Efficient Energy Generation Research Articles

A Meta-Analysis Review: Nanoparticles as a Gateway to Optimized Boiling Surfaces.

Pool boiling is essential in many industrial manufacturing applications. In addition, it can become critical in the journey towards improving energy generation efficiency and accomplishing the goal of net-zero carbon emissions by 2050 via new or traditional power generation applications. The effectiveness of boiling is governed by the bubble cycle. The chemistry and topographical features of the surface being heated have been found to highly impact the boiling performance, such as in the case of pool boiling enhancement when employing hydrophilic and hydrophobic surfaces via nano/micro heater surface modification. Nevertheless, it is questionable how feasible it is to create these surfaces for large-scale applications due to their manufacturing and maintenance cost and complexity. The current work assesses whether the use of nanoparticles in traditional coolants could potentially unlock the mass production of optimised heating surface modification through a metadata literature review analysis. It was discovered that self-assembled layers created as a result of the deposition of nanoparticles in coolants undergoing pool boiling seem to behave most similarly to manufactured hydrophilic surfaces. The creation of enhanced patterned-heat transfer surfaces is shown to be possible via the use of a combination of different nanoparticle suspensions in coolants.

Recent developments and diverse applications of high melting point materials

High melting point materials are essential to many different industries because of their unique qualities, rendering them invaluable for various uses. This paper investigates the unique features of high melting point materials, summarizes current advancements in the field, and outlines the vast range of applications that these materials have in domains like materials science, aerospace, energy, and electronics. The discourse covers various materials, such as carbon-based, ceramics, intermetallic, and refractory metals, emphasizing their potential uses and new roles in contemporary technology. Thanks to developments in material science, new high melting point materials with enhanced mechanical strength, thermal stability, and resistance to harsh environments have been found and synthesized. These materials are used in manufacturing, electronics, aircraft, and energy. They are frequently distinguished by their resistance to temperatures significantly higher than those of ordinary materials. High melting point materials are used in the aerospace sector to manufacture heat-resistant parts that improve spacecraft and jet engines' overall performance and longevity. These materials are also essential to the energy sector's creation of sophisticated thermal management systems for nuclear reactors and high-temperature environments, improving energy generation efficiency and security. The electronic industry benefits from high melting point materials when producing semiconductors and other electronic components since their stability guarantees dependable operation even in harsh circumstances.Furthermore, high melting point materials are becoming increasingly popular in additive manufacturing procedures, allowing complex structures with exceptional heat resistance to be produced for various uses. Moreover, high melting point alloys and composites have been developed due to advances in metallurgy and nanotechnology, broadening their potential uses in industries including automobile engineering, medical technology, and military technology. The interdisciplinary aspect of high melting point material research and development is shown in this abstract, which also highlights the adaptability of materials and their revolutionary effects on various industries. The continuous investigation of these materials shows promise for future discoveries that will open up new avenues and applications in fields vital to the development of contemporary society.

Geological survey techniques and carbon storage: Optimizing renewable energy site selection and carbon sequestration

Geological survey techniques play a crucial role in optimizing site selection for renewable energy projects and identifying suitable locations for carbon storage to mitigate climate change. This abstract provides an overview of how geological survey techniques can be used to achieve these objectives. Renewable energy development, particularly solar and wind power, requires careful site selection to maximize energy generation efficiency and minimize environmental impacts. Geological surveys are instrumental in assessing factors such as subsurface geology, topography, soil composition, and hydrological conditions. These surveys help identify suitable locations with optimal wind or solar resources and geologic conditions for infrastructure development. Additionally, geological surveys are essential for identifying suitable sites for carbon storage, a critical component of carbon capture and storage (CCS) technologies aimed at reducing greenhouse gas emissions. Geological formations, such as deep saline aquifers, depleted oil and gas reservoirs, and unmineable coal seams, can serve as storage reservoirs for captured carbon dioxide (CO2). Geological surveys help characterize these formations to assess their suitability for long-term CO2 storage, considering factors such as porosity, permeability, and sealing integrity. Optimizing site selection for renewable energy projects and carbon storage requires a comprehensive understanding of subsurface geology and environmental conditions. Advanced geological survey techniques, such as seismic imaging, remote sensing, and geophysical surveys, are essential for acquiring detailed subsurface data. These techniques enable scientists and engineers to assess site suitability, evaluate risks, and design effective mitigation measures. In conclusion, geological survey techniques are invaluable tools for optimizing site selection for renewable energy projects and identifying suitable locations for carbon storage. By leveraging these techniques, stakeholders can make informed decisions that promote sustainable energy development and mitigate the impacts of climate change.

