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.

In this paper, we present the effect of varying humidity levels on the electrical parameters and the multi frequency response of the electrical parameters of an organic-inorganic composite (PEPC+NiPc+Cu₂O)-based humidity sensor. Silver thin films (thickness ~200 nm) were primarily deposited on plasma cleaned glass substrates by the physical vapor deposition (PVD) technique. A pair of rectangular silver electrodes was formed by patterning silver film through standard optical lithography technique. An active layer of organic-inorganic composite for humidity sensing was later spun coated to cover the separation between the silver electrodes. The electrical characterization of the sensor was performed as a function of relative humidity levels and frequency of the AC input signal. The sensor showed reversible changes in its capacitance with variations in humidity level. The maximum sensitivity ~31.6 pF/%RH at 100 Hz in capacitive mode of operation has been attained. The aim of this study was to increase the sensitivity of the previously reported humidity sensors using PEPC and NiPc, which has been successfully achieved.

Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO₂ emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities.

In this paper, three distinct distributed energy resources (DERs) modules have been built based on demand side management (DSM), and their use in power management of dwelling in future smart cities has been investigated. The investigated modules for DERs system are: incorporation of load shedding, reduction of grid penetration with renewable energy systems (RES), and implementation of home energy management systems (HEMS). The suggested approaches offer new potential for improving demand side efficiency and helping to minimize energy demand during peak hours. The main aim of this work was to investigate and explore how a specific DSM strategy for DER may assist in reducing energy usage while increasing efficiency by utilizing new developing technology. The Electrical Power System Analysis (ETAP) software was used to model and assess the integration of distributed generation, such as RES, in order to use local power storage. An energy management system has been used to evaluate a PV system with an individual household load, which proved beneficial when evaluating its potential to generate about 20–25% of the total domestic load. In this study, we have investigated how smart home appliances’ energy consumption may be minimized and explained why a management system is required to optimally utilize a PV system. Furthermore, the effect of integration of wind turbines to power networks to reduce the load on the main power grid has also been studied. The study revealed that smart grids improve energy efficiency, security, and management whilst creating environmental awareness for consumers with regards to power usage.

(1) Background: A quest for a highly sensitive and reliable humidity monitoring system for a diverse variety of applications is quite vital. Specifically, the ever-increasing demand of humidity sensors in applications ranging from agriculture to healthcare equipment (to cater the current demand of COVID-19 ventilation systems), calls for a selection of suitable humidity sensing material. (2) Methods: In the present study, the TPPNi macromolecule has been synthesized by using a microwave-assisted synthesis process. The layer structure of the fabricated humidity sensor (Al/TPPNi/Al) consists of pair of planar 120 nm thin aluminum (Al) electrodes (deposited by thermal evaporation) and ~160 nm facile spin-coated solution-processable organic TPPNi as an active layer between the ~40 µm electrode gap. (3) Results: Electrical properties (capacitance and impedance) of sensors were found to be substantially sensitive not only on relative humidity but also on the frequency of the input bias signal. The proposed sensor exhibits multimode (capacitive and conductometric) operation with significantly higher sensitivity ~146.17 pF/%RH at 500 Hz and 48.23 kΩ/%RH at 1 kHz. (4) Conclusions: The developed Al/TPPNi/Al surface type humidity sensor’s much-improved detecting properties along with reasonable dynamic range and response time suggest that it could be effective for continuous humidity monitoring in multi environmental applications.

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 and , 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 and 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.