Plant Location Research Articles

Application of GIS in Introducing Community-Based Biogas Plants from Dairy Farm Waste: Potential of Renewable Energy for Rural Areas in Bangladesh

Dairy production is one of the most important economic sectors in Bangladesh. However, the traditional management of dairy cow manure and other wastes results in air pollution, eutrophication of surface water, and soil contamination, highlighting the urgent need for more sustainable waste management solutions. To address the environmental problems of dairy waste management, this research explored the potential of community-based biogas production from dairy cow manure in Bangladesh. This study proposed introducing community-based biogas plants using a geographic information system (GIS). The study first applied a restriction analysis to identify sensitive areas, followed by a suitability analysis to determine feasible locations for biogas plants, considering geographical, social, economic, and environmental factors. The final suitable areas were identified by combining the restriction and suitability maps. The spatial distribution of dairy farms was analyzed through a cluster analysis, identifying significant clusters for potential biogas production. A baseline and proposed scenario were designed for five clusters based on the input and output capacities of the biogas plants, estimating the location and capacity for each cluster. The study also calculated electricity generation from the proposed scenario and the net greenhouse gas (GHG) emissions reduction potential of the biogas plants. The findings provide a land-use framework for implementing biogas plants that considers environmental and socio-economic criteria. Five biogas plants were found to be technically and spatially feasible for electricity generation. These plants can collectively produce 31 million m3 of biogas annually, generating approximately 200.60 GWh of energy with a total electricity capacity of 9.8 MW/year in Bangladesh. Implementing these biogas plants is expected to increase renewable energy production by at least 1.25%. Furthermore, the total GHG emission reduction potential is estimated at 104.26 Gg/year CO2eq through the annual treatment of 61.38 thousand tons of dairy manure.

Application of simultaneous evaluation of criteria and alternatives for prioritization of photovoltaic power plant location

AbstractThis research contributes to the ongoing efforts to transition towards clean and renewable energy sources by providing a practical framework for decision‐making in energy resource planning. Hence, a recently developed multi‐criteria decision making technique tool is proposed for simultaneous evaluation of criteria and alternatives (SECA) to find an optimal site for establishing photovoltaic power stations in Iran. The proposed SECA method provides a systematic and comprehensive approach to evaluating renewable energy resources, taking into account technical, economic, environmental, and social factors. The data required for this research has also been collected from the Global Solar Atlas as well as the Iran Meteoritical Organization. The results show that despite the main influence of the solar radiation factor, it should be noted that among all the climatic and environmental indicators, “cloudiness factor” by weight of 0.2339 and “annual temperature” by weight of 0.2320 have been selected as the most important criteria. It was also found that among the ten candidate cities, Zahedan (w = 0.8939), Shahrekord (w = 0.8341) and Kerman (w = 0.8112) have the highest potential for building a solar power plant. The results of the study highlight the potential for solar energy resources to meet Iran's electricity needs in a sustainable manner.

Enhancing Regional Wind Power Forecasting through Advanced Machine-Learning and Feature-Selection Techniques

In this study, an in-depth analysis is presented on forecasting aggregated wind power production at the regional level, using advanced Machine-Learning (ML) techniques and feature-selection methods. The main problem consists of selecting the wind speed measuring points within a large region, as the wind plant locations are assumed to be unknown. For this purpose, the main cities (province capitals) are considered as possible features and four feature-selection methods are explored: Pearson correlation, Spearman correlation, mutual information, and Chi-squared test with Fisher score. The results demonstrate that proper feature selection significantly improves prediction performance, particularly when dealing with high-dimensional data and regional forecasting challenges. Additionally, the performance of five prominent machine-learning models is analyzed: Long Short-Term Memory (LSTM) networks, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Extreme-Learning Machines (ELMs). Through rigorous testing, LSTM is identified as the most effective model for the case study in northern Italy. This study offers valuable insights into optimizing wind power forecasting models and underscores the importance of feature selection in achieving reliable and accurate predictions.

