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Several graphs, streamlines, isotherms and 3D plots are illustrated to enlighten the noteworthy fallouts of the investigation. Embedding flow factors for velocity, induced magnetic field and temperature have been determined using parametric analysis.

Ternary hybrid nanofluids has outstanding hydrothermal performance compared to classical mono nanofluids and hybrid nanofluids owing to the presence of triple tiny metallic particles. Ternary hybrid nanofluids are considered as most promising candidates in solar energy, heat exchangers, electronics cooling, automotive cooling, nuclear reactors, automobile, aerospace, biomedical devices, food processing etc. In this work, a ternary hybrid nanofluid flow that contains metallic nanoparticles over a wedge under the prevalence of solar radiating heat, induced magnetic field and the shape factor of nanoparticles is considered. A ternary hybrid nanofluid is synthesized by dispersing iron oxide (Fe3O4), silver (Ag) and magnesium oxide (MgO) nanoparticles in a water (H₂O) base fluid. By employing similarity transformations, we can convert the governing equations into ordinary differential equations and then solve numerically by using the Runge–Kutta–Fehlberg approach.

There is no fund for the research work.

This kind of study may be used to improve the performance of solar collectors, solar energy and solar cells.

This investigation unfolds the hydrothermal changes of radiative water-based Fe₃O₄-Ag-MgO-H₂O ternary hybrid nanofluidic transport past a static and moving wedge in the presence of solar radiating heating and induced magnetic fields. The shape factor of nanoparticles has been considered in this study.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Living walls (LWs), vegetated walls with an integrated growth layer behind, are being increasingly incorporated in buildings. Examining plant characteristics’ comparative impacts on LWs’ energy efficiency-related thermal behavior was aimed, considering that studies on their relative effects are limited. LWs of varying leaf albedo, leaf transmittance and leaf area index (LAI) were studied for Antalya, Turkey for typical days of four seasons.

Dynamic simulations run by Envi-met were used to assess the plant characteristics’ influence on seasonal and orientation-based heat fluxes. After model calibration, a sensitivity analysis was conducted through 112 simulations. The minimum, mean and maximum values were investigated for each plant characteristic. Energy need (regardless of orientation), temperature and heat flux results were compared among different scenarios, including a building without LW, to evaluate energy efficiency and variables’ impacts.

LWs reduced annual energy consumption in Antalya, despite increasing energy needs in winter. South and west facades were particularly advantageous for energy efficiency. The impacts of leaf albedo and transmittance were more significant (44–46%) than LAI (10%) in determining LWs’ effectiveness. The changes in plant characteristics changed the energy needs up to ca 1%.

This study can potentially contribute to generating guiding principles for architects considering LW use in their designs in hot-humid climates.

The plant characteristics’ relative impacts on energy efficiency, which cannot be easily determined by experimental studies, were examined using parametric simulation results regarding three plant characteristics.

Climate change reports from New Zealand claim that climate change will impact some cities such as Auckland from a heating-dominated to a cooling-dominated climate. The benefits and risks of climate change on buildings' thermal performance are still unknown. This paper examines the impacts of climate change on the energy performance of residential buildings in New Zealand and provides insight into changes in trends in energy consumption by quantifying the impacts of climate change.

The present paper used a downscaling method to generate weather data for three locations in New Zealand: Auckland, Wellington and Christchurch. The weather data sets were applied to the energy simulation of a residential case study as a reference building using a validated building energy analysis tool (EnergyPlus).

The result indicated that in Wellington and Christchurch, heating would be the major thermal load of residential buildings, while in Auckland, the main thermal load will change from heating to cooling in future years. The revised R-values for the building code will affect the pattern of dominant heating and cooling demands in buildings in Auckland in the future, while in Wellington and Christchurch, the heating load will be higher than the cooling load.

The findings of this study gave a broader insight into the risks and opportunities of climate change for the thermal performance of buildings. The results established the significance of considering climate change in energy performance analysis to inform the appropriate building codes for the design of residential buildings to avoid future costly changes to buildings.

Jubail Industrial City is one of the largest industrial centers in the Middle East, offering potential opportunities for renewable energy generation. This research paper presents a comprehensive analysis of the wind resources in Jubail Industrial City and proposes the design of a smart grid-connected wind farm for this strategic location.

The study used wind data collected at three different heights above ground level – 10, 50 and 90 m – over four years from 2017 to 2020. Key parameters, such as average wind speeds (WS), predominant wind direction, Weibull shape, scale parameters and wind power density (WPD), were analyzed. The study used Windographer, an exclusive software program designed to evaluate wind resources.

The average WS at the respective heights were 3.07, 4.29 and 4.58 m/s. The predominant wind direction was from the north-west. The Weibull shape parameter (k) at the three heights was 1.77, 2.15 and 2.01, while the scale parameter (c) was 3.36, 4.88 and 5.33 m/s. The WPD values at different heights were 17.9, 48.8 and 59.3 W/m², respectively.

