

This study comprises six key steps. A literature review focuses on energy consumption and efficiency in buildings, monitoring a single-family building in Cairo, using Energy Plus for simulation and verification, performing multi-objective optimization, comparing energy performance between base and controlled cases, and developing a localized version of the Passive House (PH) called Energy Efficiency Design Criteria (EEDC).

The research shows that applying the (EEDC) suggested by this study can decrease energy consumption by up to 58% and decrease cooling consumption by up to 63% in residential buildings in Egypt while providing thermal comfort and reducing greenhouse gas emissions. This can benefit users, alleviate local power grid strain, contribute to Egypt's economy, and serve as a model for other countries with similar climates.

To date, no studies have focused on developing energy-efficient design standards tailored to the Egyptian climate and context using the Passive House Criteria concept. This study contributes to the field by identifying key principles, design details, and goal requirements needed to promote energy-efficient design standards for residential buildings in Egypt.

This purpose of this paper is to address the problem of reducing energy consumption in existing buildings using advanced noninvasive interventions (NVIs).

The study methodology involves systematically developing and testing 18 different NVIs in six categories (glazing types, window films, external shading devices, automated internal shades, lighting systems and nanopainting) to identify the most effective individual NVIs. The impact of each individual NVI was examined on an exemplary university educational building in a hot climate zone in Egypt using a computational energy simulation tool, and the results were used to develop 39 combination scenarios of dual, triple and quadruple combinations of NVIs.

The optimal 10 combination scenarios of NVIs were determined based on achieving the highest percentages of energy reduction. The optimal percentage of energy reduction is 47.1%, and it was obtained from a combination of nanowindow film, nanopainting, LED lighting and horizontal louver external. The study found that appropriate mixture of NVIs is the most key factor in achieving the highest percentages of energy reduction.

These results have important implications for optimizing energy savings in existing buildings. The results can guide architects, owners and policymakers in selecting the most appropriate interventions in existing buildings to achieve the optimal reduction in energy consumption.

The novelty of this research unfolds in two significant ways: first, through the exploration of the potential effects arising from the integration of advanced NVIs into existing building facades. Second, it lies in the systematic development of a series of scenarios that amalgamate these NVIs, thereby pinpointing the most efficient strategies to optimize energy savings, all without necessitating any disruptive alterations to the existing building structure. These combination scenarios encompass the incorporation of both passive and active NVIs. The potential application of these diverse scenarios to a real-life case study is presented to underscore the substantial impact that these advanced NVIs can have on the energy performance of the building.

This research aims to explore the factors influencing the adoption intention of eco-friendly smart home appliances among residents in densely populated urban areas of a developing country.

A quantitative research approach was employed to gather data from 348 respondents through purposive sampling. A comparative analysis strategy was then utilized to investigate the adoption of eco-friendly smart home appliances, combining both linear (PLS-SEM) and non-linear (fsQCA) approaches.

The results obtained from PLS-SEM highlight that performance expectancy, facilitating conditions, hedonic motivation, price value, and environmental knowledge significantly influence the adoption intention of eco-friendly smart home appliances. However, the findings suggest that effort expectancy, social influence, and habit are not significantly associated with customers' intention to adopt eco-friendly smart home appliances. On the other hand, the fsQCA results identified eight configurations of antecedents, offering valuable insights into interpreting the complex combined causal relationships among these factors that can generate (each combination) the adoption intention of eco-friendly smart home appliances among densely populated city dwellers.

This study offers crucial marketing insights for various stakeholders, including homeowners, technology developers and manufacturers, smart home service providers, real estate developers, and government entities. The findings provide guidance on how these stakeholders can effectively encourage customers to adopt eco-friendly smart home appliances, aligning with future environmental sustainability demands. The research implications underscore the significance of exploring the antecedents that influence customers' adoption intention of eco-friendly technologies, contributing to the attainment of future sustainability goals.

The environmental sustainability of smart homes, particularly in densely populated city settings in developing countries, has received limited attention in previous studies. Therefore, this study aims to address the pressing issue of global warming and make a meaningful contribution to future sustainability goals related to smart housing technologies. Therefore, this study employs a comprehensive approach, combining both PLS-SEM (linear) and fsQCA (non-linear) techniques to provide a more thorough examination of the factors influencing the adoption of environmentally sustainable smart home appliances.