Oceanographic‐informed coral out‐planting density for coral reef restoration activities

Out‐planting corals onto coral reefs as a mechanism to increase coral cover on degraded reef systems is an increasing global activity. Current practices typically focus on asexual out‐planting of coral fragments (coral gardening). Newer sexual reproductive approaches are being developed to out‐plant younger corals produced in aquaculture facilities or sea‐based rearing pools to enable assisted evolution or assisted gene flow conservation genetics approaches. Designing out‐planting deployment operations requires decisions to be made on planting density and location in order to maximize future recruitment on both the out‐planted reefs, and on nearby reefs that are connected through oceanographic transport processes. The thinking presented here seeks to unpack how out‐planting density may be influenced, especially by local oceanographic conditions, in order to guide practitioners undertaking restoration projects.

Wind Power Plant Location Selection with Fuzzy Logic and Multi-Criteria Decision-Making Methods

This study aims to examine the effectiveness of the fuzzy multi-criteria decision-making methods, especially Fuzzy Weight by Envelope and Slope (F-WENSLO) and Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) for evaluating the potential locations for wind power plants. The study's objective is to provide a solid framework for deciding which locations are most suitable for wind energy projects, taking into account various criteria and expert opinion. The study includes the evaluation of five potential places including Gürün, Kangal, Divriği, Ulaş and Zara in Sivas province of Turkey. The evaluation criteria include wind speed and direction, altitude, land use, environmental impacts, infrastructure proximity, social acceptance, economic costs, security and risk factors, climatic conditions and legal and permit requirements. Scores from experts from various fields and weights of criteria were determined. The analysis revealed that Ulaş and Kangal got the highest point for the wind power plant installation. As Ulaş gets the highest point due to favorable wind conditions, favorable altitude and advantageous land use; Kangal stood out as a strong candidate because of its acceptable wind speed, positive social acceptance and low economic costs. The study highlights the importance of integrating multiple criteria and expert assessments in the decision-making process. The findings suggest that fuzzy multi-criteria decision-making methods can be effectively supportive of wind power plant site selection. The study provides valuable information for project managers and policy makers, emphasizing the importance of criteria such as security in the choice of location, legal requirements and social acceptance. Future research may expand these findings to examine the integration of additional criteria, alternative locations, and other multi-criteria decision-making techniques.

Application of multi-criteria decision making (MCDM) model for solar plant location selection

This study aims to systematically evaluate and identify the most suitable locations for establishing solar farms in the United Kingdom. It employs a comprehensive Multi-Criteria Decision-Making (MCDM) model that incorporates the Fuzzy Analytic Hierarchy Process (FAHP) method. Eight potential locations have been identified and selected for their climatic conditions and other pertinent parameters. These potential locations underwent additional assessment considering several factors including temperature, annual hours of sunshine, absolute humidity, proximity to major roads, distance from the power network, and potential demand. The data was normalized to eliminate scale effects and improve comparability. A MATLAB program has been developed with universality and user-friendliness in mind. Its purpose is to establish location selection factors graphs according to application-specific criteria and generate the matrix representation of these graphs for each potential candidate location. The permanent matrix, a composite metric synthesizing six criteria, identifies Torquay as the optimal location with a permanent value of 18.16. This quantitative approach, intersected with qualitative assessments, presents a clear hierarchy of site suitability. The model's simplicity, scalability, and proven accuracy make it a valuable tool for assessing renewable energy project viability in diverse regions.

From biogas to biomethane: Comparison of sustainable scenarios for upgrading plant location based on greenhouse gas emissions and cost assessments

Addressing the global energy crisis and achieving sustainable development goals (SDGs) necessitates shifting towards renewable energy sources, with biomethane emerging as a viable alternative. This study evaluated three scenarios for biomethane production from biogas: localized upgrading for biofuel use, district upgrading for biofuel use, and district upgrading with grid injection. By utilizing economic assessments and spatial and network analysis, the optimal locations for upgrading plants were identified to minimize transportation costs and GHG emissions (CO2 equivalents). Results indicate that Scenario 1 achieves the lowest emissions and operating costs (41 MEUR), while Scenarios 2 and 3, despite higher initial investments (152–173 MEUR), offer significant economies of scale (1187–1321 MEUR) and substantial annual production capacities (8.8 × 106 Sm³ of biomethane). Comparative and sensitivity analyses show renewable energy sources reduce GHG emissions by up to 83.7% and significantly lower operating costs. This research underscores the critical role of strategic planning and renewable energy integration in optimizing the biomethane value chain for environmental sustainability and economic viability while also emphasizing the need for further research to refine these findings.