The findings suggest that Jubail Industrial City possesses favorable wind resources for wind energy generation. The proposed smart grid-connected wind farm design demonstrates the feasibility of harnessing wind power in the region, contributing to sustainable energy production and economic benefits.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

This study’s fundamental objective is to assess climate change impact on reference evapotranspiration (ET_o) patterns in Egypt under the latest shared socioeconomic pathways (SSPs) of climate change scenarios. Additionally, the study considered the change in the future solar radiation and actual vapor pressure and predicted them from historical data, as these factors significantly impact changes in the ET_o.

The study utilizes data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze reference ET_o. Six models are used, and an ArcGIS tool is created to calculate the monthly average ET_o for historical and future periods. The tool considers changes in actual vapor pressure and solar radiation, which are the primary factors influencing ET_o.

The research reveals that monthly reference ET_o in Egypt follows a distinct pattern, with the highest values concentrated in the southern region during summer and the lowest values in the northern part during winter. This disparity is primarily driven by mean air temperature, which is significantly higher in the southern areas. Looking ahead to the near future (2020–2040), the data shows that Aswan, in the south, continues to have the highest annual ET_o, while Kafr ash Shaykh, in the north, maintains the lowest. This pattern remains consistent in the subsequent period (2040–2060). Additionally, the study identifies variations in ET_o , with the most significant variability occurring in Shamal Sina under the SSP585 scenario and the least variability in Aswan under the SSP370 scenario for the 2020–2040 time frame.

This study’s originality lies in its focused analysis of climate change effects on ET_o, incorporating crucial factors like actual vapor pressure and solar radiation. Its significance becomes evident as it projects ET_o patterns into the near and distant future, providing indispensable insights for long-term planning and tailored adaptation strategies. As a result, this research serves as a valuable resource for policymakers and researchers in need of in-depth, region-specific climate change impact assessments.

Commercial buildings, which include office buildings, are one of the three major energy-consuming sectors, alongside industrial and transportation sectors. The vast increase in the number of buildings is a positive sign of the rapid development of Malaysia. However, most Malaysian government office buildings tend to consume energy inefficiently due to lack of energy optimization. Most of the previous studies focused on the performance of green buildings in fulfilling the green development guidelines. As such, it is essential to study the energy performance of existing government office buildings that were constructed before most energy-efficient standards were implemented to mitigate energy wastage due to the lack of energy optimization. This study aims to analyse the energy performance of existing non-green Malaysian government office buildings and the factors that influence building energy consumption, as well as to evaluate the efficacy of the existing energy conservation measures.

This study was conducted by a literature review and case study. The chosen buildings are six government office building blocks located in Kuala Lumpur, the capital city of Malaysia. In this study, a literature review has been conducted on the common factors affecting energy consumption in office buildings. The energy consumption data of the buildings were collected to calculate the building energy intensity (BEI). The BEI was compared to the MS1525:2019 and GBI benchmarks to evaluate energy performance. SketchUp software was utilized to illustrate the solar radiation and sun path diagram of the case study buildings. Finally, recommendations were derived for retrofit strategies based on non-design factors and passive design factors.

In typical government office buildings, the air-conditioning system consumed the most energy at 65.5%, followed by lighting system at 22.6%, and the remaining 11.9% was contributed by office appliances. The energy performance of the case study buildings is considered as satisfactory as the BEI did not exceed the MS1525:2019 benchmark of 200 kWh/m2/year. The E Block recorded the highest BEI of 183.12 kWh/m2/year in 2020 due to its north-east orientation which is exposed to the most solar radiation. Besides, E Block consists of rooms that can accommodate large number of occupants. As such, non-design factors which include higher occupancy rate and higher cooling demand due to high outdoor temperature leads to higher energy consumption. By considering passive design features such as building orientation and building envelope thermal properties, energy consumption can be reduced significantly.

This study provided a comprehensive insight into the energy performance of Malaysian government office buildings, which were constructed before the energy-efficient standards being introduced. By calculating the BEI of six government office buildings, it is found that the energy performance of the case study buildings fulfils the MS1525 benchmark, and that all their BEIs are below 200 kWh/m2/year. Malaysia's hot and humid climate significantly affects a building's cooling load, and it is found the air-conditioning system is the major energy consumer of Malaysian government office buildings. This study discusses the efficacy of the energy-saving measures implemented in the case study buildings to optimize energy consumption. Recommendations were derived based on the non-design factors and passive design factors that affected the energy consumption of the case study building. It is envisioned that this study can provide practical strategies for retrofit interventions to reduce energy consumption in Malaysian office buildings as well as for office buildings that are in a similar climate.