Harnessing Machine Learning to Optimize Renewable Energy Utilization in Waste Recycling

This research explores the application of Machine Learning techniques in utilizing renewable energy for the recycling process. As the world strives for sustainable solutions to meet energy needs and waste management challenges, this study investigates the integration of Machine Learning algorithms to optimize the production of renewable energy from waste recycling. By employing these algorithms, the research aims to enhance the efficiency and effectiveness of renewable energy generation while promoting environmentally responsible waste management practices. The study encompasses comprehensive data analysis from various recycling facilities, identifying energy consumption patterns and evaluating energy-saving opportunities. The findings reveal that applying Machine Learning can reduce energy consumption by up to 30%, increase recycling output, and decrease greenhouse gas emissions. These results highlight the potential benefits and challenges of implementing smart technology in the recycling process for renewable energy production. Furthermore, the research offers insights into how integrating Machine Learning can support long-term sustainability and significantly contribute to improved environmental management. Consequently, this study paves the way for a cleaner and more sustainable future, inspiring the broader adoption of innovative techniques within the waste management and renewable energy industries.

Co-extruded triple-layer micro-tubular solid oxide fuel cell: The influence of cathode extrusion rate on the fuel cell properties and performance

Micro-tubular solid oxide fuel cells (MT-SPFC) have emerged as a potential alternative for efficient energy generation. This study investigates the impact of cathode extrusion rates (ranging from 3 to 6 mL min−1) on the triple layer anode/electrolyte/cathode MT-SOFC fabricated via a simplified phase inversion-based co-extrusion/co-sintering technique. Higher cathode extrusion rates (6 mL min−1) indirectly thin the electrolyte layer, improving ion hopping efficiency between the cathode and anode. Moreover, increasing the extrusion rate enhances anode thickness, providing ample electrode reaction sites and thereby enhancing the gas diffusion process. The C6 sample attains a peak power density of 1.46 W cm−2 with 1.08 V OCV at an optimum 800 °C operating temperature, which is high for MT-SOFCs in high-temperature applications. There was a 71.8 % increase in power density for C6 when the temperature changed from 750 °C to 800 °C. The composite cathode material fulfilled both the electronic and ionic conductivity requirements. The optimal cathode extrusion rate for this simplified MT-SOFC fabrication was found to be 6 mL min−1.

A Modified Perturb and Observe Algorithm in Photovoltaic Maximum Power Point Tracking System

Abstract: The power output of the photovoltaic (PV) array is nonlinear, and maximum power point tracking (MPPT) is required to boost the efficiency of solar energy generation. The traditional perturb and observe algorithm is frequently used, but it is difficult to comprehensively consider the tracking accuracy and response speed at the same time. Misjudgment occurs when the solar insolation changes drastically. In view of these shortcomings, this paper presents a variable step size threshold search algorithm. The modified algorithm can quickly track the maximum power point and restrain fluctuations near the maximum power point. Matlab is utilized to simulate and prove the effectiveness of this modified algorithm.

Ultramicrotomy‐Assisted Fabrication of Nanochannels for Efficient Ion Transport and Energy Generation

AbstractNano‐ and angstrom‐scale channels fabricated from 2D‐layered materials provide a unique platform for studying fluidic behavior at atomic‐scale confinement, with applications in desalination, osmotic power generation, and fuel cells. While various fabrication methods exist, achieving precision, scalability, and minimal fabrication time is challenging. Ultramicrotomy‐based nanofabrication is shown here as an efficient approach to create nanofluidic membranes containing nanochannels with atomically flat walls. This approach is compatible with both bottom–up nanolaminates and top–down nanochannels, produces multiple devices from the same 2D assembly and allows swift variation of channel length. Integration of these membranes into macroscopic silicon fluidic‐chips is achieved maintaining the structural and functional properties. The robustness of the sliced membranes is demonstrated through restacked vermiculate laminates that sustain applied pressures and generate ionic streaming currents. Sliced pristine vermiculite channels exhibit charge‐selective ion transport, leading to osmotic power density (Posm/A) ranging from 9.2 to 300.4 Wm−2 under various KCl concentration gradients. Maximum conversion efficiency of 23.3% is obtained at a 100‐fold gradient, yielding Posm/A of 234.6 Wm−2. Short channel lengths sliced by ultramicrotomy contribute to high Posm/A, promising applications of miniature energy devices for pressure‐driven electricity generation and osmotic power generation.

