Adoption Of Renewable Energy Sources Research Articles

Assessing Voltage and Frequency Instability in Renah Kemumu's Micro Hydro Power Plant

The increasing demand for energy and the need for environmental sustainability have led to the adoption of renewable energy sources, such as micro-hydropower plants (PLTMH), in isolated communities in Indonesia. However, voltage and frequency instability issues are commonly encountered in PLTMH systems, which undermine their performance. In 2009, a micro-hydropower plant with a capacity of 30 KW was built in Renah Kemumu village. Ranah Kemumu village is located in Jangkat sub-district, Merangin district, Jambi province. In this study, the voltage and frequency instability of PLTMH in Renah Kemumu Village were investigated. The methodology employed in this study was observational. The voltage and frequency data were measured and recorded during peak load hours from 18.00 to 21.00 over one month. The percentage increase relative to the standard voltage and frequency was calculated. Boxplots were generated to visualize the distributions. The voltage measurements showed maximum and minimum values of 270 V and 264 V, representing increases of 22.72 % and 20.45% from the 220 V (standard set by the power utility (PLN)). The frequency ranged from 66.32 Hz to 57.46 Hz, corresponding to 30.92% and 14.92% deviations from the 50 Hz (standard set by the power utility (PLN)). These values exceeded the tolerance limits of the State Electricity Company of Indonesia. The analysis indicates that the voltage and frequency levels at the PLTMH in Renah Kemumu Village are unstable during peak loads, with fluctuations stemming from changing water flows, load currents, and weather conditions

Solar Radiation Acquisition and Data Analysis Using Arduino

The rapid increase in energy demand and environmental concerns has driven the adoption of renewable energy sources. Solar energy, being abundant and sustainable, has gained significant attention. Efficiently harnessing and monitoring solar energy systems is crucial for optimizing performance. This research focuses on developing a solar monitoring system using Arduino, a versatile and cost-effective microcontroller platform. The system aims to measure solar irradiance, temperature, and voltage in real-time, providing comprehensive data acquisition and analysis capabilities. This project aims to contribute to the advancement of solar energy technology by offering an effective and affordable solution for monitoring and optimizing solar installations.

Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI

The adoption of renewable energy sources has seen a significant rise in recent years across various industrial sectors, with solar energy standing out due to its eco-friendly characteristics. This shift from conventional fossil fuels to solar power is particularly noteworthy in energy-intensive environments such as cloud data centers. These centers, which operate continuously to support active servers via virtual instances, present a critical opportunity for the integration of sustainable energy solutions. In this study, we introduce two innovative approaches that substantially advance data center energy management. Firstly, we introduce the Genetic Reinforcement Learning Algorithm (GRLA) for energy-efficient container placement, representing a pioneering approach in data center management. Secondly, we propose the Hybrid Attention-enhanced GRU with Random Forest (HAGRU-RF) model for accurate solar energy prediction. This model combines GRU neural networks with Random Forest algorithms to forecast solar energy production reliably. Our primary focus is to evaluate the feasibility of solar energy in meeting the energy demands of cloud data centers that utilize containerization for virtualization, thereby promoting green cloud computing. Leveraging a robust German photovoltaic energy dataset, our study demonstrates the effectiveness and adaptability of these techniques across diverse environmental contexts. Furthermore, comparative analysis against traditional methods highlights the superior performance of our models, affirming the potential of solar-powered data centers as a sustainable and environmentally responsible solution.

A new deep learning method for classification of power quality disturbances using DWT-MRA in utility smart grid

Distributed generation sources (e.g. photovoltaic, wind power) are raised to fit the energy demands recently in proficient way. Further, adoption of renewable energy sources to the smart grid causes severe issues in quality of power signals. These problems can be overcome with correct identification and classification of power quality disturbances (PQDs) efficiently by the both producer and consumer. In this work, a new method for classification of PQDs based on signal processing technique and deep convolutional neural network (DCNN) is proposed. The proposed method compress the raw data of PQDs by using discrete wavelet transform (DWT) and multi-resolution analysis (MRA). Moreover, CNN has the ability to automatic feature selection and classification in a single stage instead of conventional methods i.e. artificial neural networks (ANNs). In this paper, DWT 1D-CNN is designed to extract and optimize multi-scale characteristics of disturbances. Further, 1-D convolutional, pooling and batch-normalization layers are added to facilitate normalize data, better generalization ability and, effective classification process against noisy data. A typical 11Kv distribution network is simulated in MATLAB to prove the validity of the proposed method in an essential part of smart grid.

