Recent research on solar irradiance forecasting has attracted considerable attention, as governments worldwide are displaying a keenness to harness green energy. The goal of this study is to build forecasting methods using deep learning (DL) approach to estimate daily solar irradiance in three sites in Kuwait over 12 years (2008–2020). Solar irradiance data are used to extract and understand the symmetrical hidden data pattern and correlations, which are then used to predict future solar irradiance. A DL model based on the attention mechanism applied to bidirectional long short-term memory (BiLSTM) is developed for accurate solar irradiation forecasting. The proposed model is designed for two different conditions (sunny and cloudy days) to ensure greater accuracy in different weather scenarios. Simulation results are presented which depict that the attention based BiLSTM model outperforms the other deep learning networks in the prediction analysis of solar irradiance. The attention based BiLSTM model was able to predict variations in solar irradiance over short intervals in continental climate zones (Kuwait) more efficiently with an RMSE of 4.24 and 20.95 for sunny and cloudy days, respectively.

This article presents an 11-level operation of the WE (Wee)-Type inverter. The topology employs a single DC-source, has a reduced number of components, and exhibits a boosting capability. A voltage balancing algorithm is proposed where the inverter's redundant states are employed to maintain the auxiliary DC-link voltage. It is shown that with the control algorithm, the link voltage is preserved at half the primary DC-link voltage, and the 11-level operation is possible for any load. The presented 11-level WE-type structure is also compared with recent 11-level structures and has a lowest cost factor. Two modulation strategies verify the 11-level operation of the inverter: first, a modified nearest level control (MNLC) with the usage of both zero states is developed; and, second, selective harmonic elimination (SHE) is employed, where the angles are generated using a differential evolution (DE) technique. The maximum efficiency of the inverter is 97.55 %. The performance of the inverter is validated in MATLAB/Simulink and on the hardware-in-the-loop platform.

The massive increase in disposable plastic globally can be addressed through effective recovery methods, and one of these methods is pyrolysis. R software may be used to statistically model the composition and yield of pyrolysis products, such as oil, gas, and waxes to deduce an effective pyrolysis mechanism. To date, no research reports have been documented employing the Arrhenius equation in R software to statistically forecast the kinetic rate constants for the pyrolysis of high-density plastics. We used the Arrhenius equation in R software to assume two series of activation energies (Ea) and pre-exponential factors (A_o) to statistically predict the rate constants at different temperatures to explore their impact on the final pyrolysis products. In line with this, MATLAB (R2020a) was used to predict the pyrolysis products of plastic in the temperature range of 370–410 °C. The value of the rate constant increased with the temperature by expediting the pyrolysis reaction due to the reduced frequency factor. In both assumed series of Ea and A_o, a significantly larger quantity of oil (99%) was predicted; however, the number of byproducts increased in the first series analysis compared to the second series analysis. It was revealed that an appropriate combination of Ea, A_o, and the predicted rate constants could significantly enhance the efficiency of the pyrolysis process. The major oil recovery in the first assumed series occurred at 390 °C to 400 °C, whereas the second assumed series of Ea and Ao occurred at 380 °C to 390 °C. In the second series at 390 °C to 400 °C, the predicted kinetic rate constants behaved aggressively after 120 min of the pyrolysis process. The second assumed series and anticipated rate constants at 380 °C to 390 °C can be applied commercially to improve oil production while saving energy and heat.

The Arabian Peninsula is one of the regions in the world with the highest potential for solar energy development in the sense that the levels of solar irradiance are very high, making it an ideal site to install photovoltaic (PV) systems. However, this huge potential is seriously undermined by enduring environmental problems, especially the high levels of airborne particulate matter and anthropogenic pollutants that together reduce the intensity of the sun and contribute to the rapid soiling of PV modules. Though the current literature has largely concentrated on the quantification of the technical performance instability of PV systems caused by soiling phenomena, the associated extensive analysis in terms of quantifying the technical losses into quantifiable economic and operational effects on a regional level is strikingly lacking in the literature. This study builds a combined machine learning system in order to quantitatively measure the economic losses attributable to air pollution for utility-scale, grid-connected PV systems across the Gulf Cooperation Council (GCC) member states. We developed and strictly tested a Random Forest regression model with a remarkable coefficient of determination (R 2 ) of 98.24 that was trained on a large dataset of meteorological parameters, real-time air pollution data (PM2.52.5, PM1010, SO22, NO22, O33, CO) and the most important operational features of solar panels that occurred between 2018 and 2020. With the help of this validated predictive model, we conducted an advanced counterfactual analysis by modeling the potential power production under hypothetical conditions of clean atmosphere benchmarks. We show that air pollution is one of the contributors to the loss of 8.5% to 12.3% of annual energy production in the region; or more simply, it is a significant loss that can translate to huge financial fines, which can significantly affect the economics of projects. In addition, we propose a new data-driven predictive cleaning scheduling algorithm that proves capable of cutting operational expenditures (OPEX) by up to 25 percent relative to traditional calendar-driven cleaning schedules. The findings are empirically based, critical to renewable energy investors, utility grid operators, and policymakers, and they categorically highlight the significant economic necessity to set up wide-ranging air pollution reduction measures, even as operation and maintenance (O&M) protocols in arid, dust-prone geographical settings run to their optimum.

Solid waste management has emerged as a significant challenge in smart cities, resulting in severe health and environmental concerns. Municipal tash bins are strategically placed throughout a city; nevertheless, in the absence of real-time monitoring, these bins are often observed to be overflow. It results in environmental pollution, and adverse effects on public sanitation. Thus, there is substantial necessity of real-time solid wate management using which helps the concerned authority in real-time monitoring and accordingly schedule garbage cleaning. This work proposes a sustainable, LoRaWAN-enabled battery-free smart garbage bin framework. The proposed system can harness energy from sunlight using a thin-film Photovoltaic (PV) harvester integrated with an ultra-low power management system. Subsequently, it powers the sensor node to measure the garbage level and trasmit it to a cloud-hosted application sever. Long Range Wide Area Network (LoRaWAN) technology has been incorprated for wireless communication which operates at 867 MHz and covers a communication range up to 361 meter within dense populated area. It has been observed experimentally, that the system harvests energy at an average rate of 10.44 mW under clear sky condition. Subsequently, the power consumption of the system has been optimized using duty cycle technique resulting in average consumption of 131 μW. On the other hand, a hybrid multi-objective framework has been proposed integrating Tiny Machine Learning (TinyML) and Tiny Genetic Algorithm (TinyGA) techniques. The framework predicts the garbage fullness in a garbage bin exhibiting an accuracy of 96.22 % and subsequently provides an optimized route for the garbage cleaning vehicle for maintenance. It ensures minimum fuel consumption, travel distance, travel time, emissions, and cost in garbage cleaning vehicles. The system has been extensively deployed on a real-world test bed to validate its sustainable operation. Thus, the proposed system can be considered as an ultra-low power battery-free alternative to state-of-the-art smart solid waste management systems that align with Sustainable Development Goal (SDG) 7 of the United Nations.

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis.