Incorporating solar energy into a grid necessitates an accurate power production forecast for photovoltaic (PV) facilities. In this research, output PV power was predicted at an hour ahead on yearly basis for three different PV plants based on polycrystalline (p-si), monocrystalline (m-si), and thin-film (a-si) technologies over a four-year period. Wind speed, module temperature, ambiance, and solar irradiation were among the input characteristics taken into account. Each PV plant power output was the output parameter. A deep learning method (RNN-LSTM) was developed and evaluated against existing techniques to forecast the PV output power of the selected PV plant. The proposed technique was compared with regression (GPR, GPR (PCA)), hybrid ANFIS (grid partitioning, subtractive clustering and FCM) and machine learning (ANN, SVR, SVR (PCA)) methods. Furthermore, different LSTM structures were also investigated, with recurrent neural networks (RNN) based on 2019 data to determine the best structure. The following parameters of prediction accuracy measure were considered: RMSE, MSE, MAE, correlation (r) and determination (R2) coefficients. In comparison to all other approaches, RNN-LSTM had higher prediction accuracy on the basis of minimum (RMSE and MSE) and maximum (r and R2). The p-si, m-si and a-si PV plants showed the lowest RMSE values of 26.85 W/m2, 19.78 W/m2 and 39.2 W/m2 respectively. Moreover, the proposed method was found to be robust and flexible in forecasting the output power of the three considered different photovoltaic plants.

With the emergence of computation-intensive and delay-sensitive applications, such as face recognition, virtual reality, augmented reality, and Internet of Things (IoT) devices ; Mobile Edge Computing (MEC) allows the IoT devices to offload their heavy computation tasks to nearby edge cloud network rather than to compute the tasks locally. Therefore, it helps to reduce the energy consumption and execution delay in the ground mobile users. Flying Unmanned Aerial Vehicles (UAVs) integrated with the MEC server play a key role in 5G and future wireless communication networks to provide spatial coverage and further computational services to the small, battery-powered and energy-constrained devices. The UAVenabled MEC (U-MEC) system has flexible mobility and more computational capability compared to the terrestrial MEC network. They support line-of-sight (LoS) links with the users offloading their tasks to the UAVs. Hence, users can transmit more data without interference by mitigating small-scale fading and shadowing effects. UAVs resources and flight time are very limited due to size, weight, and power (SWaP) constraints. Therefore, energy-aware communication and computation resources are allocated in order to minimize energy consumption.In this paper, a brief survey on U-MEC networks is presented. It includes the brief introduction regarding UAVs and MEC technology. The basic terminologies and architectures used in U-MEC networks are also defined. Moreover, mobile edge computation offloading working, different access schemes used during computation offloading technique are explained. Resources that are needed to be optimized in U-MEC systems are depicted with different optimization problem, and solution types. Furthermore, to guide future work in this area of research, future research directions are outlined. At the end, challenges and open issues in this domain are also summarized.

Herein we describe an effective route for the degradation of methyl green (MG) dye under visible light illumination by pristine and strontium (Sr)-doped zinc oxide (ZnO) photocatalysts (synthesized by the simple chemical precipitation method). The x-ray diffraction structural analysis has confirmed that both photocatalysts exhibit the hexagonal wurtzite structure; without any additional phase formation in Sr-doped ZnO, in particular. The optical properties of the synthesized photocatalysts have been investigated using UV–vis absorption spectroscopy in the wavelength range of 250–800 nm. Through Tauc’s plot, the slight decrease from 3.3 to 3.2 eV in band gap energy has been elucidated (in the case of Sr-doped ZnO), which has been further confirmed by the quenching in the intensity of Photoluminescence (PL) emission spectrum. This may be due to sub-band level formation between valence and conduction band, caused by the impregnation of Sr2+ ions into ZnO host. The morphological study has also been performed using Field Emission Scanning Electron Microscope, which indicates nanoparticles (NPs) based surface texture for both photocatalysts. During the photocatalytic activity study, after 30 min irradiation of visible light, ∼65.7% and ∼84.8% photocatalytic degradation of MG dye has been achieved for pristine and Sr-doped (2 wt%) ZnO photocatalysts, respectively. The rate of photocatalytic reaction (K) has been observed to be ∼0.06399 min−1 for Sr-doped (2 wt%), whereas nearly half magnitude ∼0.03403 min−1 has been observed for pristine ZnO, respectively. The significantly improved photodegradation activity may be ascribed to the relatively broader optical absorption capability, surface defects and the enhanced charge separation efficiency of the Sr-doped ZnO photocatalyst.

Herein this study, pure and manganese- (Mn-) doped ZnO (2 wt. %) nanoparticles have been synthesized using the chemical precipitation method and characterized for the photodegradation of methyl green (MG) pollutant dye under natural sunlight. The structural analysis via XRD patterns has revealed that both intrinsic and Mn-doped ZnO (2 wt. %) samples have hexagonal wurtzite structures with appropriate phase purity, clearly indicating the absence of any external impurity. The incorporation of Mn in the host ZnO lattice has decreased the crystallite size (21.10 → 18.76 nm), and nanoparticle-type surface features with sizes in the 50–100 nm range have been observed through FESEM-based surface morphological studies. Both aforementioned observations have merit in providing more active area and a high surface area to volume ratio for photocatalytic reaction. The investigation of photophysical properties indicates that in Mn-doped ZnO nanoparticles, the absorption peak is blue-shifted by 5 nm (365 → 360 nm), due to the widening of the bandgap. The degradation kinetics of MG dye follow the pseudo-second-order kinetics, and the degradation efficiency has been observed to be 62.78% mediated by pure ZnO and 66.44% by Mn-doped ZnO (2 wt. %) photocatalyst under 60 minutes of sunlight irradiation. Specifically, the rate of photocatalytic reaction (K) ~0.01792 min-1 and R 2 ~0.97992 has been achieved for pure ZnO, whereas slightly higher (K) ~0.02072 min-1 and R 2 ~0.97299 have been observed for Mn-doped ZnO, respectively. Conclusively, the synergistic interactions with multiple charge transfer pathways, improvement of e−/h+ pair charge separation, improved surface area, and efficient generation of hydroxyl radicals are supposed to be responsible for the highly efficient photocatalytic activity of the Mn–doped ZnO photocatalyst for MG dye.

Molybdenum disulfide (MoS2) is an actively pursuing material of the 2D family due to its semiconducting characteristics, making it a potential candidate for nano and optoelectronics application. MoS2 growth from molybdenum and sulphur precursors by chemical vapor depositions (CVD) is used widely, but molybdates’ conversion into MoS2 via CVD is overlooked previously. Direct growth of MoS2 on the desired pattern not only reduces the interfacial defects but also reduces the complexities in device fabrication. In this work, we combine the wet synthesis and chemical vapor deposition method where sodium molybdate and L-cysteine are used to make a solution. With the dip coating, the mixture is coated on the substrates, and then, chemical vapor deposition is used to convert the chemicals into MoS2. Raman spectroscopy revealed the presence of oxysulphides (peaks number value) other than A 1 g and E 2 g 1 , where heat treatment was performed in the presence of Ar gas flow only. On the other hand, the films reducing in the presence of sulphur and argon gas promote only A 1 g and E 2 g 1 peaks of MoS2, which confirms complete transformation. XRD diffraction showed a very small change in the diffraction peaks and value of strain, whereas SEM imaging showed the flakes formation for MoS2 samples which were heated in the presence of sulphur. X-ray photoelectron spectroscopy is also performed for the chemical composition and to understand the valence state of Mo, S, and O and other species.