Green roofs are strategies for the ecological intensification of cities and a measure of meeting some of the sustainable development goals (SDGs). They have widely been adopted as an adaptation strategy against an urban heat island (UHI). However, they are conventionally soil-based making it difficult and expensive to adopt as a strategy for greening existing buildings (GEB). This paper, therefore, develops a novel green roof system using climbers for thermal-radiative performance. The paper explores the vitality of climbing species as a nature-based strategy for GEB, and for the ecological improvement of the predominantly used cool roofs in sub-Saharan Africa (SSA).

Simulation for the same building Kejetia Central Market (KCM) Redevelopment; the existing aluminium roof (AL), soil-based extensive green roof (GR1) and the proposed green roof using climbing plants (GR2) were performed using ENVI-met. The AL and GR1 were developed as reference models to evaluate and compare thermal-radiative performance of the conceptual model (GR2). The long wave radiation emission (Q_lw), mean radiant temperature (MRT) and outdoor air temperature (T_a) of all three roofing systems were simulated under clear sky conditions to assess the performance and plant vitality considering water access, leaf temperature (T_f) and latent heat flux (LE₀) of GR1 and GR2.

There was no short wave radiation (Q_sw) absorption at the GR2 substrate since the climbers have no underlying soil mass, recording daily mean average Q_lw emission of 435.17 Wm⁻². The soil of GR1, however, absorbed Q_sw of 390.11 Wm⁻² and a Q_lw emission of 16.20 wm⁻² higher than the GR2. The AL recorded the lowest Q_lw value of 75.43 Wm⁻². Also, the stomatal resistance (r_s) was higher in GR1 while GR2 recorded a higher average mean transpiration flux of 0.03 g/sm³. This indicates a higher chance of survival of the climbers. The T_a of GR2 recording 0.45°C lower than the GR1 could be a good UHI adaptation strategy.

No previous research on climbers for green roof systems was found for comparison, so the KCM project provided a unique confluence of dynamic events including the opportunity for block-scale impact assessment of the proposed GEB strategy. Notwithstanding, the single case study allowed a focussed exploration of the novel theory of redefining green roof systems with climbers. Moreover, the simulation was computationally expensive, and engaging multiple case studies were found to be overly exhaustive to arrive at the same meaningful conclusion. As a novelty, therefore, this research provides an alternative theory to the soil-based green roof phenomenon.

The thermal-radiative performance of green roofs could be improved with the use of climbers. The reduction of the intensity of UHI would lead to improved thermal comfort and building energy savings. Also, very little dependence on the volume of soil would require little structural load consideration thereby leading not only to cheaper green roof construction but their higher demand, adoption and implementation in SSA and other low-income economies of the global south.

The reduction of the consumption of topsoil and water for irrigation could avoid the negative environmental impacts of land degradation and pollution which have a deleterious impact on human health. This fulfils SDG 12 which seeks to ensure responsible consumption of products. This requires the need to advance the research for improvement and training of local built environment practitioners with new skills for installation to ensure social inclusiveness in the combat against the intractable forces of negative climate impacts.

Climbers are mostly known for green walls, but their innovative use for green roof systems has not been attempted and adopted; it could present a cost-effective strategy for the GEB. The proposed green roof system with climbers apart from becoming a successful strategy for UHI adaptation was also able to record an estimated 568% savings on topsoil consumption with an impact on the reduction of pollution from excavation. The research provides an initial insight into design options, potentials and limitations on the use of climbers for green roofs to guide future research and experimental verification.

The purpose of this study is the synthesis and characterization of a CMYK palette (cyan of Cr-BiVO₄, magenta of Pr-CeO₂, yellow of Bi-(Ce,Zr)O₂ composite and black of YMnO₃) as an eco-friendly polyfunctional palette that combines (a) high near-infrared reflectance (cool pigments) that allows moderate temperatures in indoor environments and the urban heat island effect; (b) photocatalytic activity for the degradation of organic contaminants of emerging concern of substrates in solution (such as Orange II or methylene blue) and gaseous (NO_x and volatile organic compounds such as acetaldehyde or toluene); (c) X-ray radiation attenuators associated with bismuth ions; and (d) biocidal effect combined with co-doping with bactericidal agents.

Pigments were prepared by a solid-state reaction and characterized by X-ray diffraction, diffuse reflectance spectroscopy, photocatalytic activity over Orange II and scanning electron microscopy.

The behaviour of the proposed palette was compared to that of a commercial inkjet palette, and an improvement in all functionalities was observed.

The functionalities of pigments allow the building envelope and indoor walls to exhibit temperature-moderating effects (with the additional effects of moderating global warming and increasing air conditioning efficiency), purification and disinfection of both indoor and outdoor air, and radiation attenuation.

The proposed palette and its polyfunctional characterization are novel.