Exploitation Of Renewable Energy Sources Research Articles

How can managerial practices for Circular Business Models contribute to achieving carbon neutrality? A taxonomy of their preventive and corrective role

The implementation of managerial practices for Circular Business Models (CBMs) can significantly advance a sustainability transition pathway of companies, particularly in addressing environmental issues. However, there is a lack of studies investigating how these practices can contribute to achieving carbon neutrality. This research gap is addressed in this study using a mixed-methodological approach: the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Delphi methods. From the literature, we identified a set of eight prevalent managerial practices for CBMs implemented by companies across various industry sectors. The experts involved in the empirical analysis assessed this set of practices using first the DEMATEL, and a preliminary taxonomy showing the contribution of managerial practices to achieving carbon neutrality was proposed, considering their preventive (i.e., avoiding carbon emissions) and corrective (i.e., capturing and storing carbon from the atmosphere) role. The preliminary taxonomy was subsequently refined through the Delphi method to reduce its degree of subjectivity and enhance its effectiveness. Our results show how managerial practices for CBMs can play both a preventive and corrective role in achieving carbon neutrality, with ‘Product Design for Circularity’ and ‘Energy efficiency and usage of renewable energy sources’ standing out as contributing more than the others. Companies aiming to pursue a decarbonization strategy can also leverage a range of managerial practices for CBMs.

Solar Collectors with Nanofluid for Domestic Applications: A Review

Energy management and the usage of renewable energy sources are essential for a sustainable future. This paper carried out a review and summarize the recent topic of solar collectors heating water for use in a domestic application. Several researchers have studied solar collectors with flat plates, evacuated, and concentrators to improve heat gain for heating water. Entropy is an important function that the attention of investigators to reduce and increase the thermal efficiency of solar collectors. Nanofluid has been selected as have found a new class of thermal efficiency enhancement for this purpose. Thermal performance is enhanced by changing the geometry of the absorber plate of the solar collector and the evacuated tube is the best. It was found that increasing nanoparticle concentration to 4% improves solar collector efficiency to 30%. It was observed that the report of solar-collector enhancement by changing geometry and fluid flow through the solar-collector.

Effect of geopolitical risk on energy consumption policy: New empirical evidence from BRICS

This study investigates the impact of geopolitical risk (GPR) on energy consumption. For empirical analysis, we utilize the dataset of BRICS nations spanning 25 years from 1998 to 2022. We employ three econometric models, namely fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and autoregressive distributed lag (ARDL), to analyze the relationships between GPR and energy consumption. Our empirical findings reveal several significant insights. Firstly, we observe a substantial negative influence of GPR on both fossil fuel energy consumption (FEC) and total energy consumption (TEC). This suggests that higher levels of GPR are associated with reduced utilization of fossil fuels and overall energy consumption within the BRICS countries. Conversely, we identify a significant positive effect of GPR on renewable energy consumption (REC). This implies that, as GPR rises, there is a corresponding increase in the adoption and usage of renewable energy sources. Furthermore, our analysis uncovers the presence of asymmetric effects pertaining to other key determinants of energy consumption, including FDI inflow, economic growth, banking sector development, and inflation rate. This study offers fresh empirical evidence on the intricate interplay between GPR and energy consumption in BRICS nations, shedding light on the significant impacts of GPR and the nuanced effects of various economic factors. These findings have important implications for policymakers and stakeholders seeking to navigate energy policy decisions in a geopolitically dynamic world.

