Real-time Meteorological Data Research Articles

Comparative Analysis of Meteorological versus In Situ Variables in Ship Thermal Simulations.

Thermal simulations have become increasingly popular in assessing energy efficiency and predicting thermal behaviors in various structures. Calibration of these simulations is essential for accurate predictions. A crucial aspect of this calibration involves investigating the influence of meteorological variables. This study aims to explore the impact of meteorological variables on thermal simulations, particularly focusing on ships. Using TRNSYS (TRaNsient System Simulation) software (v17), renowned for its capability to model complex energy systems within buildings, the significance of incorporating meteorological data into thermal simulations was analyzed. The investigation centered on a patrol vessel stationed in a port in Galicia, northwest Spain. To ensure accuracy, we not only utilized the vessel's dimensions but also conducted in situ temperature measurements onboard. Furthermore, a dedicated weather station was installed to capture real-time meteorological data. Data from multiple sources, including Meteonorm and MeteoGalicia, were collected for comparative analysis. By juxtaposing simulations based on meteorological variables against those relying solely on in situ measurements, we sought to discern the relative merits of each approach in enhancing the fidelity of thermal simulations.

Comparison of personal exposure to black carbon levels with fixed-site monitoring data and with dispersion modelling and the influence of activity patterns and environment.

Short-term studies of health effects from ambient air pollution usually rely on fixed site monitoring data or spatio-temporal models for exposure characterization, but the relation to personal exposure is often not known. We aimed to explore this relation for black carbon (BC) in central Stockholm. Families (n = 46) with an infant, one parent working and one parent on parental leave, carried battery-operated BC instruments for 7 days. Routine BC monitoring data were obtained from rural background (RB) and urban background (UB) sites. Outdoor levels of BC at home and work were estimated in 24 h periods by dispersion modelling based on hourly real-time meteorological data, and statistical meteorological data representing annual mean conditions. Global radiation, air pressure, precipitation, temperature, and wind speed data were obtained from the UB station. All families lived in the city centre, within 4 km of the UB station. The average level of 24 h personal BC was 425 (s.d. 181) ng/m3 for parents on leave, and 394 (s.d. 143) ng/m3 for working parents. The corresponding fixed-site monitoring observations were 148 (s.d. 139) at RB and 317 (s.d. 149) ng/m3 at UB. Modelled BC levels at home and at work were 493 (s.d. 228) and 331 (s.d. 173) ng/m3, respectively. UB, RB and air pressure explained only 21% of personal 24 h BC variability for parents on leave and 25% for working parents. Modelled home BC and observed air pressure explained 23% of personal BC, and adding modelled BC at work increased the explanation to 34% for the working parents. Short-term studies of health effects from ambient air pollution usually rely on fixed site monitoring data or spatio-temporal models for exposure characterization, but the relation to actual personal exposure is often not known. In this study we showed that both routine monitoring and modelled data explained less than 35% of variability in personal black carbon exposure. Hence, short-term health effects studies based on fixed site monitoring or spatio-temporal modelling are likely to be underpowered and subject to bias.

Optimal design and analyzing the techno-economic-environmental viability for different configurations of an autonomous hybrid power system

AbstractIn the present day, there is widespread acceptance of autonomous hybrid power systems (AHPSs) that rely on renewable energy sources (RESs), owing to their minimal adverse effects on the environment. This paper evaluates and compares three various AHPS configurations comprising photovoltaic (PV) modules, wind turbines (WTs), batteries, and diesel generators (DGs), using a recent optimization approach. A new optimizer 'Dandelion-Optimizer' (DO) is applied to tackle design problems. Real-time meteorological data from Siwa Oasis in northwest Egypt was utilized to determine an optimum design of system components for the purpose of providing sustainable power to this remote region. The system configurations are effectively modelled and optimized to achieve the minimum cost of energy (COE), while also minimizing the loss of power supply probability (LPSP) and carbon dioxide (CO2) emissions. As per the results, the last configuration (PV with both backup equipment) is the most optimal one in terms of the lowest cost, whereas the first configuration (PV and WT with both types of backup equipment) is the most optimal one with regards to the lowest carbon emissions.

Weather Prediction using Machine Learning

This research leverages machine learning, incorporating the OpenWeather API, for advanced weather prediction. By harnessing historical and real-time meteorological data, including temperature, humidity, and wind speed, the model enhances accuracy. Various machine learning algorithms are explored for optimal Rainfall prediction, emphasizing efficiency and adaptability. Integration with the OpenWeather API enables real-time forecasting, with continuous updates for sustained precision. The development of a user-friendly interface broadens access to predictions, benefiting farmers, disaster management, and the public. This innovative approach, combining machine learning and the OpenWeather API, demonstrates a promising advancement in weather prediction for informed decision-making

Ultra-short-term forecasting of agricultural intelligent greenhouse power load based on ISDS and MDUS-LSTM

The agricultural smart greenhouse electric load differs significantly from the traditional building electric load. It is more susceptible to the influence of meteorological conditions, featuring poor regularity and significant random fluctuations, so load forecasting faces new challenges. To address this challenge, this paper focuses on a variety of meteorological and historical similar feature extraction, innovates model updating strategies, and proposes a new ultra-short-term forecasting method for agricultural smart greenhouse electric loads. Firstly, an improved similar day selection (ISDS) method is designed. This method considers both trend similarity and magnitude similarity of time series. It sets weights according to the degree of influence of different meteorological features on load, thus improving the learning efficiency of the model. Next, a model dynamic update strategy (MDUS) is designed. This strategy consists of initial training of forecasting model parameters based on historical similar daily loads and online updating of forecasting model parameters based on adjacent daily load data. Then, the forecasting model is trained online and fine-tuned with the parameters based on the adjacent daily load data. The dynamically updated forecasting model is used to achieve ultra-short-term forecasting of the electric load to improve the forecasting accuracy and adaptability of the model. Finally, the effectiveness of the proposed method is verified by actual electrical load data, real-time meteorological data, and NWP data collected in an agricultural smart greenhouse in Shouguang, China.

A calculation algorithm for ship pollutant gas emissions and diffusions based on real-time meteorological conditions and its application

Most literature focusing on the issue of ship pollutant emission and diffusion is based on static meteorological conditions. The relevant literature seldom analyzes the emission and diffusion of ship pollutant gas according to real-time meteorological data when wind direction, wind speed and other meteorological conditions have changed. This paper presents a fast translational diffusion algorithm, and empirically demonstrates it through ship and meteorological data from the Huangpu River field in Shanghai. First, CO2 emissions were estimated using the actual speed of the ship engine changed under the influence of variable wind, and simulation analysis was performed to the scenario of hourly diffusions. The research findings are as follows: In comparison with the traditional algorithms, the fast algorithm can significantly improve calculation efficiency, save calculation time and achieve the long-distance and long-time parallel calculation of multiple ships. As proven by the empirical research using the ships on Huangpu River in Shanghai, the maximum difference of CO2 emissions is 34.5%, 36.85% and 19.97% respectively after upwind, downwind and real-time meteorological conditions are taken into account. The research finding indicates that wind direction changes have a greater impact on the carbon emission of ships.

A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting.

Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson's correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results.

Study on calculation model and risk area of radionuclide diffusion in coastal waters under nuclear leakage accidents with different levels

Abstract Study on calculation model and risk area of radionuclide diffusion in coastal waters under nuclear leakage accidents with different levels can help predict and evaluate consequences of radionuclide leakage accidents. Thus they play an important role in emergency response and accident mitigation. In the first step of the study, a climate hydrodynamic model in China coastal waters was established based on the climate data. In the next step, according to the real-time meteorological data, a hydrodynamic model in coastal waters of Haiyang nuclear power station was founded using the result of the climate hydrodynamic as a boundary. Then, according to the result of the hydrodynamic model in coastal waters of Haiyang nuclear power station, a radionuclide diffusion model in coastal waters of Haiyang nuclear power station was set up, in which the Euler method was adopted. With the radionuclide diffusion model, the total leaked radioactivity of radionuclides was set from 1018 Bq to 1012 Bq with a decrease of every two orders of magnitude. Thus, scenarios of radionuclide diffusion under assumed nuclear leakage accidents with different levels were calculated and their corresponding risk area were analyzed under the assumption that radionuclides leaked for consecutive five days. The results show that when the leaked radioactivity of radionuclides is 1018 Bq, the risk area on the seventh day is about 41 km east, 22 km south and 19 km west of the power station; on the fourteenth day, the risk area is about 65 km east, 22 km south and 25 km west of the power station. When the total leaked radioactivity of radionuclides declines by two orders of magnitude, the risk area will be reduced by about 10 km–20 km in the east direction accordingly. When it declines to 1014 Bq, the risk area decreases sharply to a small area. When it declines to 1012 Bq, the risk area is barely found. This model was verified from two aspects, namely the flow field and the radionuclide concentration. Hydrodynamic results can well describe the Yellow Sea cold water mass, Yellow Sea warm current and tidal current. Changes of radioactivity in different positions are fundamentally consistent with that in Fuikushima nuclear leakage accident. It indicates the hydrodynamic model and radionuclide diffusion model in the study are feasible and reliable.

