Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems

In this study, a standalone hybrid wind turbine (WT)/photovoltaic (PV)/biomass/pump-hydro-storage energy system was designed and optimized based on technical, economic, and environmental parameters to provide the load demand with an objective function of minimum cost of energy (COE). The constraints of the proposed approach are the loss of power supply probability, and the excess energy fraction. The proposed approach allows the combination of different sources of energy to provide the best configuration of the hybrid system. Therefore, the proposed system was optimized and compared with a WT/PV/biomass/battery storage-based hybrid energy system. This study proposes three different optimization algorithms for sizing and minimizing the COE, including the whale optimization algorithm (WOA), firefly algorithm (FF) and particle swarm optimization (PSO) and the optimization procedure was executed using MATLAB software. The outcomes of these algorithms are contrasted to select the most effective, and the one providing the minimum COE is chosen based on statistical analysis. The results indicate that the proposed hybrid WT/PV/biomass/pump-hydro storage energy system is environmentally and economically practical. Meanwhile, the outcomes demonstrated the technical feasibility of a pump-hydro energy storage system in expanding the penetration of renewable energy sources compared to other existing systems. The COE of the pumped-hydro storage hybrid system was found to be lower (0.215 $/kWh) than that with batteries storage hybrid system (0.254$/kWh) which was determined using WOA at the same load demand

Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems

In this study, a standalone hybrid wind turbine (WT)/photovoltaic (PV)/biomass/pump-hydro-storage energy system was designed and optimized based on technical, economic, and environmental parameters to provide the load demand with an objective function of minimum cost of energy (COE). The constraints of the proposed approach are the loss of power supply probability, and the excess energy fraction. The proposed approach allows the combination of different sources of energy to provide the best configuration of the hybrid system. Therefore, the proposed system was optimized and compared with a WT/PV/biomass/battery storage-based hybrid energy system. This study proposes three different optimization algorithms for sizing and minimizing the COE, including the whale optimization algorithm (WOA), firefly algorithm (FF) and particle swarm optimization (PSO) and the optimization procedure was executed using MATLAB software. The outcomes of these algorithms are contrasted to select the most effective, and the one providing the minimum COE is chosen based on statistical analysis. The results indicate that the proposed hybrid WT/PV/biomass/pump-hydro storage energy system is environmentally and economically practical. Meanwhile, the outcomes demonstrated the technical feasibility of a pump-hydro energy storage system in expanding the penetration of renewable energy sources compared to other existing systems. The COE of the pumped-hydro storage hybrid system was found to be lower (0.215 $/kWh) than that with batteries storage hybrid system (0.254$/kWh) which was determined using WOA at the same load demand

Hyperparameter Search for Machine Learning Algorithms for Optimizing the Computational Complexity

For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms—the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale Optimization, and the Particle Swarm Optimization—to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms

Hyperparameter Search for Machine Learning Algorithms for Optimizing the Computational Complexity

For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms—the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale Optimization, and the Particle Swarm Optimization—to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms

A study of four-phase fluid and thermal enhancement based on tetra-hybrid nanofluid considering temperature jump on a spinning sphere

The purpose of the current development is to investigate solar thermal radiations using advanced tetra-hybrid nano-structures in advanced industrial applications. The inclusion of tetra-hybrid nanoparticles into thermal energy storage devices is a vital mechanism while suspension nanoparticles can be treated as solar absorbers, absorbing solar light and converting it to heat. The current study reveals thermal efficiency by adding four kinds of nanoparticles in a time-dependent Casson fluid on a spinning sphere. Solar radiation, chemical reaction, heat sink and Soret effect, Dufour impact and electrohydrodynamic flow. Free-stream velocity changes with changes in time while the energy equation is tackled with convective boundary constraints. Similarity variables perform the role regarding conversion of (PDEs) partial-differential equations into desired (ODEs) ordinary-differential equations. Simulations have been tackled by a modified numerical approach called the finite element technique. Consequences are derived as by enhancing values E1 and A, it was found that velocity curves increase while velocity fields diminish when β and M. The Temperature curve is enhanced with large values of Hs,A and Ec and the temperature curve diminishes with large values of the Dufour parameter

RNA-RBP interactions recognition using multi-label learning and feature attention allocation

Abstract In this study, we present a sophisticated multi-label deep learning framework for the prediction of RNA-RBP (RNA-binding protein) interactions, a critical aspect in understanding RNA functionality modulation and its implications in disease pathogenesis. Our approach leverages machine learning to develop a rapid and cost-efficient predictive model for these interactions. The proposed model captures the complex characteristics of RNA and recognizes corresponding RBPs through its dual-module architecture. The first module employs convolutional neural networks (CNNs) for intricate feature extraction from RNA sequences, enabling the model to discern nuanced patterns and attributes. The second module is a multi-view multi-label classification system incorporating a feature attention mechanism. The second module is a multi-view multi-label classification system that utilizes a feature attention mechanism. This mechanism is designed to intricately analyze and distinguish between common and unique deep features derived from the diverse RNA characteristics. To evaluate the model's efficacy, extensive experiments were conducted on a comprehensive RNA-RBP interaction dataset. The results emphasize substantial improvements in the model's ability to predict RNA-RBP interactions compared to existing methodologies. This advancement emphasizes the model's potential in contributing to the understanding of RNA-mediated biological processes and disease etiology

