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In Saudi Arabia, housing projects account for almost half of the total electricity consumed by the construction industry because of the large number of housing projects compared to other types of buildings. This paper proposes a quantitative approach using a multiple linear regression assessment model to predict the energy cost and environmental cost of housing building in Saudi Arabia. It was developed to assist house owners in Saudi Arabia in estimating the monthly energy cost and associated operational carbon cost according to several predictor parameters. Based on related literature, these parameters were reviewed and discussed by experts from the Ministry of Housing. They included building location, wall type, number of occupants, window type, envelope insulation, building age, building area, number of air-conditioning units and their systems, and lighting system. The model development process included five main stages: collecting the energy and carbon cost data from completed operating housing units, categorizing the collected data based on parameters, diagnosing the quality of gathered data and filtering outlier data if any, building and generating a model, and lastly, testing and validating the model. More than 77 datasets were collected across the country during different times of the year. The findings of this study reveal that the relationship between the number of users and the building area with the energy cost is significant and that the number of users is more correlated to the energy cost than the age of the building or the number of central air conditioners installed. Moreover, the results show that the developed model has the ability to predict energy and carbon costs with high accuracy. The developed model serves as a decision support tool for householders and decision makers in the Ministry of Housing to control the predicted parameters. This would be beneficial for the housing unit owners for allocating constrained budgets.

Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is not applicable to the recently developed data-driven BEM which requires a more flexible and data-related multi-timescales schedule-setting method to boost its performance. In this paper, a data-based schedule setting method is developed by applying K-medoid clustering with Principal Component Analysis (PCA) dimensional reduction and Dynamic Time Warping (DTW) distance measurement to a comprehensive building energy historical dataset, partitioning the data into three different time scales to explore energy usage profile patterns. The Year–Month data were partitioned into two clusters; the Week–Day data were partitioned into three clusters; the Day–Hour data were partitioned into two clusters, and the schedule-setting matrix was developed based on the clustering result. We have compared the performance of the proposed data-driven schedule-setting matrix with default settings and calendar data using a single-layer neural network (NN) model. The findings show that for the data-driven predictive BEM, the clustering results-based data-driven schedule setting performs significantly better than the conventional fixed schedule setting (with a 25.7% improvement) and is more advantageous than the calendar data (with a 9.2% improvement). In conclusion, this study demonstrates that a data-related multi-timescales schedule matrix setting method based on cluster results of building energy profiles can be more suitable for data-driven BEM establishment and can improve the data-driven BEMs performance.

High Concentrator Photovoltaic Thermal (HCPV/T) systems produce both electrical and thermal energy and they are efficient in areas with high Direct Normal Irradiance (DNI). This paper estimates the lifecycle environmental impact of the HCPV/T 2000x system for both electrical and thermal functionalities. Process-based attributional method following the guidelines and framework of ISO 14044/40 was used to conduct the Life Cycle Assessment (LCA). The midpoint and endpoint impact categories were studied. It was found that the main hotspots are the production of the thermal energy system contributing with 50% and 55%, respectively, followed by the production of the tracking system with 29% and 32% and the operation and maintenance with 13% and 7%. The main contributor to the lifecycle environmental impact category indicators was found to be the raw materials acquisition/production and manufacturing of the thermal energy and tracking systems. The results indicate that the lifecycle environmental impact of the HCPV/T 2000x system is lower compared to fuel-based Combined Heat and Power (CHP) and non-Renewable Energy Sources (non-RES) systems.

Worldwide, the penetrations of photovoltaic (PV) and energy storage systems are increased in power systems. Due to the intermittent nature of PVs, these sustainable power systems require efficient managing and prediction techniques to ensure economic and secure operations. In this paper, a comprehensive dynamic economic dispatch (DED) framework is proposed that includes fuel-based generators, PV, and energy storage devices in sustainable power systems, considering various profiles of PV (clear and cloudy). The DED model aims at minimizing the total fuel cost of power generation stations while considering various constraints of generation stations, the power system, PV, and energy storage systems. An improved optimization algorithm is proposed to solve the DED optimization problem for a sustainable power system. In particular, a mutation mechanism is combined with a salp–swarm algorithm (SSA) to enhance the exploitation of the search space so that it provides a better population to get the optimal global solution. In addition, we propose a DED handling strategy that involves the use of PV power and load forecasting models based on deep learning techniques. The improved SSA algorithm is validated by ten benchmark problems and applied to the DED optimization problem for a hybrid power system that includes 40 thermal generators and PV and energy storage systems. The experimental results demonstrate the efficiency of the proposed framework with different penetrations of PV.

