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This paper investigates the optimization of window design factors (WDFs) in hospital buildings, particularly in government hospitals within the arid climate of the Qassim region, with the aim of achieving a better cooling load reduction. Continuous monitoring of the hospital ward section is crucial due to patients' needs, requiring optimal indoor air quality and cooling load.

The study identifies the optimal conditions for WDF design to mitigate cooling load, including window-to-wall ratio (WWR), window orientation (WO), room size and U-value (thermal properties), effectively reduce energy consumption in terms of sensible cooling load (MWh/m2) and comply with local codes. Data collection involved a smart weather station, while the Integrated Environmental Solution Virtual Environment (IESVE) software facilitated the simulation process.

Key findings reveal that larger patient rooms were about 40% more energy-efficient than smaller rooms. The northern orientation showed lower energy consumption, and specific WWRs and glazing U-values significantly affected energy loads. In an analysis of U-value changes in energy performance based on the Saudi Building Code (SBC), the presented values did not meet the minimum energy consumption standards. For a valid 40% WWR with a thermal permeability of 2.89, 0.181 MWh/m2 was consumed, while for an invalid 100% WWR with the same permeability but facing the north, 0.156 MWh/m2 was consumed, which is considered an invalid practice. It is vital to follow prescribed standards to ensure energy efficiency and avoid unnecessary costs.

Focus lies in emphasizing the significance of adhering to prescribed standards, such as SBC, to guarantee energy efficiency and prevent unwarranted expenses. Additionally, the authors highlight the crucial role of optimizing glazing properties and allocating the WWR appropriately to achieve energy-efficient building design, accounting for diverse orientations and climatic conditions.

This study aims to investigate environmental efficiency based on energy change by using energy-related or nonenergy-related variables by reckoning with months and years as decision-making units (DMUs) for a hospital under radial and nonradial models.

The non-oriented slack-based measures (SBM)-data envelopment analysis (DEA) model considering desirable and undesirable outputs has been embraced in this study, where its obtained results were compared with the results of other DEA models are output-oriented SBM-DEA and Banker, Charnes, & Cooper-DEA. For this purpose, this research has used a data set covering the 2012–2018 period for a reference hospital, which includes energy-related and nonenergy-related variables.

The results demonstrate that environmental efficiency based on energy reached the highest level in the winter months, whereas the summer months have the lowest efficiency values arising from the increasing electricity consumption due to high cooling needs. According to results of the non-oriented SBM model, the month with the highest efficiency in all periods is January with a 0.936 average efficiency score, the lowest month is August with a 0.406 value.

This paper differs from other studies related to energy and environmental efficiencies in the literature with some aspects. First, to the best of the authors’ knowledge, this study is the first one that takes into account time periods (months and years) as (DMUs for a single organization. Second, this study investigates environmental nonefficiencies, which are derived from energy uses and factors affecting energy use.

The COVID-19 pandemic has generated a crisis that has hit the European economy and the currently existing systems. To cope with the crisis, Europe has started an investment aiming the energy transition and crisis. Portugal, Spain and Greece have received the approval of their National Recovery Plans from the European Commission, with a definition of spending up to 2026 through the European Union (EU) Next Generation Found. The study investigates whether the Green Deal policies are relaunched by the plans financed and whether the pandemic has changed and conditioned the priorities of the energy transaction. The study uses the lens of corporate social responsability (CSR) and relapse measurable across the Sustainable Development Goals (SDGs).

The Green Deal policies supported by new European investments in the three countries were analyzed through a content analysis (CA) technique to investigate the associated practical and theoretical elements.

The energy theme has a relevance compared to other issues in the investment plans envisaged in Greece, Portugal and Spain. The analysis highlights energy efficiency, sustainable energy and reduction of consumption among the main themes. Energetically, sustainable building plays a central role. The study highlights the relationship between Green Deal policies, CSR, SDGs and management tools adopted.

The study strengthens the relationship between the Green Deal, CSR and SDGs by identifying policies that have already been implemented and theoretical and practical gaps on which politicians and scholars will have to investigate and support in the process of development and continuity of the identified pillars.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.

This paper aims to investigate and formulate several business models (BM) for various energy communities (EC) members: prosumers, storage facilities, electric vehicle (EV) charging stations, aggregators and local markets.

One of the flexibility drivers is triggered by avoiding the cost and maximizing value that consists of delivering a service such as increasing generation or reducing consumption when it is valued most. The transition to greener economies led to the emergence of aggregators that aggregate bits of flexibility and handle the interest of their providers, e.g. small entities such as consumers, prosumers and other small service providers. On one hand, the research method consists of formulating six BM and implementing a BM that includes several consumers and an aggregator, namely, scheduling the household electricity consumption (downstream) and using flexibility to obtain revenue or avoid the cost. This is usually performed by reducing or shifting the consumption from peak to off-peak hours when the energy is cheaper. Thus, the role of aggregators in EC is significant as they intermediate small-scale energy threads and large entities' requirements, such as grid operators or retailers. On the other hand, in the proposed BM, the aggregators' strategy (upstream) will be to minimize the cost of electricity procurement using consumers’ flexibility. They set up markets to buy flexibility that is valued as long as their costs are reduced.

Interesting insights are revealed, such as when the flexibility price doubles, the deficit coverage increases from 62% to 91% and both parties, consumers and retailers obtain financial benefits from the local market.

One of the limitations of using the potential of flexibility is related to the high costs that are necessary to implement direct load control. Another issue is related to the data privacy aspects related to the breakdown of electricity consumption. Furthermore, data availability for scientific research is limited. However, this study expects that new BM for various EC members will emerge in the future largely depending on Information Communications and Technology developments.