The acceptance of biogas in Islamic Boarding School Surakarta

The rapid population growth in Indonesia is leading to increased energy demands and the depletion of global energy sources. One promising to meet these escalating energy needs in the adoption of renewable energy sources like biogas. This study employs qualitative research methods to measure the response of the Islamic boarding school community in Central Java towards biogas utilization. Data analysis was conducted interactively, narratively describing insights gleaned from questionnaires completed by respondents. The findings reveal a varied response within Central Java Islamic Boarding School Community towards embracing biogas as part of efforts to enhance the national renewable energy mix. However, there is a consensus on certain aspects: respondents generally support biogas utilization as long as it is safe, sourced from clean materials, and developed in suitable locations. The exploration among ten boarding schools suggests that the government’s renewable energy initiatives require further optimization in terms of socialization and engagement with Islamic boarding schools.

Recent advances on control strategies for microgrid islanding operation

In order to achieve the national strategic goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, the electricity-energy-ecosystem is undergoing significant transformation, presenting new opportunities for the development of new energy generation technologies. With the widespread adoption of renewable energy sources, such as solar and wind energy, microgrids have begun to play a prominent role as small-scale power systems. Microgrids, as small, autonomous power systems, have received significant attention in recent years. Microgrids can enhance energy sustainability by integrating renewable energy resources and energy storage systems, while also providing the capability to maintain power supply during main grid failures or planned maintenance. This article aims to review the advances in control strategy research for microgrid islanding operation, with a focus on the classification of control strategies, design principles, and their impact on microgrid stability. The article also explores potential directions for future research to enhance the efficiency and reliability of microgrid islanding operation, providing valuable references for future studies.

Evaluation of extracting biomass energy using a strategic decision support system

Electricity significantly influences the long-term development of society. Traditional power generation technologies have encountered numerous challenges. The rising demand for power generation and the scarcity of fossil fuel resources have prompted the adoption of renewable energy sources. Biomass is a renewable energy source that can be utilized to generate power and promote a more sustainable environment. This paper presents improved Pythagorean Probabilistic Hesitancy Fuzzy Set (PPHFS) multi-criteria information fusion processes. The development of connection numbers (CN) and the proposal of a distance measure between CNs have been initiated. The suggested solution involves a distance measure based on the CN score function. Set-pair analysis (SPA) is a prior uncertainty theory that incorporates three elements of CN and overlaps with the PPHFS. Biomass energy generation poses a fuzzy multi-criteria decision-making (MCDM) problem that requires a wide range of resources for evaluation. The study introduces a novel Projection Ranking by Similarity to the Referencing Vector (PRSRV) and Simultaneous Evaluation of Criteria and Alternatives (SECA) to effectively handle data ambiguity and generate weights for all criteria in the MCDM problem. The criteria’s weights were found to fall within the range of 0.2122 and 0.0232. The proposed method indicates that animal residues are the most desirable resource for bioenergy production, followed by municipal solid residues, agricultural residues, forest and crop residues, industrial residues, and algae. The research aims to assist decision-makers and organizations in evaluating and prioritizing various energy sources on a sustainable scale. The method presented exhibits superior data adaptability compared to other MCDM approaches.

Carbon Emission Analysis and Reporting in Urban Emissions: An Analysis of the Greenhouse Gas Inventories and Climate Action Plans in Sarıçam Municipality

The urban carbon footprint (UCF) is an important tool for assessing an organization’s ecological impacts and in guiding sustainability efforts. This calculation is usually measured in tons of carbon dioxide equivalent (CO2-eq). Calculations provide important data to determine strategies to reduce the carbon footprint and establish sustainability targets. Various standards and protocols guide UCF calculation, and many organizations aim to make these data transparent to their stakeholders and the public. This study aims to calculate the UCF of Sarıçam Municipality (SM) in the Adana Province of Türkiye. This study includes the greenhouse gas emission inventories resulting from all activities of the SM main service building, guest house, construction site service building, Cultural Center service building, and additional service buildings between 1 January 2022 and 31 December 2022. The calculations include generator fuel consumption, electricity consumption, the refrigerant gas leaks and refills resulting from these activities, the fuel consumed in vehicles owned by the company or whose fuel consumption is under company control, emissions originating from personal travel, emissions originating from customers and visitors, emissions originating from business travel, purchases, etc. Emissions from products purchased and emissions from waste transportation are included. The findings show that, in 2022, the total UCF of SM was equal to 10,862.46 tons of CO2-eq. The Paris Agreement aims to reduce the per capita emissions to approximately two tons of CO2-eq by 2030. The carbon footprint per employee within the municipality was calculated at 12.43 tons of CO2-eq, as derived from the analyzed data. The results reveal the importance of implementing sustainable practices and strategies within SM, such as energy efficiency measures, waste reduction, and the adoption of renewable energy sources, to mitigate its carbon footprint. This study plans to provide a basis for SM’s reduction efforts by keeping greenhouse gas emissions under control.