Significant augmentation of hydrogen and benzaldehyde production through mediator-free in-situ synthesis of CuNi/CN photocatalysts for benzyl alcohol splitting

Amidst growing concerns surrounding global warming and energy scarcity, there is an urgent demand for more efficient and environmentally friendly methods of hydrogen production. In response, we introduce a novel mediator-free in-situ approach for synthesizing CuNi/CN composite photocatalysts. This method entails the synthesis of copper and nickel complexes to fabricate bimetallic Cu and Ni nanoparticles on carbon nitride (CN). These catalysts are deployed for the photocatalytic dehydrogenation of benzyl alcohol, resulting in the generation of hydrogen and benzaldehyde. The CuNi bimetal-loaded CN structure provides ample active sites and demonstrates a low hydrogen reduction overpotential, thereby enhancing both charge separation and surface hydrogen reduction processes. In comparison to pure CN, the composite photocatalyst exhibits significantly improved photocatalytic activity. Notably, the composite 2 % Cu1Ni1/CN photocatalyst achieves a record-high hydrogen production rate of about 80 μmol g-1h−1, representing an impressive increase of approximately 533 times compared to pristine CN. Moreover, the catalyst offers cost-effectiveness, ease of preparation, and reusability, ensuring stable hydrogen production efficiency over multiple cycles. This precious metal-free composite photocatalyst not only facilitates the synthesis of valuable compounds but also promotes sustainable advancements in efficient hydrogen energy generation.

Energy Production Optimization by Positioning of Solar Panels and Surveillance System with Cleaning of Panels

The pursuit of efficient and sustainable energy generation has led to the widespread adoption of solar panels. However, the optimal performance of these panels can be hindered by factors such as dirt accumulation, suboptimal orientation, and security concerns. To address these challenges, we propose an integrated Solar Panel Tracking, Cleaning, and Surveillance System (SPTCSS). Our system employs advanced tracking technology to optimize solar panel orientation, ensuring maximum exposure to sunlight throughout the day. Additionally, it incorporates automated cleaning mechanisms, utilizing water and brushes to remove dust, debris, and other contaminants, thereby maintaining peak efficiency. Furthermore, the surveillance aspect of the system provides real-time monitoring and security features, safeguarding against theft, vandalism, and unauthorized access. Through a network of sensors and cameras, any anomalies or suspicious activities can be promptly detected and addressed. By integrating tracking, cleaning, and surveillance functionalities into a single system, our solution offers a comprehensive approach to enhancing the performance, longevity, and security of solar panel installations. This not only maximizes energy generation but also minimizes maintenance requirements and mitigates risks, ultimately contributing to a more sustainable and reliable renewable energy infrastructure

A preliminary model of water and salt transport in a laboratory scale pressure-retarded osmosis (PRO) module

Nowadays, more and more attention is being paid to the production of green energy. Recently, energy from salinity gradients has also attracted interest. One such process that can generate useful work is the pressure-retarded osmosis (PRO), which uses a semi-permeable membrane to separate the feed and pressurized draw solutions. The semi-permeable membrane allows the transport of solvent (water) from the feed solution (diluted low pressure) side to the draw solution (concentrated high pressure) side, and thus, the excess of pressurized water on the draw side can be used to generate power, i.e., by expansion in the turbine. This work presents a preliminary numerical model developed to study the water and NaCl salt transport through the semi-preamble membrane and in the PRO module designed to perform experimental studies. The model was first verified for simple 2D module geometry. It was then used to study the flow through a simplified 3D module, mimicking the real one used in laboratory-scale experiments. The influence of the hydrodynamic pressure and the NaCl draw solution’s mass flow rate in the module on the energy generation efficiency was examined. The maximum power density obtained for half the osmotic pressure of the NaCl draw solution (i.e., for 28 bar) was found to be equal to approximately 4 W/m2. An increase in the flow rate of the draw solution causes a decrease in the thickness of the boundary layer at the membrane, which reduces the effect of the external concentration polarization. This results in an increase in the concentration difference on either side of the membrane, contributing to a non-linear increase in power density depending on this mass flow rate. Increasing the average velocity from 0.02 m/s to 0.1 m/s increases the power density by up to 80%, from approximately 2.6 to 4.7 W/m2.