Using MCFC for capturing CO2 from flue gases and delivering to Sabatier reactor for SNG synthesis

In contemporary power generation, enhancing efficiency and mitigating environmental contamination are of paramount importance. The imperative to curtail greenhouse gas emissions stands as a preeminent challenge within this sector. Concurrently, there is a marked surge in the exploitation of renewable energy sources, which, due to their intermittent nature, precipitates the imperative for advanced energy storage solutions. This paper introduces an integrated system designed to address both the reduction of CO2 emissions and the storage of energy. The advocated system integrates a Molten Carbonate Fuel Cell (MCFC), Solid Oxide Electrolysis Cell (SOEC), and a Sabatier reactor. The MCFC is employed for its proficient CO2 capture capabilities at the cathode, exhibiting remarkable efficiency, operational flexibility, and a high CO2 separation quotient. The SOEC is recognized for its effective hydrogen production, leveraging high operational temperatures to augment hydrogen output while diminishing electrical energy consumption through thermal energy substitution. The Sabatier reactor is utilized for catalytic methanation, transforming CO2 into Substitute Natural Gas—a compound predominantly comprising methane and hydrogen with minimal CO2 and water traces. This system facilitates the capture and utilization of over 80% of CO2 from exhaust fumes, achieving an overall energy efficiency of 71%. The system's design and off-design operational parameters were meticulously modeled and analyzed.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Calcium Manganite Based Materials for Thermochemical Energy Storage in High Temperature Solar Thermal Plants: Materials Screening

The urgent need for sustainable energy supply requires maximum exploitation of renewable energy sources. The latter, being of intermittent nature, need to be coupled with efficient energy storage. Solar-thermal power-plants are inherently compatible with thermal storage, which is a cost-efficient method of storing energy for later use but the field is currently dominated by sensible heat molten salts used as heat storage media but with a maximum operating temperature of about 560oC. Certain ceramic materials, able to induce reversible reduction-oxidation reactions under air flow, are promising alternatives to molten salts because they can withstand much higher temperatures (>1000oC) and thus can be integrated with high-efficiency air-Brayton thermodynamic cycles. At the same time the chemical energy stored/released during such reduction-oxidation reactions can boost energy storage density by up to 10 times cf. sensible only concepts. In this framework, Ca-Mn-based perovskite compositions were demonstrated to function effectively as energy storage materials. The current work offers insights on material synthesis parameters to achieve relatively high purity Ca-Mn-based compositions and subsequently optimize their redox performance in the course of a preliminary 5-cycle campaign. Moreover, the occurring structural transitions and their corresponding heat effects are also discussed and elaborated upon. This study is the first step towards the, currently in progress, process of synthesising – at multi kg scale – and shaping these compositions into extruded honeycomb-like monolithic structures for subsequent future application in lab- and pilot-scale high temperature thermochemical energy storage systems.

Sustainable Economic Development in China: Balancing Growth and Environmental Concerns

Purpose: Over the last three decades, China’s economy has grown remarkably, ranking second in the world among all countries. China’s market-oriented policies, which give priority to economic development, are credited with the country’s economic boom. China’s remarkable economic growth in recent years has had a significant effect on the environmental balance of the nation. Presently, Chinese authorities must address environmental issues without impeding the nation’s progress. Even though China’s economy has expanded quickly since reforms and the opening up, the country’s environmental degradation issue has gotten more serious.Examine China’s current strategies for economic development and their effects on environmental sustainability and economic growth.The dynamic connects between the highlighted factors (renewable consumption of energy, economic expansion, oil rent, and resources of all kinds) and the increase in greenhouse gas emissions—a proxy for environmental sustainability—is examined in this article using China as an example.The findings of an empirical study using every year data from 2018 to 2022 and the auto-regressive distribution lag limits technique point to the existence of a long-run equilibrium link between each of the variables highlighted and greenhouse gas emissions. Specifically, since they have a negative impact on greenhouse gas emissions, the usage of renewable energy sources and petroleum rents contribute to ecological sustainability.In conclusion, China’s commitment to sustainability may benefit from some of the remedies this paper’s results suggest. This study demonstrates the useful use of New Structure Economy in a crucial industrial area in China, adding to the body of knowledge on sustainability in the environment and economic development.