New Decomposition Models for Hourly Direct Normal Irradiance Estimations for Southern Africa

This research develops and validates new decomposition models for hourly direct Normal Irradiance (DNI) estimations for Southern African data. Localised models were developed using data collected from the Southern African Universities Radiometric Network (SAURAN). Clustered areas within Southern Africa were identified, and the developed cluster decomposition models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data are limited or when data are available from multiple nearby stations. Further, a regional Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. The overall metrics affirm the substantial advancement achieved with the regional model as an accurate decomposition model representing Southern Africa. Two stations were used as a validation study, as an application example where no localised model was available, and the cluster and regional models both outperformed the comparative decomposition models. This study focused on validating the model for hourly DNI in Southern Africa within a range of Kt-intervals from 0.175 to 0.875, and the range could be expanded and validated for future studies. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

A Strategic Framework for Sustainable Business Model of Renewable Energy Services

Purpose: The main objective of this research is to investigate the ongoing scientific investigations into sustainable business models framework in the energy sector, with a particular focus on the shift towards renewable energy sources. This entails comprehending how firms may adjust to and propel the transition towards sustainability in accordance with the Sustainable Development Goals (SDGs). Theoretical Framework: The theoretical foundation of this study is anchored in the concept of the triple bottom line, which emphasizes the need for businesses to focus on three key areas: economic, social, and environmental dimensions. This approach is essential for achieving sustainable development, as it ensures that business models are designed to contribute positively to society and the environment, in addition to being economically viable. Methodology: A comparative literature review serves as the methodology for this research. This method involves systematically analyzing existing literature on sustainable business models in the energy sector to identify trends, gaps, and theoretical underpinnings. The comparative aspect allows for the evaluation of different approaches and the identification of best practices and areas for future research. Findings: The research identifies that while there is a growing trend towards the adoption of renewable energy sources, significant challenges remain. These include technological limitations, regulatory barriers, and financial constraints. However, by refining the business model canvas to incorporate elements of sustainability, businesses can create adaptable frameworks that address these challenges. This revised canvas includes components that emphasize economic viability, socio-cultural values, and ecological integrity. Research & Practical Implications: The conceptualization of a sustainable business model canvas is a key practical implication of this research. It offers businesses a clear and systematic way to integrate sustainable practices into their operations. This tool is designed to promote holistic sustainable development and informed decision-making by incorporating economic, socio-cultural, and ecological values into the business planning process. By doing so, it has the potential to facilitate the transition towards more sustainable business practices in the energy sector and beyond, thereby contributing to the achievement of the SDGs. Originality: The originality of this study is grounded in its comprehensive approach to integrating sustainability into business models, its adaptation of existing frameworks to better suit sustainable development goals, and its forward-looking perspective that seeks to guide future research and practical applications in the energy sector and beyond.

Assessing the Role of Hydrogen in Sustainable Energy Futures: A Comprehensive Bibliometric Analysis of Research and International Collaborations in Energy and Environmental Engineering

The main results highlighted in this article underline the critical significance of hydrogen technologies in the move towards carbon neutrality. This research focuses on several key areas including the production, storage, safety, and usage of hydrogen, alongside innovative approaches for assessing hydrogen purity and production-related technologies. This study emphasizes the vital role of hydrogen storage technology for the future utilization of hydrogen as an energy carrier and the advancement of technologies that facilitate effective, safe, and cost-efficient hydrogen storage. Furthermore, bibliometric analysis has been instrumental in identifying primary research fields such as hydrogen storage, hydrogen production, efficient electrocatalysts, rotary engines utilizing hydrogen as fuel, and underground hydrogen storage. Each domain is essential for realizing a sustainable hydrogen economy, reflecting the significant research and development efforts in hydrogen technologies. Recent trends have shown an increased interest in underground hydrogen storage as a method to enhance energy security and assist in the transition towards sustainable energy systems. This research delves into the technical, economic, and environmental facets of employing geological formations for large-scale, seasonal, and long-term hydrogen storage. Ultimately, the development of hydrogen technologies is deemed crucial for meeting sustainable development goals, particularly in terms of addressing climate change and reducing greenhouse gas emissions. Hydrogen serves as an energy carrier that could substantially lessen reliance on fossil fuels while encouraging the adoption of renewable energy sources, aiding in the decarbonization of transport, industry, and energy production sectors. This, in turn, supports worldwide efforts to curb global warming and achieve carbon neutrality.