SLOPE ANALYSIS OF CLOT-ADJUSTED SSI-FY4A DATA FOR SOLAR PV POWER POTENTIAL ASSESSMENT IN THE PHILIPPINES

Abstract. Utilization rate of energy from solar photovoltaic (PV) systems has surged considerably with the increase in global demand for sustainable energy solutions.The angle at which panels are positioned relative to the horizontal plane must be aligned with the sun's incoming radiation to achieve optimum power production. Unfortunately, this ideal alignment is not always feasible, particularly for panels lacking tracking systems. Therefore, determining the best tilt angle becomes a key factor that significantly influences the effectiveness of solar PV systems, considering various influencing factors. The study aims to establish the relationship between different slopes and solar PV performance and to assess the impact of this on energy generation efficiency for specific locations across Luzon and Mindanao. The study focuses on determining the optimal tilt for specific locations across Luzon, Visayas, and Mindanao. Sites including Metro Manila, Cebu, and Davao were selected as representative areas for each respective island group for being the most populous cities in their respective geographical island groups. Theoretical solar energy obtained from SSI was CLOT-adjusted and incorporated with dust and temperature effects to determine the maximum solar energy resource potential within the study area. Results show that the highest solar PV potential was determined at 5°–10° tilt angle for both Metro Manila and Davao followed by 10–20° and 20–30° tilt angle with an average of 86.42 W solar PV potential (PPV). For Cebu, the highest solar PV potential was determined at 20° to 30° tilt angle with the value of 95.99 W solar PV potential (PPV). The lowest PPV was found on installations with tilt > 30° in all the study areas at an average of 65.02 W. This suggests that to maximize solar PV output in different regions, installations should be tilted according to the optimal tilt angle specific to that region. The latitude differences across the country significantly impact which tilt angle is most effective, as they influence the amount of sunlight received in each area. To improve the accuracy of future studies, it is recommended to increase the number of data points per slope interval. While the differences observed in this study were small, having more data points can enhance the precision of average values obtained from formulas.

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Efficient energy generation from a sweat-powered, wearable, MXene-based hydroelectric nanogenerator

Efficient energy generation from a sweat-powered, wearable, MXene-based hydroelectric nanogenerator

Analysis of the energy efficiency of a system with a hybrid solar collector and thermal energy storage

The object of research is heat transfer in a hybrid thermal photovoltaic solar collector. International agreements and strategies aimed at combating climate change and reducing greenhouse gas emissions strongly call for the active implementation of renewable energy sources on a global scale. A special emphasis is placed on the development of solar energy, which has significant growth potential due to the constant improvement of technologies and cost reduction of production. With this in mind, the authors focused on the development and analysis of a computer model of an innovative hybrid system that effectively combines a solar collector for the simultaneous production of both thermal and electrical energy. The research included a detailed study of the temperature changes of the heat carrier in the hybrid photovoltaic solar collector and thermal accumulator during the period of solar irradiation. Thanks to careful monitoring, the main patterns of gradual temperature increase in both key components of the hybrid system were established. In addition, an assessment of the dynamics of changes in the instantaneous thermal power of the solar collector under the influence of various factors, such as the intensity of solar radiation, the angle of inclination of the collector, wind speed, etc., was carried out. The results of computer modeling showed the average indicator of the efficiency of the entire hybrid system, as well as its variations during a certain time of operation. In addition, the change in the instantaneous specific heat capacity and the overall efficiency of heat energy generation by the hybrid photovoltaic solar collector were analyzed. Special attention was paid to the study of the dynamics of changes in the thermal efficiency of the entire system, as well as its ability to efficiently store thermal energy in a specialized battery. The comprehensive analysis made it possible to obtain the key thermophysical parameters of the developed hybrid system with a photovoltaic solar collector. This data is extremely important, as it will allow engineers and scientists to accurately calculate the potential performance and efficiency of such a system when it is put into practical use in the future. In general, the results of the study emphasize the promising development of hybrid solar collectors as one of the leading technologies in the field of renewable energy in the context of global challenges of climate change.