Heat pumps to upgrade existing CHP-DHN systems towards new generation thermal networks

District heating networks with combined heat and power systems and renewable energies are one of the most promising solutions for efficient and sustainable energy supply. In many cases, however, especially for district heating networks prior to the 4th generation, significant renovations are required to meet decarbonization targets. In this paper a study is proposed to evaluate the integration of high temperature heat pumps in an existing combined heat and power - district heating plant to reduce fossil fuel consumption and increase the exploitation of renewable energy sources. The plant is currently operating in central Italy and connects more than 1250 users. The identified solution implies lowering the district heating networks operating temperature and supplying power peaks with a high temperature heat pump acting as a booster. Results showed significant improvements in system performance especially in the winter months, due to the greater impact of lowering the temperature level of the district heating network during these months. Overall, the updated scenario allows the overall demand and ground heat losses to be reduced annually by 5.3 % and 13.5 % respectively. This reduces natural gas consumption by 13.3 % and avoids the emission of about 836 tCO2. The analysis provides guidelines for the upgrade of 3rd generation district heating network that can be useful for planning improvements towards newest generation thermal networks.

Environmental deterioration, renewable energy, natural resource rents, and schooling in Türkiye: Does the degree of energy transition matter for environmental quality?

Within the literature on energy and environmental economics, it is generally acknowledged that renewable energy can improve environmental quality; however, certain papers suggest that an optimal level of the usage of renewable energy sources may exist. Consequently, the utilization of renewable energy sources can result in environmental degradation up to a certain threshold. Then, environmental quality can be enhanced through the continued application of renewables. This indicates that the link between renewable energy and environmental devastation is inverted U-shaped.This paper presents empirical evidence concerning this possible association between renewable energy and environmental destruction in Türkiye, a country where fossil energy predominates in the energy mix. Additionally, the paper investigates the environmental influences of natural resource rents and schooling. This study utilizes annual data from 1971 to 2020 and implements time series methodologies that rely on the Fourier approximation. The paper thus accounts for an undetermined quantity of structural breaks. The results suggest that an inverted U-shaped link occurs between renewable energy and environmental destruction, signifying renewable energy initially contributes to a diminution in environmental quality before subsequently improving it. Additionally, environmental quality is positively associated with natural resource rents and negatively associated with schooling, according to the findings. Furthermore, the findings reveal that schooling worsens the combined effect of renewable energy on environmental degradation. These conclusions are discussed in the paper.

Optimal Power Extraction for PMSG based WTG through monotonous Power Increments

Abstract Due to the uncertainty in the cost of fossil fuels due to various reasons, its depleting trend in reserves and the consequential pollution caused due to its usage forced to enhance the usage of renewable energy sources. As the characteristic behavior of the renewable energy extraction systems are exhibiting non-linear characteristic, it is quite cumbersome to extract power from the renewable systems in a straight forward manner. In the case of wind energy systems, the change in wind speed through the day makes the extraction of power still more difficult. Hence, different algorithms with the intention of extracting maximum possible available power are implemented. Different algorithms are already available whose behavior depends on the various parameters to be measured and controlled. In this paper one such algorithm, which is simple in nature is proposed. The algorithm takes the benefit of the monotonicity of power with respect to the wind turbine’s spinning speed. The algorithm senses the power increments in conjunction with the increments in turbine speed and implements further increment magnitude in such a manner to reach the maximum power point.

Forest 4.0: Technologies and digitalization to create the residual biomass supply chain of the future

Currently, there is an increasing pressure to decarbonize and meet energy demands. In this regard, the usage of renewable energy sources is becoming more prominent. Biomass energy can significantly contribute to this effort. However, due to logistical costs, its exploitation is often considered less ‘desirable.’ Sometimes, particularly in the context of residual biomass, open burning on site is desirable. This study aims to optimize the residual biomass supply chain by leveraging digitalization's advantages, thereby creating a new model for this purpose. Methodologically, this research initially adopted an empirical approach, analyzing documents from a project that recreated the current scenario. In its second phase, the study involved a literature review to gather insights needed for developing the final model. Consequently, this study offers three major contributions: a detailed mapping of the current scenario, a comprehensive synthesis of the potentials of emerging technologies in the supply chain, and a model illustrating the integration of these technologies in the context of residual biomass. Thus, this research presents an opportunity to transform a common ‘leftover’ into a value-added material. To better assess the model's value, two analyses, PEST and SWOT, were conducted to align the conceptual proposal with real-world contexts.