Multi-Time-Scale Energy Storage Optimization Configuration for Power Balance in Distribution Systems

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the “dual carbon” objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years.

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

This study develops a new technique for optimising Energy Consumption (EC) and occupant satisfaction in business centres using Building Energy Management Systems (BEMS) that implement Deep Reinforcement Learning (DRL). Energy Management Models (EMM) are growing increasingly advanced and vital for intelligent power systems due to the growing demand for energy efficiency and the adoption of Renewable Energy Sources (RES), which are subject to variability. Flawed energy Consumption (EC) and problems are typical effects of traditional BEMS due to their unpredictability and failure to adapt to new environments. In this intended investigation, a DRL framework is demonstrated that may evolve its decision-making in real-time to control energy savings, electricity, and HVAC through input from the environment in which it operates. A pair of significant metrics, namely the cost of energy and room temperature stability, are employed to assess the effectiveness of the model compared to that provided by conventional rule-driven and predictive control systems. As investigated with different baseline models, the experimental findings proved that the DRL approach significantly reduced the cost of electricity while maintaining stable levels of comfort.

Innovative Compact Twin Whirly Bird-based Wind Turbine: A Sustainable Solution for EV Charging Infrastructure

The global adoption of whirly bird vent roofs across various industries underscores their utility in harnessing wind energy for cooling purposes within warehouses and similar environments. The innovation proposed in the current study builds upon this established technology by integrating semi-perpetual motion principles into a compact twin whirly bird-inspired wind turbine design. This novel approach aims to enhance power generation efficiency, thereby offering a sustainable solution for energy production. The compact twin whirly bird turbine concept represents a significant advancement, particularly in the context of powering electric vehicle (EV) chargers. As the demand for EVs continues to surge, concerns persist regarding the availability of charging infrastructure. By integrating the proposed innovative wind turbine technology into the EV charging ecosystem, we address this critical issue while simultaneously promoting the adoption of renewable energy sources. The key to our proposal is the deployment of small-scale wind turbines in diverse public locations, thereby democratizing access to clean energy generation. Unlike conventional large-scale wind turbines, our whirly bird-inspired turbines boast versatility in installation, akin to streetlamps, facilitating widespread deployment across urban landscapes. While these turbines may yield comparatively modest power outputs relative to megawatt-scale counterparts, their strategic placement ensures a decentralized network capable of powering EV charging stations. In summary, our research presents a compelling solution that bridges the gap between EV charging infrastructure availability and the utilization of renewable energy sources. By leveraging the inherent advantages of whirly bird-inspired wind turbines, we offer a practical pathway towards sustainable energy provision in public spaces, thereby facilitating the widespread adoption of electric vehicles.

Determinants of Green Energy Use: Household Level Comparative Analysis of Southern Khyber Pakhtunkhwa

In recent times, the utilization of renewable energy sources has gained significant traction and garnered widespread attention across diverse social segments. The prominent driver behind this surge in popularity is its cost-effectiveness and long-term durability. Notably, even individuals with limited financial means are increasingly drawn toward various forms of renewable energy. This research endeavor aims to draw comparisons between users and non-users of green energy and their corresponding household behaviors. Upon conducting a rigorous data analysis, the study will present a comprehensive and well-founded conclusion, along with offering pertinent recommendations. This study is slated to take place in the rural regions of Southern Khyber Pakhtunkhwa, Pakistan. The study employed random sampling to select a sample from the population and focused on examining the relationship between the utilization of green energy, its characteristics, conventional green energy use, and the quality of solar energy. The primary objective of the study was to explore the choices made by individual households concerning green energy consumption and the factors that influence this choice. The analysis was carried out using Ordinary Least Squares (OLS) regression. This research gap presents a significant opportunity for advancing the promotion and adoption of renewable energy sources.

Multi-scenario flexibility assessment of power systems considering renewable energy output uncertainty

The widespread adoption of renewable energy sources presents a significant challenge to the flexibility of power system. To assess the flexibility of the power system in scenarios with uncertain renewable energy output, it is crucial to quantify it quantitatively. This evaluation plays a vital role in planning flexible regulatory resources and dispatching resources for both the energy source and load. This study introduces a novel flexibility assessment model tailored for power grids with high renewable energy penetration, specifically addressing uncertainty associated with wind and PV. By analyzing the impact of wind and PV uncertainty on system flexibility, the paper proposes an improved cohesive hierarchical cluster analysis method, incorporating reliability considerations based on the Davies-Bouldin classification reliability index. Additionally, the study develops models for flexibility resources and demands within high renewable energy power systems, along with quantitative assessment indicators across three key aspects. Through a structured flexibility assessment process accounting for wind and PV uncertainty, the effectiveness of the proposed approach is validated using real-world data from a renewable energy power grid in Shandong province. A set of typical renewable energy output scenarios with uncertainty is constructed using the improved hierarchical cluster analysis method. The study then analyses the impact of different wind and PV penetration rates and the proportion of energy storage units on system flexibility by the flexibility assessment model to validate the proposed method's effectiveness.