Plant Density and Location: Optimization of Growth and Quality of Cut Sunflower in Tropical and Subtropical Environments

The cultivation of sunflower (Helianthus annuus L.) as a cut flower stands out in floriculture due to its aesthetic beauty and commercial value. Understanding how cut sunflower genotypes adapt to different edaphoclimatic regions and management practices is essential to optimize flower quality and productivity. This study aimed to evaluate the effect of plant density and location on the development, growth, and quality of cut sunflower in tropical and subtropical environments. Plant densities of 10, 20, 30, 40, and 50 plants/m2 were evaluated in tropical climate and subtropical climate using a randomized block design in a factorial scheme. Results showed significant differences between locations for plant height, capitulum and stem diameter, final number of leaves, leaf area, leaf area index, phyllochron, and the developmental cycle. Plant density significantly influenced these variables except for plant height and developmental cycle. The interaction between location and plant density was significant only for capitulum diameter and final leaf number. The findings indicate that both planting density and location significantly influence the developmental cycle of cut sunflowers, with lower densities favoring more robust plants at harvest. A density of 30 plants/m2 is recommended for efficient space use without significantly compromising floral stem quality. All produced stems are marketable, suggesting that adjusting planting density can optimize production without compromising quality, adapting to specific regional conditions.

Influence of the Surrounding Plants by Rapeseed Field on Population Density of Cabbage Aphid (Brevicoryne brassicae L.) and its Biological Enemies

The result revealed that the peak of population density of cabbage aphid Brevicoryne brassicae was 523.20 individuals/plant on 21 March in edges of rapeseed field and was 1141.67 individuals/plant in center of the field. Results revealed that population density of cabbage aphid in rapeseed fields surrounded by cover crops significantly were low compared with that of monoculture rapeseed. The location of rapeseed plants (in edges or in center) significantly affected (p

Choosing the best locations to establish a nuclear power plant in Iraq with respect to radioactivity in those areas

Abstract Iraq is in a good position to run its nuclear power plant to prepare for long-term energy needs, including electricity demands. The ideal location for the nuclear power plant was identified in this study, as were the standards for choosing that location. Salah al-Din and portions of Anbar were the options. This research takes into account seven criteria: low population density, residential area, river access, topography, and land use. The criteria are represented in GIS layers, a GIS analysis is conducted to provide many proposed sites, and the criteria are categorized based on the weight distance value. To protect the lives of the station employees, the natural radioactivity of the research region was also monitored. The effectiveness of the site suitability map lies in its capacity to evaluate and display the suitability rates for nuclear power plants. The nearest quarter for distance of water sources of the total samples, which is No. (15, 11, 19, 20 and 13), the water sources are (Tigris River, Haditha Dam, Tigris River, Tigris River, and Tharthar Lake). As for the distance from the city centers, the furthest quarter of the samples were chosen from the residential areas, which are each of the samples with No. (14, 13, 10, 6 and 12) respectively. To the city centers (Tikrit City, Tikrit City, Tikrit City, Rawa City, and Kubaisa City) respectively, with distances (51256, 50385, 38308, 30934, and 25392).

Forecasting solar irradiance for the strategic integration of hybrid hydro and solar photovoltaic systems in rural Indian regions

ABSTRACT Conversion of the conventional electrical grid into a smart and sustainable grid involves several considerations. The primary factors, however, are renewable energy penetration, associated storage systems, and energy generation costs. This research endeavors to conduct a thorough survey and analysis of the solar irradiance on various hydropower locations in India, including Run-of-River (RoR), Run-of-River with Pondage (RoRP), Reservoir Storage (S), Multi-Purpose Storage (MP) and Pumped Storage Systems (PSS). The hydroelectric projects in rural Indian regions have been the subject of the proposed case study. As a preliminary study, the probabilistic variables like minimum, maximum, and mean solar irradiance are calculated for 252 High-Scale Hydropower Plant locations (HSHPs) using the past 40 years day ahead solar radiation data to identify the high-irradiance hydropower plant location in each state of India. This study concludes that the maximum mean solar irradiance location in each state as these sites are well suited for hybrid PV-hydro systems. The identified high-irradiance locations 40 years day ahead data sets are analyzed employing 8 machine learning models and 2 deep learning models. This analysis aims to forecast solar irradiance, serving as a crucial foundation for the initial phase of the implementation of hybrid PV-hydro.

