Solar Energy Applications Research Articles

Prediction of Frequency-Dependent Optical Spectrum for Solid Materials: A Multioutput and Multifidelity Machine Learning Approach.

The frequency-dependent optical spectrum is pivotal for a broad range of applications from material characterization to optoelectronics and energy harvesting. Data-driven surrogate models, trained on density functional theory (DFT) data, have effectively alleviated the scalability limitations of DFT while preserving its chemical accuracy, expediting material discovery. However, prevailing machine learning (ML) efforts often focus on scalar properties such as the band gap, overlooking the complexities of optical spectra. In this work, we employ deep graph neural networks (GNNs) to predict the frequency-dependent complex-valued dielectric function across the infrared, visible, and ultraviolet spectra directly from the crystal structures. We explore multiple architectures for the spectral multioutput representation of the dielectric function and utilize various multifidelity learning strategies, such as transfer learning and fidelity embedding, to address the challenges associated with the scarcity of high-fidelity DFT data. Additionally, we model key solar cell absorption efficiency metrics, demonstrating that learning these parameters is enhanced when integrated through a learning bias within the learning of the frequency-dependent absorption coefficient. This study demonstrates that leveraging multioutput and multifidelity ML techniques enables accurate predictions of optical spectra from crystal structures, providing a versatile tool for rapidly screening materials for optoelectronics, optical sensing, and solar energy applications across an extensive frequency spectrum.

Unveiling the unique potential of MXene with and without graphene nanoplatelet for thermal energy storage applications.

Solar thermal energy storage (TES) is an outstanding innovation that can help solar technology remain relevant during nighttime and cloudy days. TES using phase change material (PCM) is an avant-garde solution for a clean and renewable energy transition. The present study unveils the unique potential of MXene as a performance enhancer in lauric acid (LA), which functions as a base PCM. The addition of graphene nanoplatelet (GNP) into the LA-MXene composite is prepared to comprehend and evaluate the benefits and detriments of adding carbon-based nanomaterial into the PCM via a two-step homogenizing method. A similar weight percentage of MXene and GNP at 0.75 was used for composite synthesis. The study found that the enthalpy of LA-MXene is comparable to LA at 169.87 J/kg and greater than LA-MXene/GNP, which has 137.53 J/kg. Regarding thermal storage performance, LA-MXene exhibited outstanding performance compared to LA-MXene/GNP in terms of enthalpy efficiency (λ) and relative enthalpy efficiency (η), achieving 95.4% and 96.1%, respectively. This is supported by the XPS spectra, which show that the crosslinking structure acted as a barrier, reinforcing the material and preventing further thermal degradation. This has resulted in robust and denser shells that significantly improved light absorption, enhancing both the photothermal conversion and thermal energy storage efficiency of LA/MXene. The present study reveals that LA-MXene is a promising and optimal candidate for the feasibility and reliability of TES in solar renewable energy applications. It was observed that the incorporation of exclusive MXene may effectively address the limitations of LA as a conventional PCM and surpass the traditional role of GNP. This study offers valuable insights into the superior performance of MXene alone, eliminating the need for doping with various nanomaterials and thereby reducing the complexity in synthesizing the PCM.

Machine learning versus empirical models to predict daily global solar irradiation in an average year: Homogeneous parallel ensembles prevailed

Abstract Accurate predictive daily global horizontal irradiation models are essential for diverse solar energy applications. Their long-term performances can be assessed using average years. This study scrutinized 70 machine learning and 44 empirical models using two disjoint five-year average daily training and validation datasets, each comprising 365 records and 10 features. The features included day number, minimum and maximum air temperature, air temperature amplitude, theoretical and observed sunshine hours, theoretical extraterrestrial horizontal irradiation, relative sunshine, cloud cover and relative humidity. Fourteen machine learning algorithms, namely, multiple linear regression, ridge regression, lasso regression, elastic net regression, Huber regression, k-nearest neighbors, decision tree, support vector machine, multilayer perceptron, extreme learning machine, generalized regression neural network, extreme gradient boosting, gradient boosting machine and light gradient boosting machine were trained, validated and instantiated as base learners in 4 strategically designed homogeneous parallel ensembles—variants of pasting, random subspace, bagging and random patches—which also were scrutinized, producing 70 models. Specific hyperparameters from the algorithms were optimized. Validation showed that at least two ensembles outperformed its individual model. Huber-subspace ranked first with a root mean square error of 1.495 MJ/m2/day. The multilayer perceptron was most robust to the random perturbations of the ensembles which extrapolates to good tolerance to ground truth data noise. The best empirical model returned a validation root mean square error of 1.595 MJ/m2/day but was outperformed by 93% of the machine learning models with the homogeneous parallel ensembles producing superior predictive accuracies.