Commercial Small-Scale Horizontal and Vertical Wind Turbines: A Comprehensive Review of Geometry, Materials, Costs and Performance

The effective exploitation of renewable energy sources is one of the most effective solutions to counter the energy, environmental and economic problems associated with the use of fossil fuels. Small-scale wind turbines (converting wind energy into electric energy with a power output lower than 50 kW) have received tremendous attention over the past few decades thanks to their reduced environmental impact, high efficiency, low maintenance cost, high reliability, wide wind operation range, self-starting capability at low wind speed, limited installation space, reduced dependence on grid-connected power and long transmission lines, low capital costs, as well as the possibility to be installed in some urban settings. However, there are significant challenges and drawbacks associated with this technology from many different perspectives, including the significant discrepancy between theoretical performance data provided by the manufacturers and real field operation, that need to be investigated in greater depth in order to enable a more widespread deployment of small-scale wind turbines. In this review, a complete and updated list of more than 200 commercially available small-scale horizontal and vertical wind turbine models is provided and analysed, detailing the corresponding characteristics in terms of the number and material of blades, start-up wind speed, cut-in wind speed, cut-out wind speed, survival wind speed, maximum power, noise level, rotor diameter, turbine length, tower height, and specific capital cost. In addition, several scientific papers focusing on the experimental assessment of field performance of commercially available small-scale horizontal and vertical wind turbines have been reviewed and the corresponding measured data have been compared with the rated performance derived from the manufacturers’ datasheets in order to underline the discrepancies. This review represents an opportunity for the scientific community to have a clear and up-to-date picture of small-scale horizontal as well as vertical wind turbines on the market today, with a precise summary of their geometric, performance, and economic characteristics, which can enable a more accurate and informed choice of the wind turbine to be used depending on the application. It also describes the differences between theoretical and in-situ performance, emphasizing the need for further experimental research and highlighting the direction in which future studies should be directed for more efficient design and use of building-integrated small-scale wind turbines.

Optimising low-temperature district heating networks: A simulation-based approach with experimental verification

Fifth generation district heating and cooling systems are becoming increasingly popular due to their ability for working with low temperature of heat transfer fluids. Among the other benefits, this characteristic allows for a better exploitation of renewable energy sources. On the other hand, these networks require a fine design and precise management to exploit their full potential. Both these requirements can be met by using advanced simulation and optimisation tools. This research proposes a simulation tool purposely conceived for the design and the optimisation of fifth-generation district heating and cooling systems. This tool is capable of assessing the effects of each building-plant system on the whole district heating and cooling water loop, and to evaluate the effectiveness of diverse network morphology. These capabilities are due the level of detail of the mathematical modelling which takes into account the thermohydraulic characteristics of the network, each building thermo-physics properties, and the heat pump/chiller detailed operation. The described tool has been adopted to simulate an existing experimental network prototype (consisting of a central heat pump, behaving as thermal energy balancing station, and eight users), and the achieved results were compared to those experimentally obtained for validation aims. The capabilities of the validated tool have been demonstrated by investigating an innovative control logic (representing a further novelty of this research) for a “proof-of-concept” fifth-generation district heating and cooling network. In particular, by adopting a predictive control logic, the water loop temperature is dynamically optimised to minimise the entire network energy demand. The adopted control strategy has yielded significant primary energy savings, amounting to 10.3 MWh/year, with a rate of 6.5 % compared to the reference case characterised by a fixed network temperature. These results underscore the potential of the proposed method and demonstrate the effectiveness of the developed tool.