Assessment of water quality in the Great Zab River (Erbil city, Iraq) exposed to wastewater from different industries using the WQI and GIS-based Python script approaches

ABSTRACT This study was conducted to assess the water quality of the Great Zab River (GZR) under potential pollutant pressure from some industries. The Water Quality Index (WQI) was used to evaluate the results of the water quality monitoring study. WQI and Geographical Information System (GIS)-based Python scripts were used to evaluate the results. Results indicate varying water qualities, ranging from moderate to poor, with fluctuations attributed to pollution from industrial plants mixing with river water. The WQI rating of the GZR before industrial discharges was 57.48, and in the lower part, after discharges, the WQI rating was 56.00, indicating marginal water quality. In contrast, the areas near the Cola Factory, where the river confluence occurs, have WQI values of 43.32 and 44.97, representing unacceptable, poor water quality. The study highlights that the refinery, Cola factory, and power plant locations significantly impact the river's water quality. These findings underscore the urgent need to adopt ecological practices, establish environmental regulations, and implement sustainable technologies to preserve the river's natural state. Concise measures aimed at eliminating pollutant sources and enhancing environmental awareness within communities are essential for the conservation of the GZR and the the protection of its surrounding ecosystems. surrounding ecosystems.

Analytic network process (ANP) based decision support tool for nuclear power plant location and reactor type selection

The nuclear energy industry has seen a resurgence due to interest in sustainable energy. Regulatory agencies are evaluating innovative technology and facility designs for the first time in a long time. The nuclear energy business needs a workable instrument for decision-making in light of the shift in public and regulatory opinion. This study has two main motivations. The first is to determine the set of criteria to be used in determining suitable locations for a nuclear power plant (NPP) to be established in Turkey and to determine suitable regions with Analytic Network Process (ANP) based analyzes in the light of these criteria. The second is to select the most suitable nuclear reactor type for the specified location. Plant efficiency and lifetime vary depending on the reactor type used. For this reason, in the study, both the location of the nuclear power plant and the selection of the appropriate reactor type for the plant were evaluated together. In this context, a criterion set consisting of 3 main criteria and 20 sub-criteria to be used in NPP installation location and reactor selection was determined. In the study, site selection was made by focusing on economic, social and technical factors, and reactor selection was made by focusing on technical factors. Giresun, Trabzon, Rize and Artvin provinces located in the Eastern Black Sea region were evaluated for NPP installation. For reactor selection, we focused on reactor types according to their coolants and in this context, PWR, PHWR, GCR and BWR reactors were examined. NPP location and reactor selection in the study; It was realized as Giresun (PWR) > Rize (PWR) > Trabzon (BWR) > Artvin (BWR).

Life cycle assessment of limestone calcined clay concrete: Potential for low-carbon 3D printing

Limestone calcined clay cement is being promoted throughout the construction industry as a way to considerably reduce the cement proportion in concrete mixtures. At the same time, concrete 3D printing could save resources by placing concrete material only where its functionalities are maximized. This study addresses the need for a quantification of the environmental impacts related to the material acquisition phase regarding a low-clinker 3D printing material.Compared to a 30 MPa 3D printing material from the literature, a 3D printable LC3-based concrete with low clinker content in the Quebec context displays a 36 % climate change score reduction with the same compressive strength (46 % reduction in the French context). A small impact shift is noticed in 6 out of 16 midpoint indicators (5 in the French case), mainly due to the calcined clay production. However, it is offset by the significant reduction in other indicators when considering an endpoint assessment. When considering different sourcing scenarios, a global warming potential variability of up to 15 % is observed.LC3 remains a viable solution for the mitigation of greenhouse gas emissions in the context of concrete 3D printing as it provides mechanical strength and enhanced structuration rate for low-clinker materials. However, a specific attention should be accorded to the calcined clay plant locations, especially when the calcined clay content is higher than the value used in this study. As a perspective, a mix design tool could allow the optimization of environmental impacts depending on expected fresh and hardened properties.