Efficient solar utilization: Multifunctional solar absorber devices realize self-driven hydrogen production

Considered as one of the effective approaches to address the energy crisis and develop green and sustainable energy, the application of solar energy in multiple stages was investigated in this study. By designing a WS2/ZnIn2S4 heterojunction, a multifunctional coupling system based on interfacial water evaporation technology was constructed. In this system, the water evaporation rate was 1.67 kgꞏm−2ꞏh−1, and the photocatalytic degradation efficiency of rhodamine B reached 96.5 %. Moreover, the electric energy output from thermoelectric conversion was innovatively in situ applied for photocatalytic hydrogen production, which increased the photocatalytic hydrogen production efficiency by five times, with a hydrogen production rate of 40.3 μmolꞏcm−2ꞏh−1. This study successfully integrated thermoelectric power generation, photocatalytic hydrogen production, and photocatalytic degradation of dye wastewater into an advanced solar-driven interface evaporation system, enabling the simultaneous conversion of solar energy into multiple forms of energy, improving solar energy utilization efficiency, which was of great significance for promoting the practical application of solar energy.

Experimental Study of a Prototype Solar Water Heater Used in Sahelian Homes

Environmentally-friendly, low-cost energy supplies are the backbone of sustainable development. Development must not only meet current needs, but must also look to the future and consider environmental issues as a challenge. Solar power as an environmentally-friendly energy source is a promising way of reducing greenhouse gas emissions. One of the most important applications of solar energy is the production of hot water using solar water heaters. These solar water heaters, which can provide between 100 and 200 l/d of hot water over a temperature range of 40-70°C., are of the separate-element and integrated-storage types. In the Saharan environment, hot water requirements are concentrated in winter. It is therefore necessary to develop efficient systems adapted to this reality. It is then necessary to develop effective systems adapted to this reality. There is a need to propose a new design to boost the solar irradiation absorbed by the collector-storage while reducing nighttime thermal losses. For greater user-friendliness and with the aim of adapting them to the Saharan environment, it is also necessary to offer solar water heaters integrated into the building and operating mainly in the winter period. The present work represents an experimental study of a solar water heater with separate elements and a simple design, in a Burkinabe climate. This is a separate element solar water heater with a capacity of 200 liters with an aluminum collector as absorber designed at the Research Institute of Applied Sciences and Technologies (IRSAT). The water in the tank forms a loop with the sensor. In the tank, donut a copper coil of a length equal to 15m with a diameter of 16mm. They use the principle of thermosyphon (cold water pressure) to circulate and store heat and are simpler and less expensive, but can only be installed in sunny countries and have limited efficiency. During the measurement period from 11:25 a.m. to 4:05 p.m., the maximum temperature of the hot water was around 57°C at 11:45 a.m., and that of the cold water was around 27°C at 12:15 p.m.. The experimental results showed acceptable thermal performance despite the simplicity of the sensor. Finally, an improvement can easily be made whether by perfecting the thermal insulation or using selective collection surfaces.