Assessment of Syngas Storage Tank Hazards Taking Account of the Domino Effect

In most countries energy needs are satisfied using fossil fuels. Fossil fuel combustion involves environmental pollution and greenhouse gas emissions. The effect of the depletion of natural resources and the growing awareness of the need to protect the environment are the reasons that clean energy and alternative energy sources have been significant research issues. One of the most important technologies enabling efficient generation of low-emission energy is the gasification process of synthesis gas production. Syngas is primarily composed of hydrogen and carbon monoxide, but depending on the feedstock, it can also contain smaller concentrations, e.g., of carbon dioxide, methane and nitrogen. Because synthesis gas contains flammable and toxic substances, it may pose hazards to humans and the environment at every stage of gas production, storage, transport or final utilization if released uncontrollably. This paper presents the results of analyses related to hazards created by an uncontrollable release of synthesis gas during storage. A failure of a syngas system may cause damage to other, subsequent technological systems and facilities located in the neighborhood and containing dangerous substances. The problem gains special significance if syngas is stored in many tanks, where a failure of one may result in damage to subsequent tanks due to the so-called domino effect. The conditions in which the domino effect may occur are analyzed and the effect occurrence probability is determined depending on the mutual location of the tanks.

Investigating the optimal Photovoltaic (PV) tilt angle using the Photovoltaic Geographic Information System (PVGIS)

This work presents an investigation of the optimal tilt angles for mounting pv panels in South –South and South - East States in Nigeria when no tracking system exists using the photovoltaic geographic information system (PVGIS). Experiments were carried out to verify results obtained from the PVGIS database. The experimental set up consist of five PV panels each rated 10Watts inclined at five different angles including the optimal angle obtained from the PVGIS database. South-South Nigeria consists of six states (Rivers, Delta, Bayelsa, Edo, Cross-Rivers and Akwa-Ibom). South-East Nigeria consists of five states (Abia, Enugu, Imo, Ebonyi and Anambra). The optimal installation angle of selected cities within each of the States was identified. Results from experiments conducted showed that from January to April and September to December, the yearly optimal tilt angle (11o for Port Harcourt, 12o for Yenagoa, 11o for Benin City, 10o for Calabar, 11o for Asaba and 10o for Uyo) is suitable for use in the South – South while from January to April and September to December, the yearly optimal tilt angle (10o for Umuahia, 11o for Awka, 12o for Enugu, 10o for Owerri, and 11o for Abakaliki) is suitable for use in the South – East. The horizontal plane (0o ) is suitable from May to August in both South – South and South – Eastern States. The untapped solar potential in the South – South was observed to be as high as 286.62kWh/m2 for Port Harcourt and a low value of 19.34kWh/m2 was recorded in Calabar when solar panels are not installed at the optimal angles. For the South –East, the untapped solar potential was observed to be as high as 222.11kWh/m2 for Umuahia and a lowest value of 21.31kWh/m2 was also recorded in Umuahia when solar panels were not installed at the optimal angles. Installing PV panels at the optimal tilt angle can improve the efficiency of solar energy generation, making it more cost-effective by maximizing the total amount of diffused and direct radiation.

Smart Footstep Energy Harvesting System

This research paper introduces a novel approach to footstep energy harvesting by integrating piezoelectric sensors into flooring surfaces. The project aims to generate sustainable electrical power from footsteps, contributing to the development of energy-efficient systems. The proposed system incorporates a unique feature that enables users to easily identify and address sensor failures or damages. The core components of the system include piezoelectric sensors strategically placed in high-traffic areas, an Arduino microcontroller for signal processing and energy management, and a rechargeable battery for energy storage. A rectifier circuit is employed to convert AC signals generated by the sensors to DC, while a voltage regulator ensures a stable output voltage. The energy harvested is stored in the battery, which is connected to a USB charging module capable of charging electronic devices. To enhance the system's usability and maintenance, LEDs are integrated with each piezoelectric sensor. These LEDs act as indicators for sensor status, allowing users to visually identify any damaged or failed sensors. The Arduino code is designed to continuously monitor sensor values and activate the corresponding LED when a sensor falls below a predefined threshold, signaling a potential issue. This unique feature enhances user awareness, simplifies troubleshooting, and aids in the efficient maintenance of the energy harvesting infrastructure. The research focuses on the practical implementation of the system, exploring its effectiveness in real-world scenarios. Various aspects, such as energy generation efficiency, adaptability to different environments, and the ease of sensor failure identification, are evaluated through extensive experimentation. The paper discusses the system's potential applications, including sustainable power generation for small electronic devices and its integration into smart infrastructure. The proposed Smart Footstep Energy Harvesting System with Sensor Failure Identification addresses a crucial aspect of sustainability and smart infrastructure management. The findings from this research contribute to the growing field of energy harvesting technologies and pave the way for future developments in efficient, user-friendly, and maintainable footstep energy harvesting systems.