Renewable energy-powered water desalination and treatment network under wind power and water demand uncertainty: A possibilistic chance-constrained programming

Given the scarcity of freshwater resources, the growing significance of desalination is undeniable. It holds immense potential, particularly in regions grappling with severe water shortages. However, desalination's Achilles heel lies in its voracious energy appetite, requiring roughly ten times more energy than wastewater treatment. Moreover, the prevalent use of fossil fuels in desalination plants poses concerning issues like environmental pollution, fossil fuel depletion, and rising costs. The present study has designed an integrated Water desalination and treatment Network that includes a number of desalination facilities, storage centers, wind farms, and wastewater treatment facilities. The water desalination and treatment network has been structured using a Mixed-Integer Linear Programming (MILP) model, considering uncertainties in wind power and water demand. Employing a chance constraint probabilistic programming approach, this model ensures robustness and balances conservatism with investment attractiveness. It aims to enhance resilience against fluctuations in wind energy and water demand within the water and energy supply chain network. The study applied this model to optimize the locations of desalination plants, treatment centers, and storage facilities. This integrated model ensures autonomy, eliminating the need for external water and energy sources while reliably meeting regional demands. In the context of the Makran coasts case study, our comprehensive mathematical model demonstrates an optimal allocation with 96.67 % attributed to fixed costs and only 3.33 % to variable costs. Moreover, this model precisely optimizes the locations of two desalination centers, two storage facilities, and ten water treatment centers, effectively managing the need for external water resources. Ultimately, through a rigorous sensitivity analysis, we unveiled that the chance constraint parameters have a significant impact on the variable costs.

Greenhouse Gas Mitigation Potential Through Clean Energy for Cement Production in India

Abstract A preliminary approach has been made to assess the concentrated solar energy applications in cement production as well as greenhouse gas mitigation potential. The work starts with identification of processes that utilize thermal energy in cement production. Then, a cluster of cement plants located at different locations in the country has been made. Also, the availability of solar radiation and wind speed at each plant location have been identified. Subsequently, the solar industrial process heating systems have been developed for different locations in the country. Further, solar reactor output, number of heliostats, total land area and mirror surface have been estimated. All these estimations are done by considering three types of thermal losses in solar reactors, i.e., 15, 30 and 45%, respectively. Solar energy can provide a total of 738.11 PJ of thermal energy, which is needed to fulfill the process heating requirement of the calcination process for the manufacturing of cement in India. Solar industrial process heating systems for cement production in India can reduce yearly CO2 emissions by 2457–7648 thousand tons.

Compressor Surge Identification in Innovative Heat-Pump Systems Equipped by Vaned Diffuser Centrifugal Compressor

Abstract In the present energy scenario, heat-pumps play an important role to improve energy efficiency in reversible cooling systems. In case of industrial size plants, centrifugal compressors are preferred with respect to other solutions; nonetheless, they work in variable operation and therefore they must withstand off-design conditions. Carrier provided the University of Genoa with a small size chiller rig equipped with an innovative high speed centrifugal compressor driven by a variable speed motor to study unstable operation of refrigerant closed loop systems. To this aim, vibro-acoustic signature analysis of such closed loop rig is performed mainly focusing on compressor mechanical response. This experimental characterization is firstly conducted in system stable operation. Afterwards, surge transients are obtained by acting on plant feeding lines valves; meanwhile, experimental signals are acquired at relevant plant locations to investigate system response just before instability onset. Indeed, system dynamics is affected by interposed volumes, and therefore the effect of heat exchangers in system response may be relevant. Therefore, system dynamics is analysed both in sub-synchronous frequency range and in high frequency region to assess how its vibro-acoustic response varies when moving from stable conditions towards surge. By doing so, suitable surge precursors can be defined by relying only on vibro-acoustic signals both in low and high frequency ranges to perform early surge detection in such chiller systems. The proposed approach main advantage is to exploit non-intrusive probes, which allow to define diagnostic indicators without interacting directly with the working fluid.

The geography of wild American ginseng in North Carolina, USA: a comparative study of four capability analysis methods