Improving photovoltaic cell parameter calculations through a puffer fish inspired optimization technique

The precise estimation of solar PV cell parameters has become increasingly important as solar energy deployment expands. Due to the intricate and nonlinear characteristics of solar PV cells, meta-heuristic algorithms show greater promise than traditional ones for parameter estimation. This study utilizes the Puffer Fish (PF) meta-heuristic optimization method, inspired by male puffer fish's circular structures, to estimate parameters of a modified four-diode PV cell. The PF algorithm's performance is assessed against ten benchmark test functions, with results presented as mean and standard deviation for validation. Comparative analysis with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Rat Search Algorithm (RAT), Heap Based Optimizer (HBO), and Cuckoo Search (CS) algorithms highlights PF's superior performance, achieving optimal solutions with minimal error of 7.8947E-08. Statistical tests, including Friedman Ranking (1st) and Wilcoxon's rank sum (3.8108E-07), confirm PF's superiority. The circular structures of male puffer fish serve as an effective model for optimization algorithms, enhancing parameter estimation. Benchmark tests and statistical analysis consistently underscore PF's superiority over other meta-heuristic algorithms. Future research should explore PF's potential applications in solar energy and beyond.

A comprehensive investigation of the synthesis, spectral, DFT, and optical properties of a novel oxadiazolyl-pyrano[3,2-c]quinoline for photosensor applications

The condensation reaction of pyranoquinoline-3-carbaoxldehyde 1 with benzohydraide afforded hydrazone 2 which upon oxidative cyclization yielded 6-ethyl-4‑hydroxy-3-(5-phenyl-1,3,4-oxadiazol-2-yl)-2H-pyrano[3,2-c]quinoline-2,5(6H)‑dione (EHPOPQ, 3). The DFT and B3LYP/6–311++G(d,p) basis set were used to compute the optimized molecular geometry. The global reactivity descriptors were examined and showed that hydrazone 2 exhibited high softness while aldehyde 1 had more electronegativity and high global electrophilicity. Subsequently, the reactive sites of the present compounds were located by tracing the MEP map. The GIAO method was used to calculate the 1H and 13C NMR. The current comprehensive investigation of the novel (EHPOPQ) reveals its significant potential for applications in nonlinear optics (NLO) and photovoltaics. Through high-level computational methods, EHPOPQ has an exceptionally high first hyperpolarizability (βtot) of 950 × 10−30 esu, which is substantially higher than that of the conventional NLO material urea. This high βtot value suggests that EHPOPQ could dramatically enhance the efficiency and performance of NLO devices such as frequency converters and optical modulators. In parallel, the detailed spectroscopic analysis of EHPOPQ's optical properties provided critical insights for photovoltaic applications. The optical band gap was measured to be 3.07 eV, indicating a direct band gap transition suitable for efficient light harvesting in solar cells. Additionally, dispersion parameters were carefully analyzed, which are essential for understanding the interaction of light with the material. The experimental characterization of EHPOPQ-based photovoltaic devices showed promising results. Current-voltage (J-V) measurements under varying illumination intensities revealed significant enhancements in the device's short-circuit current (from 1.2 µA to 4.3 µA) and open-circuit voltage (from 0.25 V to 0.47 V) as light intensity increased. These findings demonstrate the material's heightened photoresponsivity and suggest its potential as an efficient light conversion material for solar energy applications. These specific findings underline the importance of EHPOPQ in advancing both NLO and photovoltaic technologies, offering a pathway to more efficient and effective devices.

Novel phase-change material based on stearic-acid@MOFs@SiO2 with perfect impermeability promoted by coordination with unsaturated metal sites

In this study, to achieve excellent impermeability and thermal storage performance of metal-organic framework (MOF) based composite phase change materials (PCM), an encapsulation method was prepared by utilizing unsaturated metal sites of MOF, and the PCM composite SA@MOFs@SiO2 were prepared. Stearic acid is adsorbed into the MOF pores through adsorption before encapsulation. To achieve high impermeability, the unsaturated metal site of MOFs forms a coordination Me-SH bond with 3-mercaptopropyltriethoxysilane (KH590), then dense silica was formed on PCM surface through the hydrolysis condensation reaction of both KH590 and triethoxysilane (TEOS). The heat storage of SA@MIL-101(Cr)-NH2@(Cr)-S-Si@SiO2 composite can reach 113.89 J/g, and the melting permeability was as low as 0.69 %, exceptional chemical stability can be obtained at 70 °C for 10 days. This work provides a general method for designing a variety of MOF-based phase-change composites with superior impermeability, demonstrated by three different MOFs derived PCMs. These will be promising materials in the fields of textiles, architecture, solar energy applications, and etc.