Performance of Photovoltaic Thermal and Biomass Gasification for Efficient Renewable Energy Generation: A Review

AbstractThis review presents the technical and operational performances of solar and biomass energy technologies viz photovoltaic thermal (PVT) and biomass gasification systems. This work aims to offer a reference and guidelines to the renewable energy‐related players, especially for those at the operational level and investors. This paper highlights the technical advantage of hybrid PVT with phase change material in terms of electrical and thermal efficiencies. While the operational performance of biomass gasification is thoroughly discussed via sensitivity analysis, the potential integration between solar and biomass gasification technologies is explored to complement and bolster the capabilities of both renewable systems within the power energy mix portfolio. Finally, few directions and significant takeaways considering the technical criteria are addressed for identifying efficient renewable energy generation.

Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency

AbstractRenewable energy sources and hydroelectric power generation in large parts of the electricity market are crucial as environmental pollution worsens . Utilizing meteorological data from the region, where the Hirfanlı Dam is located, this study employs machine learning (ML) and introduces a novel hybrid Genetic Grey Wolf Optimizer (GGW0)-based Convolutional Neural Network/Recurrent Neural Network (CNN/RNN) regression technique to predict hydroelectric power production (PP). In the first section of the study, various ML techniques SVR (Support Vector Regression), ELM (Extreme Learning Machine), RFR (Random Forest Regression), ANN (Artificial Neural Networks) and WKNNR (Weighted K-Nearest Neighbor) are presented with the Principal Component Analysis (PCA) method and the minimum–maximum method in the normalization of the features. A novel GGWO and CNN/RNN model)-Long Short-Term Memory (LSTM) regression technique is introduced in the second section. GGWO is used to select features, while the proposed CNN/RNN-LSTM model is employed for feature extraction and prediction of PP. The study demonstrates that the ELM algorithm in Method I outperforms other ML models, achieving a correlation coefficient (r) of 0.977 and the mean absolute error (MAE) of 0.4 with the best feature subset. Additionally, the proposed CNN/RNN hybrid model in Method II yields even better results, with r and MAE values of 0.9802 and 0.314, respectively. The research contributes to the field of renewable energy prediction, and the results can aid in efficient decision making for electricity generation and resource management.

Optimizing Solar Panel Systems using Machine Learning and Ant Colony Optimization

In this study, the optimization of solar panel systems is investigated using machine learning algorithms and Ant Colony optimization. The performance of ML models, such as Artificial Neural Network, Support Vector Machine, Decision Tree, and Random Forest, is assessed relying on precision, recall, F1 score, and accuracy. Furthermore, the ANN model is combined with ACO for additional optimization. The results of the experimentation indicate that the ANN model demonstrates the best performance with the following scores: 97.86% precision, 96.50% recall, 97.20% F1 score, and 98.00% accuracy. In addition, when ACO is applied, the ANN model focuses its capabilities to predict the pressure hitting on the panel as follows: 98.50% precision, 97.80% recall, 98.15% F1 score, and 98.80%. The given outcomes also reflect the application of confusion matrices to show the classification of each model, and the results indicate that the most effective way to predict the parameters of the solar panel and optimize the system is presented by the combination of ANN and ACO. In such a way, the importance of using machine learning approaches and optimization techniques to optimize the energy generation efficiency in renewable energy systems is indicated. In the future, extensive research should be performed to assess experiments on large-scale solar panel systems using the proposed approaches and algorithms to scale-up sustainability efforts and minimize the environmental impact.