Opportunities and Challenges in AI Based Modern Power System

Nowadays, the power grid has transformed into a dynamic and extensive resource generation and management system. This transformation is primarily driven by the widespread adoption of renewable energy sources and the utilization of intelligent information and communication technologies to handle dynamic workloads. The smart grid encompasses various innovative operations, including power electrification, intelligent information integration at the physical layer, and intricate interconnections. These operations leverage data-driven deep learning, big data, and machine learning paradigms to effectively analyze and control transient issues within the electric power system. Artificial intelligence (AI) has emerged as a crucial tool in addressing and resolving challenges associated with transient stability assessment (TSA) and power generation control. In this research paper, we present a comprehensive review that explores the role of AI and its sub-procedures in tackling TSA problems. The article outlines an AI-based intelligent power system structure, along with the rationality of applying AI to transient situations in power system TSA. Distinguishing itself from other reviews, this paper delves into the AI-based TSA framework and design process, highlighting intelligent applications and their analytical capabilities in addressing power system transient problems. Furthermore, our analysis extends beyond AI alone, as we also explore the integration of big data, which aligns seamlessly with AI. The paper discusses future trends, opportunities, challenges, and open issues pertaining to AI-Big data based transient stability assessment in the smart power grid.

Employing advanced control, energy storage, and renewable technologies to enhance power system stability

As the world witnesses a surge in the adoption of renewable energy sources to meet the surging global power demands, the dynamic and intermittent nature of these sources emerges as a significant hurdle. This article extensively explores the potential of advanced control systems, energy storage technologies, and renewable resources to fortify stability within power systems. Advanced control methodologies are strategically amalgamated with energy storage deployment and the utilization of renewable energy, to advance the reliability, predictability, and sustainability of power systems. The stability analysis, with a dedicated focus on Input-to-input-to-state stability (ISS), is conducted meticulously by applying the Lyapunov function and the Reciprocally Convex Approach, resulting in an impressive stability rate of 23.6%. Additionally, Positive Realness is substantiated by extracting Linear Matrix Inequalities (LMI) in the context of Enhancing Grid Stability with Wind Power. The study places particular emphasis on evaluating ISS and Passivity in both delayed and non-delayed systems, with a specific focus on neutral time-delay systems. This evaluation involves the selection of an appropriate Lyapunov-Krasovskii Functional (LKF) and its derivation, coupled with the integration of reciprocally convex methods, descriptive approach, and Jensen inequality. The outcomes of these analyses shed light on the causes of excess energy and its effective storage, along with highlighting the synergistic impact of integrating renewable sources and controlling grid frequency, voltage, and power in real time. The study also accentuates the robustness achieved through Enhanced Stability and the mitigation of Reduced Fluctuations, especially in the context of renewable energy sources.

Photovoltaic cooling techniques’ effect on the average monthly performance

Nowadays, mitigating climate-altering emissions resulting from air conditioning and mechanical ventilation of indoor spaces is of utmost importance. Encourage the adoption of renewable energy sources for power generation is a critical approach in this regard. Among the available technologies, photovoltaic technology stands as the most mature option. However, it does have limitations, such as reduced efficiency and performance degradation at elevated temperatures. To enhance the efficiency of photovoltaic systems, various solutions have been proposed over time, with significant research focusing on the exploration of new materials. One of the most promising solutions involves panel cooling through the utilization of external fluids, either in a forced or natural manner. Furthermore, the extracted heat from this cooling process can be effectively reused in other industrial processes, adding to its appeal. Nonetheless, despite its potential, the application of panel cooling technology is relatively recent, and assessing its suitability in specific scenarios at an early stage can be challenging. Currently, there is a lack of clear and straightforward methodologies to evaluate the performance gains achievable through the implementation of panel cooling. The primary objective of this research is to present an innovative methodology that can effectively assess panel cooling efficiency on an average daily-monthly basis. Specifically, we propose corrective parameters that modify the widely used Siegel method, which determines the monthly average daily efficiency of uncooled panels. Throughout the study, it has become evident that the input values derived from the UNI standard do not fully represent the real-world conditions. This finding may indicate the necessity for regulatory updates to accurately account for the practical operational environment.