Photoluminescence Measurement of Triplet Sensitizer-Emitter Solution Using a Customized 3D-Printed Sample Holder

This study explores the photoluminescence (PL) measurement of triplet sensitizer-emitter (TSE) solutions using a custom 3D-printed sample holder, within the context of triplet-triplet annihilation based molecular photon upconversion (TTA-UC) systems targeting the Vis-to-UV spectral region. TTA-UC converts low-energy visible photons to higher-energy ultraviolet (UV) photons, holding promise for solar energy harvesting and photonics applications. Two TSE couples, 4CzIPN/TP and 4CzIPN/QP, were investigated, and their upconverted fluorescence spectra showed peaks at 344 nm and 354 nm / 370 nm, respectively, confirming efficient upconversion capabilities. The 3D-printed sample holder facilitated reproducible PL measurements, enabling the calculation of quantum yields (ΦUC). The 4CzIPN/TP and 4CzIPN/QP couples exhibited low quantum yields (0.028% and 0.043%, respectively), suggesting the need for improved deoxygenation methods to enhance the triplet-triplet annihilation process and overall quantum efficiency. Despite modest yields, successful UV upconverted fluorescence observation underscores the feasibility of the Vis-to-UV TTA-UC system. This study provides insights into TTA-UC optimization and demonstrates the utility of the 3D-printed sample holder for affordable and precise PL measurements, paving the way for future advancements in photonics and solar energy applications.

Experimental investigation of two-stage NH3–H2O resorption heat storage system with solution concentration difference

Widespread application of solar energy is severely restricted to its instability in time and space. Heat storage system is considered to be one of the most effective pathways to achieve long-term heat supply. Among all the heat storage technologies, solution concentration difference storage technologies stand out for low heat loss and high energy density. By substituting condenser and evaporator in conventional absorption cycle with a high-pressure absorber and low-pressure generator, a resorption cycle could be established. Compared with absorption heat storage cycle, working pressure of resorption heat cycle is lower, making it possible to utilize low-pressure heat source. Two-stage heat source cycle is further proposed to lower working pressure and upgrading supply water temperature. Resorption energy storage cycle with solution concentration difference is established and studied. A prototype based on two-stage ammonia-water resorption heat storage cycle was set up and tested. The result shows that the constraint relation between ambient temperature and heat source temperature of the proposed system. In single-stage mode, the lowest working ambient temperature is 5 °C when heat source temperature is 90 °C. The lowest working ambient temperature could be declined to 0 °C when heat source temperature rises to 120 °C. In two-stage mode, the lowest working ambient temperature is 0 °C when heat source temperature is only 110 °C, which proves the adaptability of two-stage cycle. The highest ESCOP appears in the 15 °C–120 °C single-stage mode, which is 0.742. The highest COP is 1.324 under the same condition. The highest energy density occurs in two-stage mode, which is 467.07 kJ/kg when ambient and heat source is set as 15 °C, 120 °C. The advantage that two-stage mode could enlarge solution concentration difference is verified.

Investigation of graphene based disk-square integration resonator for enhanced solar absorption using machine learning for solar heaters