The COVID-19 pandemic has forced many organizations around the world to make full use of a variety of emerging online communication platform technologies. Universities are among the organizations that have asked students, tutors, and lecturers to use a number of different online communication platforms to ensure the education process remains uninterrupted. However, the COVID-19 pandemic has generated considerable challenges for the global higher education community while using such emerging technologies. This research has two main goals. First, this paper will begin by investigating whether the online learning platforms used by university students during the COVID-19 period have presented any challenges to their learning. Second, the paper will then go on to address proposed solutions by developing a conceptual model to reduce the impact of such challenges. This research uses an exploratory qualitative research approach, supported by literature content analysis techniques. The data set for this study was collected during the first peak of the pandemic period in Malaysia, between the 16th of May 2020 and the 5th of June 2020. We used SPSS to conduct a descriptive analysis and NVivo12 to analyse data collected from 486 students from different universities in Malaysia. These students disclosed various obstacles they encountered when they used IT platform applications for online learning. These obstacles include (a) work and information overload received from instructors, (b) inadaptability and unfamiliarity of the new online learning environment, and (c) personal health challenges related to stress and anxiety. Based on previous relevant research, this study introduced a set of motivational factors and developed a conceptual motivational model for sustainable and healthy online learning.

The dyeing industry is considered one of the most polluting industries. Thus, several researchers have focused on studying the possibilities of natural textile dyeing. The objective of this paper was to optimize the microwave extraction process for Juglans regia bark residues in order to dye acrylic fabrics. Hence, at first, the following extraction conditions were studied: microwave power, pH, extraction duration and concentration of dry mass. Flavonoid and tannin content was measured each time. Moreover, the obtained extracts were used for dyeing acrylic fibers with microwave assistance, and the corresponding color yield (K/S) was measured. Then, the microwave extraction process already developed was optimized; a response surface design was established using Minitab 19 software. The optimal extraction conditions were found to be: microwave power = 850, pH = 3 and extraction time = 4 min. Finally, dyed and undyed acrylic were characterized by infrared (FTIR) spectroscopy in order to distinguish the effect of this natural dye on the external layer of the acrylic fiber.

The Human Mobility Transition model describes shifts in mobility dynamics and transport systems. The aspirational stage, ‘human urbanism’, is characterised by high active travel, universal public transport, low private vehicle use and equitable access to transport. We explored factors associated with travel behaviour in Africa and the Caribbean, investigating the potential to realise ‘human urbanism’ in this context. We conducted a mixed-methods systematic review of ten databases and grey literature for articles published between January 2008 and February 2019. We appraised study quality using Critical Appraisal Skills Programme checklists. We narratively synthesized qualitative and quantitative data, using meta-study principles to integrate the findings. We identified 39,404 studies through database searching, mining reviews, reference screening, and topic experts’ consultation. We included 129 studies (78 quantitative, 28 mixed-methods, 23 qualitative) and 33 grey literature documents. In marginalised groups, including the poor, people living rurally or peripheral to cities, women and girls, and the elderly, transport was poorly accessible, travel was characterised by high levels of walking and paratransit (informal public transport) use, and low private vehicle use. Poorly controlled urban growth (density) and sprawl (expansion), with associated informality, was a salient aspect of this context, resulting in long travel distances and the necessity of motorised transportation. There were existing population-level assets in relation to ‘human urbanism’ (high levels of active travel, good paratransit coverage, low private vehicle use) as well as core challenges (urban sprawl and informality, socioeconomic and gendered barriers to travel, poor transport accessibility). Ineffective mobility systems were a product of uncoordinated urban planning, unregulated land use and subsequent land use conflict. To realise ‘human urbanism’, integrated planning policies recognising the linkages between health, transport and equity are needed. A shift in priority from economic growth to a focus on broader population needs and the rights and wellbeing of ordinary people is required. Policymakers should focus attention on transport accessibility for the most vulnerable.