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Shading the greenhouses is necessary in summer to reduce the solar radiation load. This however generates a considerable amount of thermal radiation heat load that needs to be removed via cooling systems. This study aimed to evaluate the effect of different shading configurations on the solar and thermal radiation in a greenhouse. Nets at four different locations were employed to shade the roof and side-walls of a polycarbonate, mechanically ventilated greenhouse. The spectral radiative properties of all these plastic materials were measured in short and long wave spectrum bands. The net solar and thermal radiations and air temperature were measured outside and inside two identical shaded and unshaded greenhouses. The results showed that external roof-shading is desirable, as it reduced the generated thermal radiation in the greenhouse by 21% and 15% during the day and night time, respectively and reduced the greenhouse air temperature during the day. The internal shading (roof and side walls) is undesirable, since it drastically increased the generated thermal radiation in the greenhouse by 147% and strongly increased the greenhouse air temperature during the day. Shading the side-walls is not recommended because it significantly reduces the transmitted solar radiation in the morning and afternoon (when the outside irradiance is low) and is useless at around noon when the outside irradiance is extremely high.

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.

The increase in operating temperature of PV generators leads to degradation of their performance. These adverse effects of high temperatures are considered as one of the most important problems that solar panel operation faces in hot weather areas. A lot of research has been undertaken to study this aspect and find ways of limiting the harm caused by such high temperatures. To overcome this harm and to maintain the operating temperature of the PV cells within the optimum range specified by manufacturers, cooling the solar panels often becomes indispensable. This paper discusses the heat transfer through the solar panel layers and studies the effect of high temperature on the solar panel performance in a hot desert environment. It also presents the development of a new solar panel structure viz. by installing an aluminum heat sink to reduce the effect of temperature rise and thus improve the solar panel performance. The study focuses on a pole-mounted solar panel for a street lighting apparatus in extremely hot desert conditions with fluctuating wind speeds. It will be shown that adding an aluminum heat sink to the solar panel bottom mitigates the effect of increased temperature and hence modifies the solar panel operating point by increasing both the efficiency and the lifetime. The solar cell temperature is decreased by 16.4% as a result of the aluminum heat sink installation on the solar panel back sheet and consequently, the accumulated energy produced by the the solar panel is increased by 13.23% per day.

Biodiesel offers an advantage only if it can be used as a direct replacement for ordinary diesel. There are many reasons to promote biodiesel. However, biodiesel cannot get wide acceptance until its drawbacks have been overcome including poor low temperature flow properties, variation in the quality of biodiesel produced from different feedstocks and fuel filter blocking. In the present study, a much cheaper and simpler method called high vacuum fractional distillation (HVFD) has been used as an alternative to produce high-quality refined biodiesel and to improve on the abovementioned drawbacks of biodiesel. The results of the present study showed that none of biodiesel sample produced from crude Azadirachta indica (neem) oil met standard biodiesel cetane number requirements. The high vacuum fractional distillation (HVFD) process improved the cetane number of produced biodiesels which ranged from 44–87.3. Similarly, biodiesel produced from fractionated Azadirachta indica oil has shown lower iodine values (91.2) and much better cloud (−2.6 °C) and pour point (−4.9 °C) than pure Azadirachta indica oil. In conclusion, the crude oil needs to be vacuum fractioned for superior biodiesel production for direct utilization in engine and consistent quality production.

The world has witnessed a rapid transformation in the field of electrical generation, transmission and distribution. We have been constantly developing and upgrading our technology to make the system more economically efficient. Currently, the industry faces an acute shortage of energy resources due to overconsumption by industries worldwide. This has compelled experts to look for alternatives to fossil fuels and other conventional sources of energy to produce energy in a more sustainable manner. The microgrid concept has gained popularity over the years and has become a common sight all over the world because of the ability of a microgrid to provide power to a localized section without being dependent on conventional resources. This paper focuses on development of such an AC hybrid microgrid, which receives power from distributed energy resources (DERs) such as a PV array alongside a battery storage system, and also uses an emergency diesel generator system and an online uninterruptible power supply (UPS) system to provide power to predefined loads under different conditions. This paper also addresses on the power flow to the loads under two main modes of operation—on grid and off grid—and investigates the microgrid in different states and sub-states. The final objective is to design an efficient microgrid model such that it can sustain the multiple loads simultaneously under all operating conditions.

Concentrating PV thermal (CPVT) collector with spectral splitting technology is a promising solution for heat and electricity production. To extend the use of this technology, a novel and cost-effective CPVT collector for harsh environments, such as those in Saudi Arabia, is presented and evaluated using theoretical energy, economy, and environmental analysis. Two questions are answered in this study, namely: which is the best operation strategy, and which is the best energy storage technology for CPVT. The potential of using a CPVT under the climate conditions of six cities in Saudi Arabia is also evaluated. It is found that a heat/electricity production strategy and a thermal energy storage are the most suitable for the CPVT technology. The economic assessment shows a levelized cost of electricity (LCOE) of $0.0847/kWh and a levelized cost of heat (LCOH) of $0.0536/kWh when water is used as a spectral filter, and a LCOE of $0.0906/kWh and a LCOH of $0.0462/kWh when ZnO nanoparticles are added. The CO2-equivalent emissions in a 20 MW CPVT plant are cut from 5675 tonnes to 7822 tonnes per year for Saudi Arabian weather and present power generation 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.