Optimal Configuration of a Hybrid Photovoltaic/Wind Turbine/Biomass/Hydro-Pumped Storage-Based Energy System Using a Heap-Based Optimization Algorithm

Recently, renewable energy resources (RESs) have been utilized to supply electricity to remote areas, instead of the conventional methods of electrical energy production. In this paper, the optimal design of a standalone hybrid RES comprising photovoltaic (PV), wind turbine (WT), and biomass sources as well as an energy storage system, such as a hydro-pumped storage system, is studied. The problem of the optimal sizing of the generating units in the proposed energy system is formulated as an optimization problem and the algorithms heap-based optimizer (HBO), grey wolf optimizer (GWO), and particle swarm optimization (PSO) are applied to achieve the optimal sizing of each component of the proposed grid-independent hybrid system. The optimization problem is formulated depending on the real-time meteorological data of the Ataka region on the Red Sea in Egypt. The main goal of the optimization process is to minimize the cost of energy (COE) and the loss of power supply probability (LPSP), while satisfying the constraints of system operation. The results clarify that the HBO algorithm succeeded in obtaining the best design for the selected RE system with the minimum COE of 0.2750 USD/kWh and a net present cost (NPC) of USD 8,055,051. So, the HBO algorithm has the most promising performance over the GWO algorithm in addressing this optimization problem.

Estimation of safe cover thickness to steel reinforcement in coastal structures addressing several tidal stations

Corrosion of steel in reinforced and prestressed concrete members exposed to a marine tidal zone is a serious durability problem. This study presents a methodology for rationally estimating the safe cover thickness for steel in concrete structures exposed to the tidal zone. The proposed methodology was implemented for the case of six Indian coastal stations using 100-year-long simulations of chloride transport in concrete. Real-time meteorological and tidal height data were used for simulating the model boundary conditions. Using simulated results of chloride threshold depths (CTDs), safe cover thickness to steel was assessed. Estimated safe cover thicknesses to steel across coastal stations differed significantly (25–30 mm), indicating a need for corrosion vulnerability zonation of coastlines. Conventionally used tide models were improved by superimposing with an annual component of astronomical tides, which expanded the range of wet-dry regimes simulated by the tide models. The study also compared the chloride ingress predictions from the modified tide models with those from real tides. The superimposed tide model was found to be a better approximation of real tides than the diurnal tide model. The modified tide models very well simulated chloride ingress around the critical sections in the tidal zone.

Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth. Rice is propagated from the seeds of paddy and it is a stable food almost used by fifty percent of the total world population. The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains. This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques. Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy. There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind, relative humidity, Instant Wind Speed in paddy cultivation. The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series. A Robust Optimized Artificial Neural Network (ROANN) Algorithm with Genetic Algorithm (GA) and Multi Objective Particle Swarm Optimization Algorithm (MOPSO) proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation. A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database. The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database. Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm. The reason for improving the growth of paddy was identified using the output of the Neural Network. Performance metrics such as Accuracy, Error Rate etc were used to measure the performance of the proposed algorithm. Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.

Personal exposure to black carbon in Stockholm, Sweden, compared to fixed-site monitoring and dispersion modelling

INTRODUCTION AND AIM Short-term studies of health effects from ambient air pollution usually rely on fixed site monitoring data or spatio-temporal models for exposure characterization, but the relation to personal exposure is often not known. We aimed to explore this relation for black carbon (BC) in central Stockholm. METHODS Families (n=36) with an infant, one parent working and one parent on leave, agreed to perform personal measurements of BC for the parents, with battery-operated aethalometers for 7 days. Routine monitoring data were from rural background (RB) and urban background (UB). Outdoor levels at home and work were estimated in 24h periods by dispersion modelling with real-time meteorological data. Global radiation, air pressure, precipitation, temperature, and wind speed data were achieved from an urban background station. All families lived within 4 km from the city center. RESULTS The average level of 24h personal BC was 370 (s.d. 200) ng/m3 for parents on leave, and 357 (s.d. 175) ng/m3 for working parents. The corresponding fixed-site monitoring observations were 148 (s.d. 139) at RB and 317 (s.d. 149) at UB. Modelled BC at home and at work were 493 (s.d. 228) and 331 (s.d. 173) ng/m3, respectively. UB, RB and air pressure explained only 21% of personal 24h BC variability for parents on leave and 25% for working parents. Modelled home BC and air pressure explained 23% of personal BC, and adding modelled BC at work increased the explanation to 34% for the working parents. CONCLUSION Both routine monitoring and modeled data explained less than 35% of variability in personal 24h observations of BC exposure. Any associated short-term health effects are likely to be underestimated in studies using such exposure data. KEYWORDS Black carbon, Personal exposure, Dispersion modelling