Wild American ginseng is the root component of a family of plants called Panax, and it grows mainly in the understory of the eastern deciduous forests in North America. The growing market for wild American ginseng in Asia along with the scarcity of this plant in the wild due to rising illegal harvest have been simultaneously pushing up the price of American ginseng in the market, increasing incentives further for illegal harvest of this soon-to-be-extinct species. This paper studies the geography of wild American ginseng to identify the spatial distribution of the suitable habitat of this plant in the wild and provides advice on what conditions are required and what regions are suitable for cultivating it in farms, introducing an alternative to the wild American ginseng to decrease the harvesting pressure on its wild counterpart. By employing four popular geospatial methods, including the Binary Screening method, the Ordinal Ranking method (both Graduated Screening and Addition-of-Factors methods), and the Weighted Linear Combination method, this paper carries out a capability analysis and mapping for wild American ginseng in North Carolina (NC). After building the models, they are validated by using the data of the known locations of wild American ginseng plants in NC. Developing these models helps in gaining a more comprehensive understanding of where the plant naturally grows and sheds light on how government agencies can more effectively and more efficiently plan for law enforcement activities to better protect this plant from illegal harvest. The general pattern of our results suggests that western NC counties such as Jackson, Haywood, Transylvania, Henderson, Cherokee, and Ash are some of the most suitable geographical areas for wild American ginseng to grow. The results of our model validation analysis, along with the comparison of our models' predictions to the observed occurrences of wild American ginseng in nature, indicate that the Binary Screening method’s predictions align with almost 96% of the reported observations in nature. This suggests that the influential natural factors necessary for wild American ginseng are more of complementary factors to each other than being substitutes, meaning that they all must exist in the environment for it to grow, and it could not be that the lack of one natural factor can be compensated by the abundance of another natural factor.

Mitigation of Photovoltaics Penetration Impact upon Networks Using Lithium-Ion Batteries

The paper conducts a comprehensive analysis of the impact of very large-scale photovoltaic generation systems on various aspects of power systems, including voltage profile, frequency, active power, and reactive power. It specifically investigates IEEE 9-bus, 39-bus, and 118-bus test systems, emphasizing the influence of different levels of photovoltaic penetration. Additionally, it explores the effectiveness of battery energy storage systems in enhancing system stability and transient response. The transition to PV generation alters system stability characteristics, impacting frequency response and requiring careful management of PV plant locations and interactions with synchronous generators to maintain system reliability. This study highlights how high penetration of photovoltaic systems can improve steady-state voltage levels but may lead to greater voltage dips in contingency scenarios. It also explores how battery energy storage system integration supports system stability, showing that a balance between battery energy storage system capacity and synchronous generation is essential to avoid instability. In scenarios integrating photovoltaic systems into the grid, voltage levels remained stable at 1 per unit and frequency was tightly controlled between 49.985 Hz and 50.015 Hz. The inclusion of battery energy storage systems further enhanced stability, with 25% and 50% battery energy storage system levels maintaining strong voltage and frequency due to robust grid support and sufficient synchronous generation. At 75% battery energy storage system, minor instabilities arose as asynchronous generation increased, while 100% battery energy storage system led to significant instability and oscillations due to minimal synchronous generation. These findings underline the importance of synchronous generation for grid reliability as battery energy storage system integration increases.

Predicting power and solar energy using neural networks and PCA with meteorological parameters from Diass and Taïba Ndiaye

The excessive reliance on conventional fossil fuel-based resources poses a significant threat to our environment. To mitigate this impact, it has become increasingly crucial to increase the integration of intermittent and non-polluting energy sources into our electrical grids. However, while this higher penetration rate brings benefits such as improved producer satisfaction and reduced fossil fuel consumption, it also presents challenges for traditional non-smart electrical networks. To promote intermittent energy sources effectively and maintain a balance between consumption and production, accurate forecasting of these energy outputs plays a vital role. This research paper focuses on studying the application of artificial neural networks for predicting the power and energy output of the Diass solar power plant in the short and medium term. The proposed approach utilizes not only the meteorological data from the city where the power plant is located but also data from a nearby city with a data acquisition station. Principal component analysis (PCA) is employed to select the relevant variables for the prediction model. Furthermore, the results obtained from our approach are compared to existing literature that solely uses meteorological data from the power plant's location. The comparison shows that our method achieves more satisfactory results, with mean absolute errors and root mean square errors of 0.0223 KWh and 0.003 KWh, respectively, and a prediction accuracy of 94.57% in terms of energy and power. It is worth noting that the computational resource requirements for our approach are higher, with simulation times ranging between 1788 seconds and 2201 seconds. By utilizing a broader range of data sources and employing advanced techniques like artificial neural networks, this research contributes to improving the accuracy of solar power generation forecasts. The findings highlight the potential of incorporating additional data inputs and advanced modeling techniques to enhance the performance of renewable energy systems, paving the way for a more sustainable and efficient energy future.