As worries about climate change and energy security grow, the need for clean and sustainable energy sources becomes increasingly critical. Among the possible alternatives, solar thermal technology provides a dependable and adaptable method for harnessing the sun's energy as heat. At the core of this technique is the solar thermal absorber, a critical component that converts sunlight into useful thermal energy. The Graphene Based Disk-Square Integration Resonator Solar Absorber (GBDSIRSA) structure is examined in this work between 200 and 2500 nm in wavelength. The GBDSIRSA has remarkable performance throughout a wide variety of spectral ranges, underscoring its adaptability and effectiveness. GBDSIRSA has exceptional efficiency in absorbing light over the full spectrum, from UV to MIR wavelengths, with an average absorptance of 97.73%. The absorptance rates are particularly high, achieving 94.02% in the UV, 96.36% in the VIS, 97.12% in the NIR, and an astounding 98.94% in the mid-infrared (MIR) region. The GBDSIRSA is independent of polarization effect and also an incident angle independent up to 80 degrees. The machine learning (Local Regression) approach used with test size of 0.2 and 1.8780 ×10-5 mean squared error for absorptance prediction has good prediction accuracy R2 of more than 95% which is applied to simulation resource reduction. Because of its wide absorptance spectrum, polarization and incident angle independence, the GBDSIRSA is a very promising technology for a variety of applications, including thermal imaging and renewable energy. It can efficiently harvest solar energy. All things considered, GBDSIRSA's remarkable absorptance properties highlight its promise as a top choice for cutting-edge optical and solar energy applications.

Structural evolution of anodized TiO2 nanotubes and their solar energy applications

Structural evolution of anodized TiO2 nanotubes and their solar energy applications

Analyzing the thermal performance of transient heat transfer mechanism due to the combined effects of solar energy and variable surface temperature: Growing applications of solar energy in co-axial pipes

The current analysis is carried out to study the laminar convective heat transfer characteristics that change with time in the co-axial pipes along the impact of heat radiation and variable surface temperature. The flow is assumed along the axial direction of the pipe, and variable boundary condition is assumed at the surface of the pipe due to the variable surface temperature. A two-dimensional mathematical model made up of non-linear partial differential equations is solved using the implicit finite difference method. The project involves predicting the thermal efficiency of a pipe's time dependent flow across a number of flow model-relevant parameter ranges. Graphical representations highlighted the derived predictions. After separating the numerical solutions into the time independent and the time dependent components, the time dependent energy and surface shearness were found using the data from the time independent component. Comprehensive detail of the obtained results for the non-dimensional parameters included in the flow formulation is predicted for steady state velocity, temperature distribution, time dependent surface sheerness, and time dependent energy sheerness, which is given in results and discussion section of the manuscript. Major focus is given on the influence of the radiation parameter on the above-mentioned primary measures. Furthermore, it is concluded that in all cases, the steady state flow is as R→∞ and velocity profile as U→1 and θ→0, which ensured the accuracy of the obtained results by satisfying the boundary conditions. For various values of the fluid's absorption parameter D = 1.0, 3.0, 5.0, and 10.0, the flow profile is increased and U→1 as R→∞. Simultaneously, thermal distribution also increases and θ→0 as R→∞.

Artificial intelligence applications in solar energy

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

Validating ANSYS Heat Transfer Models Using Experimental Data Analysis of Two Phase Change Materials with Differing Melting Temperatures

Phase change materials are becoming more and more popular as viable options for improving the thermal performance and energy efficiency of a variety of applications, especially in innovative building envelope designs. In this paper, experimental data from two different phase change materials with differing melting temperatures (21°C and 28°C) are used to validate numerical system heat transfer models. The experimental setup included a heat flux apparatus, which was utilized to ascertain average temperature and heat flow changes over time during both heating and cooling phases for both phase change materials. The data obtained from the experiments were utilized to generate ANSYS Fluent simulation models replicating the experimental setup. The parameters and boundary conditions for the models can be assigned in several ways within ANSYS Fluent software. Consequently, two simulation models were created: one integrated the thermal and physical properties of the experimental setup's system components, while the other utilized the measured heat-flux values over time from the experiments as an input source for calculating average temperatures within the phase change material. The average temperature data from both simulation and experimental results were compared to validate both ANSYS models. By aligning the simulated results with the experimental data, the accuracy and reliability of the numerical models have been established in predicting the thermal behaviour of the two phase change materials. The two numerical system heat transfer models developed in this study serve as valuable tools for conducting further analysis and optimization of systems based on phase change materials. This research highlights the significance of phase change materials in enhancing the thermal performance of building envelopes, particularly in solar energy applications.

A proposed hybrid model of ANN and KNN for solar cell defects detection and temperature prediction using fuzzy image segmentation

This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.

