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Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.

As the time spent by people indoors continues to significantly increase, much attention has been paid to indoor air quality. While many IAQ studies have been conducted through field measurements, the use of data-driven techniques such as machine learning has been increasingly used for the prediction of indoor air pollutants. For the present study, the concentrations of indoor air pollutants such as CO2, PM2.5, and VOCs in child daycare centers were predicted by using an artificial neural network model with three different training algorithms including Levenberg–Marquardt, Bayesian regularization, and Broyden–Fletcher–Goldfarb–Shanno quasi-Newton methods. For training and validation, data of indoor pollutants measured in child daycare facilities over a 1-month period were used. The results showed all the models produced a good performance for the prediction of indoor pollutants compared with the measured data. Among the models, the prediction by the LM model met the acceptable criteria of ASHRAE guideline 14 under all conditions. It was observed that the prediction performance decreased as the number of hidden layers increased. Moreover, the prediction performance was differed by the type of indoor pollutant. This was caused by patterns observed in the measured data. Considering the outcomes of the study, better prediction results can be obtained through the selection of suitable prediction models for time series data as well as the adjustment of training algorithms.

This paper examines the relationship between energy consumption, financial development, and economic growth in an oil-rich economy—Azerbaijan—employing cointegration techniques to the data ranging from 1992 to 2015. The results confirm the existence of a long-run relationship among the variables. Also, we find that there is a positive and statistically significant impact of financial development and economic growth on energy consumption in the long-run. The positive and statistically significant coefficient of financial development and decreasing volatility in the proxy for financial development over time can be considered as improvements in the financial system. Estimation results show that a 1% increase in financial development, proxied by the private credit indicator, and economic development increases energy consumption by 0.19% and 0.12%, respectively. The positive and significant impact of financial development on energy consumption on the backdrop of relatively cheaper energy prices due to rich oil and gas resources, should be considered by policymakers in their energy use, financial development, and economic growth related decisions.

In this paper, the linear and nonlinear effects of oil price on growth for Association of Southeast Asian Nations (ASEAN)—3 net oil-exporting countries, namely Brunei, Malaysia and Vietnam, are investigated. The empirical analysis applies the augmented autoregressive distributed lag model (ARDL) bound test approach and the nonlinear autoregressive distributed lag model (NARDL) methodology over the period of 1979 to 2017. Evidence suggests that ignoring nonlinearities may lead to misleading results. Specifically, results reveal that the effect of oil price is asymmetric for the case of Brunei, while the effect oil price is deemed insignificant for the case of Malaysia and Vietnam, both linear and nonlinear model. Brunei’s high dependency on oil revenue makes it susceptible to negative oil price shock. This suggests that oil price still plays a significant role as the main driver of economic progress for Brunei.

Global warming has led to rising electricity demands due to soaring cooling load, resulting in different technologies being implemented with renewable energy options. Renewable energy has been used to partially or fully operate these cooing systems through different technology routes in both conventional and hybrid modes. The feasibility of a particular cooling process is influenced by several technological, economic, environmental and other related factors. Selection of the appropriate route also requires consideration of external factors such as local weather, cooling load requirements and the potential of possible renewable energy. Multi-criteria decision analysis is a useful tool to systematically arrive at the right option from several possible options. This tool is used to assess the feasibility of eight technology routes for three different climatic conditions. Other than the direct cooling processes, two routes of renewable energy utilization, namely, the solar photovoltaic system and solar thermal system, are considered. The normalized decision matrix is established and weighted decision matrix is estimated, and the best solution and the worst solution values are obtained by using equations. This study is performed for three climatic zones under the Koppen classification, namely, the tropical maritime arid condition with average midday temperature from 40 to 45 °C, with two different relative humidity ranges, namely, dry area and maritime area. Additionally, the temperate continental climatic zone is analyzed for comparison. The results of this study will help decision makers to judiciously implement air conditioning systems in the above climatic zones. The distance of each waste treatment strategy from the overall best alternative treatment strategy and the overall worst alternative treatment strategy is obtained. Finally, the cooling strategies are ranked for the best option for the cooling mechanism to be adopted for the three climatic conditions.

The development of new climate change policies has increased the motivation to reduce energy use in buildings, as reflected by a stringent regulatory landscape. The construction industry is expected to adopt new methods and strategies to address such requirements, focusing primarily on reducing energy demand, improving process efficiency and reducing carbon emissions. However, the realisation of these emerging requirements has been constrained by the highly fragmented nature of the industry, which is often portrayed as involving a culture of adversarial relationships and risk avoidance, which is exacerbated by a linear workflow. Recurring problems include low process efficiency, delays and construction waste. Building information modelling (BIM) provides a unique opportunity to enhance building energy efficiency (EE) and to open new pathways towards a more digitalised industry and society. BIM has the potential to reduce (a) waste and carbon emissions, (b) the endemic performance gap, (c) in-use energy and (d) the total lifecycle impact. BIM also targets to improve the whole supply chain related to the design, construction as well as the management and use of the facility. However, the construction workforce is required to upgrade their skills and competencies to satisfy new requirements for delivering BIM for EE. Currently, there is a real gap between the industry expectations for employees and current training and educational programmes. There is also a set of new requirements and expectations that the construction industry needs to identify and address in order to deliver more informed BIM for EE practices. This paper provides an in-depth analysis and gap identification pertaining to the skills and competencies involved in BIM training for EE. Consultations and interviews have been used as a method to collect requirements, and a portfolio of use cases have been created and analysed to better understand existing BIM practices and to determine current limitations and gaps in BIM training. The results show that BIM can contribute to the digitalisation of the construction industry in Europe with adapted BIM training and educational programmes to deliver more informed and adapted energy strategies.

The purpose of the current study was to examine the determinants of R&D expenditure and health expenditure of ASEAN countries. The research objectives were developed to analyze the short-term as well as the long-term impact of economic growth, environmental pollution, and energy consumption on health and R&D expenditures. The data was collected for ten years for ASEAN countries using the Auto-Regressive Distributed Lag (ARDL) approach, which helped the researcher to assess long-run as well as the short-run association of these variables. The findings revealed that environmental pollution, energy consumption, and economic growth had a significant positive impact on health expenditure as well as on R&D expenditure of ASEAN countries in long-run. The findings further revealed that environmental pollution and economic growth had a significant impact on R&D expenditure in short-run; however, there was no significant impact of energy consumption on R&D expenditure in short-run. It was also found that there was no significant impact of any of the independent variables, i.e., energy consumption, economic growth, and environmental pollution, on health expenditure in short-run. The current study and findings have significant implications in theory and practice.