Towards distributed based energy transaction in a clean smart island

Over the last few decades, the use of renewable and clean energy sources has been the core of attention of the researchers. Besides, utilization of the energy hubs as a great innovation for future multi-carrier energy systems is well perceived for integrating intermittent sources of energy into the energy systems, which is surged and bolded in recent years. In this regard, this paper proposes an effective distributed energy management framework for modeling and optimal operation management of clean smart islands based on primal-dual method of multipliers. The primal-dual method of multipliers approach has shown superior performance compared to the alternating method of multipliers for distributed optimization. In this study, two different agents including a smart energy hub and a microgrid comprises of renewable and clean energy sources are considered in the smart island. These two distinct energy systems are assumed to be equipped with communication apparatuses and are intended to negotiate over the energy they need in a proper and completely distributed manner. Results show the effectiveness, accuracy and applicability of the approach for energy communication.The authors are grateful to the Raytheon Chair for Systems Engineering for funding

Global warming’s grip on agriculture: Strategies for sustainable production amidst climate change using regression based prediction

The intersection of climate change and food production is emerging as a critical area of research, focusing on both the potential benefits and the significant challenges posed by changing climate conditions. Elevated levels of carbon dioxide alongside rising global temperatures could theoretically boost crop yields, benefiting both human and animal consumption. This study examines the impact of various climate variables—temperature, humidity, precipitation, and soil moisture—on the primary production of essential foods such as rice, wheat, livestock, milk, eggs, vegetables, and fruits. Utilizing data from different countries spanning from 2000 to 2020, drawn from world development indicators, this research employs econometric analysis coupled with deep learning-based cluster analysis. Additionally, it projects future production trends up to 2100 using the moving average time series forecasting method. The findings reveal a direct correlation between climate variables and the production levels of vegetables and other food items, highlighting the immediate effects of climatic changes on agriculture. The study also points out the uneven distribution of these climate impacts, with developing countries facing more severe challenges due to their limited resources and adaptive capacities. This uneven impact contributes to increased uncertainty in food supply and affects market stability. Furthermore, concerns about food safety are intensifying under the influence of climate change, although some regions have implemented effective food conservation and control measures to mitigate these risks. This research underscores a complex landscape where the risks and benefits of climate change on food production are not uniformly distributed, but rather are influenced by a myriad of factors including geographic location, economic conditions, and the level of technological advancement in food safety practices. The nuanced understanding of these dynamics is crucial for developing targeted strategies to enhance food security in the face of a changing climate

A Novel Renewable Power Generation Prediction Through Enhanced Artificial Orcas Assisted Ensemble Dilated Deep Learning Network

The different energy resource generation tends to have high-level variation, making the power supply complex for the end-users. Because of the intermittent nature, the variations occur by time, weather conditions, and output energy. Hence, this research aims to develop a new “Renewable Power Generation Prediction (RPGP)” model using Deep Learning (DL) to give the end user a reliable power supply. The data aggregation process initially accumulated the data in a normalized and structured format. Then, the data cleaning and scaling are performed to decrease the outliers and varying ranges of values. A higher-order statistical feature was attained from the cleaned and scaled data. This statistical feature was given to “Optimal Weight Computation Ensemble Dilated Deep Network (OWC-EDDNet)” to predict generated power. In this EDDLNet, networks such as “Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN)” are employed to predict the renewable generated power. Finally, the prediction score attained from all deep networks is multiplied by the optimized weight to get the final prediction outcome, where the weights are optimally determined with the support of the Enhanced Artificial Orcas Algorithm (EAOA). The extensive empirical results were analyzed among traditional algorithms and prediction models to showcase the efficacy of the designed energy generation prediction scheme

Investigating the mediating role of ethical issues and healthcare between the metaverse and mental health in Pakistan, China, and Saudi Arabia

This article examines the mediating role of ethical issues and healthcare on the relationship between the Metaverse and mental health. It also investigates the impact of the Metaverse on ethical issues and healthcare. It is based on quantitative methodology. Using a purposive sampling technique, a close-ended questionnaire was used to collect data from 392 nurses and doctors across Pakistan, China, and Saudi Arabia. The Partial Least Squares Structural Equation Modelling technique was used for the analysis. The findings show a significant mediating role of ethical issues between the Metaverse and mental health. The results do not support the mediating role of healthcare between the Metaverse and mental health. In addition, the findings also show a positive relationship between the Metaverse and ethical issues and between ethical issues and mental health. Similarly, the findings also support the relationship between the Metaverse and healthcare. The results do not support the relationship between healthcare and mental health. The study has many implications for technology developers, scientists, policymakers, and healthcare providers