An implementation of a local flexibility market (LFM) using 114 apartments with flexible loads is proposed, demonstrating the gains obtained from trading flexibility. For LFM simulation, this study considers exemplifying a BM using 114 apartments located in a multi-apartment building representing a small urban EC situated in the New England region in North America. Open data recorded in 2016 is provided by UMassTraceRepository.

As a novelty, six BM are proposed considering a bottom-up approach and including various EC members.

The aim is to holistically assess the environmental performance of windows and analyse how their design and characteristics contribute to the overall performance of the building/space. This study focuses on the performance of windows in patient rooms hosting less mobile people.

This study investigates the life cycle environmental impacts of different glazing types, window frames and fire safety doors at the product level. This article also presents a building-integrated environmental analysis of patient rooms that considers the multiple functionalities of windows by incorporating dynamic energy analysis, comfort and daylighting performance with a life cycle assessment (LCA) study.

The results indicate that the amount of flat glass is the main contributor to the environmental impacts of the glazing units. As for the patient rooms, global warming shows the most significant contribution to the environmental costs, followed by human toxicity, particulate matter formation and eutrophication. The key drivers for these impacts are production processes and operational energy use. This study highlights the significance of evaluating a wide range of criteria for assessing the performance of windows.

An integrated assessment approach is used to investigate the influence of windows on environmental performance by considering the link between window/design parameters and their effects on energy use/costs, daylighting, comfort and environmental impacts. The embodied impacts of different building elements and the influence of various design parameters on environmental performance are assessed and compared. The environmental costs are expressed as an external environmental cost (euro).

The purpose of the present study is to investigate the community’s view of power outages and their effect on sustainable community development. This research has three aims. This project will first investigate how a blackout affects a community’s ability to live sustainably.

A survey questionnaire was developed that included 22 factors from the literature. The questionnaire was distributed to diversified segments of society from different regions of Pakistan and examined critical factors affecting sustainable community development. A total of 349 (77%) responses were received.

The study results confirm that power failure negatively affects the sustainable life of a community. This study found that the reduced production of large manufacturing, decrease leisure and comfort, reduced public administrative efficiency, loss of educational opportunities and increased use of traditional energy sources are the most important factors for sustainable community development. Findings also suggest that policymakers and practitioners in public organizations need to ensure that projects are completed in a timely manner to meet growing community needs.

The purpose of this research is to address knowledge gaps related to power outages and sustainable community development. The major impact of power disruptions on communities was previously disregarded in development discourse. Participants in these discussions recognize communities as genuine stakeholders and acknowledge that power outages can have a substantial impact on their economic and social development. However, previous studies have neglected to address this phenomenon.

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 investigate the influence of building design on the thermal comfort of occupants of naturally ventilated hospital (NVH) wards to identify the aspects with the most significant influence on the thermal comfort of hospital buildings during the hot-dry season in the hot-humid tropics of Southeast Nigeria.

Field measurements, physical observations and a questionnaire survey of 60 occupants of the wards of the Joint Presbyterian Hospital, Uburu in Ebonyi State, Nigeria were undertaken. The data were analysed using Humphreys' neutral temperature formula, descriptive statistics and multiple regression analysis.

The results revealed that the neutral temperature for the wards ranges from 26.2 °C to 29.9 °C, the thermal condition in the wards was not comfortable because it failed to meet the ASHRAE Standard 55 as only 65% of the occupants said the thermal condition was acceptable. The number and sizes of windows, building orientation, the presence of high-level windows and higher headroom significantly influenced the occupants' thermal comfort vote.

This research is valuable in estimating comfort temperature and identifying aspects that require attention in enhancing the capacity of NVH wards to effectively meet the thermal comfort needs of occupants in the hot-humid tropics of Southeast Nigeria and other regions that share similar climatic conditions.

To the best of the authors’ knowledge, this is the first study of this nature that provides valuable feedback for building design professionals on the performance of existing hospital buildings in meeting users' thermal comfort needs in the hot-dry season of the hot-humid tropics in Southeast Nigeria.

This study aims to investigate the impact of service quality, as measured by the SERVQUAL model with extended variables of safety awareness and energy conservation, on consumer satisfaction. It also explores the mediating effect of energy conservation and the moderating effect of consumer residence of electricity on this relationship.

The study uses a quantitative research approach, with data collected from 1,004 respondents through a questionnaire from the household consumers of UGVCL. The survey measures service quality using the SERVQUAL model with the additional variables of safety awareness and energy conservation. The data is analyzed using structural equation modeling, mediation and moderation to test the proposed hypotheses.

The results show that service quality, safety awareness and energy conservation all have a positive impact on consumer satisfaction expect responsiveness. Energy conservation also mediates the relationship between service quality and satisfaction, indicating that when consumers perceive higher service quality, they are more likely to conserve energy and thereby increase their satisfaction. Additionally, the moderating effect of consumer residence of electricity shows that the impact of service quality and safety awareness on satisfaction is stronger for consumers living in areas with higher electricity rates.

The paper lies in its investigation of the impact of service quality on consumer satisfaction using the SERVQUAL model, with the inclusion of two additional variables, safety awareness and energy conservation. Furthermore, this study examines the mediating effect of energy conservation and the moderating effect of consumer residence of electricity on the relationship between service quality and consumer satisfaction. To the best of our knowledge, this is the first study to incorporate these variables in examining the relationship between service quality and consumer satisfaction in the context of energy services. Therefore, this study offers a novel contribution to the existing literature on service quality and consumer satisfaction.