Characterization and Calibration of a Photodiode Based Pyranometer for Solar Energy Applications

<p>In the domain of solar science, a pyranometer assumes a pivotal position, functioning as an indispensable tool for appraising solar irradiance across the abbreviated wavelengths of the solar spectrum, spanning from 300 to 3000 nanometers. Conventional pyranometers typically employ intricate and labour intensive thermopiles. These devices rely on arrays of thermocouples interconnected either serially or in parallel and necessitate rare earth minerals for efficient operation. In this investigation, we advocate for an innovative methodology employing photodetectors, specifically photodiodes and phototransistors, to surmount the limitations linked with traditional pyranometers. Photodiodes, commonly utilized for this purpose, proffer numerous benefits, including accelerated response times, customizable spectral range selection, and cost-effectiveness. Nonetheless, they cannot entirely supplant thermopiles owing to impediments such as the generation of dark current in the absence of illumination, temperature-dependent functionalities, and saturation at elevated irradiance levels. To counteract these constraints, we have devised a pioneering design of a photodiode-based pyranometer that incorporates a data acquisition system and correction mechanism. This innovative approach furnishes a feasible resolution for autonomous solar radiation measurement, furnishing enhanced precision and dependability.</p>

Model development and preliminary study of the concentrated-radiative cooling based thermoelectric generator

Photovoltaic (PV) cells, widely adopted in solar energy applications, achieve a maximum efficiency of only 30 % and remain inactive during nighttime. In contrast, solar-thermal power generation has surpassed 50 % efficiency. To address these limitations, we introduce the Solar- Radiative cooling -Thermoelectric system (Solar-RC-TE), integrating solar energy, radiative cooling (RC), and thermoelectric power generation. This system enhances energy density, reduces consumption, lowers costs, and minimizes environmental impact while enabling nighttime power generation. For the optimization of the Solar-RC-TE model to further improve power generation efficiency, we employ the comprehensive three-dimensional methodology of COMSOL, considering material properties, structural configurations, and environmental factors. Research findings reveal that a 710 mm × 710 mm radiative cooling film can generate 386.12 mV of voltage on the thermoelectric generator (TEG). Environmental factors, such as increased solar irradiance, lead to temperature elevation, affecting efficiency. Overheating and higher wind speeds also contribute to decreased TEG efficiency. Regarding materials, improving the thermoelectric figure of merit (ZT) and film emissivity positively impact power generation efficiency. Elevating the film emissivity from 0.7 to 0.89 significantly enhances performance by 1.2 %. These outcomes provide a tangible and practical solution for addressing energy crises and environmental concerns.

The Lead-free Double Perovskites K2LiTlX6 (X = Cl, Br, I) as an Emerging Aspirant for Solar Cells and Thermoelectric Energy Applications

The Lead-free Double Perovskites K2LiTlX6 (X = Cl, Br, I) as an Emerging Aspirant for Solar Cells and Thermoelectric Energy Applications

Solar Energy's Role in Achieving Sustainable Development Goals in Agriculture

The adoption of solar energy in agriculture has the potential to significantly contribute to achieving multiple Sustainable Development Goals (SDGs), particularly those related to clean energy access (SDG 7), sustainable economic growth (SDG 8), responsible consumption and production (SDG 12), and climate action (SDG 13). This review article examines the various applications of solar energy in agricultural practices, including irrigation, crop drying, greenhouse heating, and powering farm machinery. It analyzes the economic, environmental, and social benefits of transitioning to solar-powered agriculture, such as reduced reliance on fossil fuels, lower greenhouse gas emissions, improved energy security, and increased income for farmers. The article also discusses the challenges and barriers to widespread adoption, including high upfront costs, lack of awareness and technical expertise among farmers, and inadequate policy support. Through a comprehensive review of existing literature and case studies, this article highlights the immense potential of solar energy in transforming the agricultural sector and contributing to sustainable development. It concludes by emphasizing the need for concerted efforts from governments, international organizations, and the private sector to promote and facilitate the integration of solar energy in agriculture, particularly in developing countries where access to clean energy and sustainable farming practices are most crucial for achieving the SDGs.