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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.

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 transportation sector accounts for more than 70% of Nigeria’s energy consumption. This sector has been the major consumer of fossil fuels in the past 20 years. In this study, the technical and economic feasibility of an electrical vehicle (EV) charging scheme is investigated based on the availability of renewable energy (RE) sources in six sites representing diverse geographic and climatic conditions in Nigeria. The HOMER Pro® microgrid software with the grid-search and proprietary derivative-free optimization techniques is used to assess the viability of the proposed EV charging scheme. The PV/WT/battery charging station with a quantity of two WT, 174 kW of PV panels, a quantity of 380 batteries storage, and a converter of 109 kW located in Sokoto provide the best economic metrics with the lowest NPC, electricity cost, and initial costs of USD547,717, USD0.211/kWh, and USD449,134, respectively. The optimal charging scheme is able to reliably satisfy most of the EV charging demand as it presents a small percentage of the unmet load, which is the lowest when compared with the corresponding values of the other charging stations. Moreover, the optimal charging system in all six locations is able to sufficiently meet the EV charge requirement with maximum uptime. A sensitivity analysis was conducted to check the robustness of the optimum charging scheme. This sensitivity analysis reveals that the technical and economic performance indicators of the optimum charging station are sensitive to the changes in the sensitivity variables. Furthermore, the outcomes ensure that the hybrid system of RE sources and EVs can minimize carbon and other pollutant emissions. The results and findings in this study can be implemented by all relevant parties involved to accelerate the development of EVs not only in Nigeria but also in other parts of the African continent and the rest of the world.

Human beings share the same community in which the usage of energy by fossil fuels leads to deterioration in the environment, typically global warming. When the temperature rises to the critical point and triggers the continual melting of permafrost, it can wreak havoc on the life of animals and humans. Solutions could include optimizing existing devices, systems, and platforms, as well as utilizing green energy as a replacement of non-renewable energy. In this special issue “Artificial Intelligence for Smart and Sustainable Energy Systems and Applications”, eleven (11) papers, including one review article, have been published as examples of recent developments. Guest editors also highlight other hot topics beyond the coverage of the published articles.

Morocco is a country with a lack of fossil fuel resources and an increasing demand for energy. This inspired the country to increase the use of renewable energy in the energy mix. The objective of this study was to conduct an optimization and techno-economic appraisal of a concentrated solar power plant (CSP) using different scenarios that took Ouarzazate city in the south of Morocco as a case study. To achieve this, several parameters were assessed, including the impacts of solar collector assemblies (SCAs), receiver types, heat transfer fluids, cooling systems, solar multiples, and thermal storage hours, with regard to the profitability of the CSP plant. Then, performance and sensitivity analyses were conducted to select the best integration scenarios based on different economic indicators, including levelized cost of electricity (LCOE) and net present value (NPV). The findings revealed that the use of the Luz LS-3 as the collector/SCA, Solel UVAC 3 as receiver, and Dowtherm Q as heat transfer fluid exhibited the highest performance in terms of the annual energy production yield and capacity factor, as well as the lowest real and nominal LCOEs with a wet cooled condenser. Furthermore, the LCOE is extremely sensitive to changes in the number of hours of storage and the solar multiple, and the optimal real and nominal LCOEs are determined by a highly specific combination of the solar multiple and the number of hours of storage. As a consequence, the maximum and minimum net electricity outputs for the best configuration of the Parabolic Trough Collector (PTC) plant were 24.6 GWh and 7.4 GWh in May and December, respectively. Likewise, the capacity factor and the gross-to-net conversion factor for the optimized plant were found to be 47.9%, and 93.5%, respectively. Concerning the economic study, the expected energy cost was 0.1303 USD per kWh and the NPV value for Ouarzazate city was positive (more than USD 20 million), which indicates that the studied PTC plant was estimated to be financially and economically feasible. The results of this analysis are highly significant and may persuade decision makers, financiers, and solar energy industry players to increase their investments in the Kingdom of Morocco.