A Fuzzy Framework for Self-Aware Wireless Sensor Networks

We have developed a fuzzy logic framework to simulate self-awareness in wireless sensor networks (WSNs) that envisages data transmission losses due to abnormalities in the operating environment. Fuzzy logic is selected to model the awareness, because it requires less data to encode, and occupies less memory, making it suitable for sensor nodes with limited resources. We view WSN as a multi-layered infrastructure, where a central-server in the top-layer manages the gateways in the intermediate-layer, and the gateways are aggregating the raw sensed data from the bottom layer’s sensors. The self-awareness is simulated using Mamdani Fuzzy inference system (FIS) that utilizes a custom-defined membership functions of the selected parameters, such as temperature, humidity, wind speed, and battery residual-level to guide WSN through its data transmissions. The input and output parameters are defined with three linguistic variables (low, medium and high) and a predetermined set of rules is applied to the selected FIS to determine the impact level. The impact is mapped into packet error rate and the gateway then decides whether to carry out the data transmissions while being aware of the selected uncertainties and their impact. The experimental results on a multivariate dataset integrating real-time meteorological data with randomly generated battery voltage revealed that the proposed FIS framework demonstrated 38% energy savings by non-executing unworthy communications against normal transmissions.

A web-based GIS platform supporting innovative irrigation management techniques at farm-scale for the Mediterranean island of Crete

The aim of this paper is the creation of an integrated and free-access web platform for parcel irrigation water management on a large spatial scale (Water District of Crete, in Greece) in order to: a) accurately determine the irrigation needs of the main crops for Crete such as olives, citrus, avocados and vineyards, b) design strategies, for optimal adaptation of the agricultural sector in the context of climate change, and c) incorporate the dynamic integration of the above information through the creation of a digital platform. In the proposed decision-making system, essential factors are taken into account, such as real-time meteorological data, information about the type and spatial distribution of the agricultural parcels in Crete, algorithms for calculation crop evapotranspiration per development stage and age of the crops, satellite remote sensing techniques in combination with field surveys to depict accurate soil texture map for the whole island of Crete as well as sustainable cultivation practices for saving water per crop and parcel geomorphology. Based on the proposed decision-making system, users will have the opportunity in any specific location/farm in Crete to know the irrigation needs of the crops in real-time and obtain information about proper climate-water adaptation practices. The main novelty points of the proposed platform include the derivation of parcel-level soil texture data from Sentinel-2 satellite imagery and field samples, the comprehensiveness of the irrigation management information, the relatively low data requirements and the application interface simplicity provided to the end-user.

Research on Ultra-Short-Term Prediction Model of Wind Power Based on Attention Mechanism and CNN-BiGRU Combined

With the rapid development of new energy technologies and aiming at the proposal of the “DOUBLE CARBON” goal, the proportion of wind energy and other new sustainable energy power solutions in the power industry continues to increase and occupy a more critical position. However, the instability of wind power output brings serious challenges to safe and stable power grid operations. Therefore, accurate ultra-short-term wind power prediction is of great significance in stabilizing power system operations. This paper presents an ACNN-BiGRU wind power ultra-short-term prediction model based on the Attention mechanism, the fusion of convolutional neural network (CNN), and bidirectional gated recurrent unit (BiGRU). The model takes a single wind turbine as the prediction unit and uses the real-time meteorological data in the wind farm, the historical power data of the wind turbine, and the real-time operation data for parallel training. Then, it extracts the key features of the input data through CNN and uses the BiGRU network to conduct bidirectional modeling learning on the dynamic changes of the features proposed by CNN. In addition, the Attention mechanism is introduced to give different weights to BiGRU implicit states through mapping, weighting, and learning parameter matrix to complete the ultra-short-term wind power prediction. Finally, the actual observation data of a wind farm in Northwest China is used to verify the feasibility and effectiveness of the proposed model. The model provides new ideas and methods for ultra-short-term high-precision prediction for wind power.

Analysis of Artificial Neural Network: Architecture, Types, and Forecasting Applications

The artificial neural network reduces humanity and society’s burden to solve complex problems highly efficiently. Artificial neural networks resemble brain activities based on the acquired training samples used for various applications such as classification, regression, prediction, smart grid, natural language processing, image processing, medical diagnosis, and so on. This paper illustrates the different artificial neural network architectures, types, merits, demerits, and applications. Therefore, this paper provides valuable information to students and researchers to enrich their knowledge about an artificial neural network and research it. This paper also proposed a multilayer-perceptron-neural-network-based solar irradiance forecasting model, an improved backpropagation neural network-based rainfall forecasting model, and an Elman neural network-based temperature forecasting model. The performances of the proposed neural network-based forecasting models are analyzed with various hidden neurons and validated using the acquired real-time meteorological data. The proposed neural network forecasting models achieve rigorous results with reduced errors for the considered applications and aid sustainability.

Analysis and Prediction of Meteorological Data Based on Edge Computing and Neural Network

In this work, aiming at the problem of missing element values in real-time meteorological data, we propose a radial basis function (RBF) neural network model based on rough set to optimize the analysis and prediction of meteorological data. In this model, the relative humidity of a single station is taken as an example, and the meteorological influencing factors are reduced by rough set theory. The key factors are used as the input of RBF neural network to interpolate the missing data. The experimental results show that the interpolation effect of the model is significantly higher than that of the linear interpolation method, which provides an effective processing method for the lack of real-time meteorological data, and improves the analysis and prediction effect of meteorological data.

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

Sustenance of Indian Moored Buoy Network During COVID-19 Pandemic – A Saga of Perseverance

The moored buoy network in the Indian Ocean revolutionized the observational programs with systematic time-series measurement of in situ data sets from remote marine locations. The real-time meteorological and oceanographic data sets significantly improved the weather forecast and warning services particularly during extreme events since its inception in 1997. The sustenance of the network requires persistent efforts to overcome the multitude of challenges such as vandalism, biofouling, rough weather, corrosion, ship time availability, and telemetry issues, among others. Besides these, the COVID-19 pandemic constrained the normal functioning of activities, mainly by delaying the maintenance of the network that resulted in losing a few expensive buoy system components and precious data sets. However, the improvements in the buoy system, in-house developed data acquisition system, and efforts in ensuring the quality of measurements together with “best practice methods” enabled 73% of the buoy network to be functional even when the cruises were reduced to 33% during the COVID-19 lockdown in 2020. The moored buoys equipped with an Indian buoy data acquisition system triggered high-frequency transmission during the Super cyclone Amphan in May 2020, which greatly helped the cyclone early warning services during the COVID-19 pandemic. The COVID-19 lockdown points toward the reliability and enhanced utility of moored buoy observations particularly when other modes of measurements are limited and necessitates more such platforms to better predict the weather systems. The present study analyzed the enhancement of the buoy program and improvisation of the buoy system that extended the life beyond the stipulated duration and enabled the high-frequency data transmission during cyclones amid the COVID-19 lockdown. The recommendations to better manage the remote platforms specifically in the event of a pandemic based on the operational experience of more than two decades were also presented.

Optimal design of stand-alone hybrid PV/wind/biomass/battery energy storage system in Abu-Monqar, Egypt

The objective of smart power systems is to combine all renewable energy sources in order to increase the electricity supply of clean energy sources. This paper proposes an optimization model for minimizing the energy cost (EC) and enhancing the power supply for rural areas by designing and analyzing three different hybrid system configurations based on integrating a biomass system with a photovoltaic (PV), wind turbine (WT) and battery system. The first hybrid system includes PV, WT, Biomass generator, and Battery storage device; the second configuration includes PV with Biomass and Battery, and the last one includes WT with Biomass and Battery. The control parameters are kept the same for both algorithms in all case studies. Real-time meteorological data are used for a remote area located in the western desert of Egypt called Abu-Monqar village. Four recent optimization algorithms, namely Slime Mould Algorithm (SMA), Seagull optimization algorithm (SOA), gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) are utilized and compared with each other to ensure that all load demand is met at the lowest energy cost (EC) for the proposed hybrid system. Based on the comparison of the obtained results and the convergence curves for the three scenarios revealed that the SMA outperformed the other algorithms in terms of the best objective function. The obtained results revealed that the third scenario using SMA method provides the optimal configuration in terms of the net present cost (NPC), EC, and LPSP with 3,476,371.76$, 0.1186861 $ /kWh, and 0.032493, respectively.1)Motivations