Valuation of Internet of Energy (IoE) Platforms in Smart Cities: A Hybrid Multi-Criteria Decision Making Approach

A smart city is a place where people live using computing, sensing, and networking technology in a variety of ways, including smart buildings, smart homes, smart transportation, smart workplaces, smart offices, smart industries, and smart agriculture. These smart technologies need to connect so; we need to use the Internet of Things (IoT) that has applications in many areas of our smart lives. IoT has been used due to the growing usage of renewable energy sources and the challenges of network management. Internet of Energy (IoE) integrates the IoT and smart grid capabilities. The basic role of smart grids or IoE is an essential element in creating smart cities. In this work, we present some IoE platforms that represent the alternatives and their criteria. The proposed study treats the uncertainty of multi-criteria decision-making (MCDM) modified by single value triangular neutrosophic number (SVTrN) by choosing the optimal IoE platform based on the criteria importance through inter-criteria correlation (CRITIC) method and multi-attributive ideal-real comparative analysis (MAIRCA). The proposed model proved its effectiveness by applying it to an experimental case. The results show that the DCC (A3) platform is the best one.

Minimizing the Effect of Integrating Wind Farms on Transmission Power Systems

Expanding the renewable energy sources usage in power systems will help the government meet its energy policy goals of lowering emissions, ensuring high power supply reliability, and promoting spot markets with high competitivity. However, due to the intermittent nature of these sources, this will necessitate the mitigation of network vulnerabilities. High wind energy penetration in the grid could have an impact on the system frequency, voltage, real and reactive power response, and power quality of the system. The main objective of this paper is to investigate how the integration of wind farms in the Jalan Bani Bu Ali (JBBA) area will affect Oman's main interconnected system (MIS). Results show that the effect is minor on voltage and frequency stability by all disturbances taken into account.

Technical innovation, renewable energy consumption, and CO2 emissions in the USA: a cross-quantile approach.

This study investigates whether technological innovation and the consumption of renewable energy tend to reduce the emissions of CO2 in the USA by analyzing datasets from January 2010 to May 2022. The main contribution to this study is that we applied a cross-quantile approach, which possesses several strengths compared to other methods used for directional predictability. The empirical results of this research can be concluded as three points: (1) both the consumption of renewable energy and technological innovation significantly and negatively impacted the emissions of CO2 in the short run (i.e., 1month) across high quantiles, which gradually diminished over time (i.e., 3months, 12months, and 24months), implying that technological innovation and the consumption of renewable energy possess a short-lived effect on CO2 emissions, respectively; (2) this relationship remains significant for causal links spanning 1 and 3months and 1 and 2years when the consumption of renewable energy and technological innovation are treated as control variables respectively; (3) a recursive cross-quantilogram was constructed to support further our findings, which showed that the consumption of renewable energy and technological innovation tend to negatively impact the emissions of CO2 across all quantiles. These results imply that an increase in the consumption of renewable energy and technological innovation can curb CO2 emissions in the USA; these effects tend to be more lasting when technological innovation and the consumption of renewable energy are combined. Therefore, future policies focused on curbing the emissions of CO2 should pay attention to the combined effect, which is the promotion of technological innovation and the exploitation of renewable energy sources in the USA.

Does tourism development, financial development and renewable energy drive high-quality economic development?

Tourism development is generally agreed upon to be a key tool in promoting economic growth, and green development has emerged as a significant idea and an efficient approach to accomplish this goal in a manner that is environmentally responsible. It is common knowledge that making the switch to renewable sources of energy may act as a catalyst for economic development in both developed and developing nations. Therefore, people all over the globe are beginning to realize the significance of advancing renewable energy's rising importance that suggests that it will be used extensively in the years to come. The purpose of this study is to examine the effect that increasing tourism and adopting renewable energy sources impact on green economic growth development in the region using the AMG test BRICS nations from 2001 to 2022. This research additionally makes use of a rigorous check by means of the CCEMG exam and the DCCEMG test. According to the findings, green economic growth is influenced favorably by the expansion of the tourist industry, renewable energy, and the digital economy, but urbanization and the rise financial industry are detrimental to green economic growth. D-H panel causality test results show that tourism development is causally related to green economic growth, green economic growth to urbanization, and green economic growth to the usage of renewable energy sources. According to these results, the management authorities of BRICS nations should embrace policies of green growth while also controlling environmental pollution in order to achieve sustainable economic development whereas in rural areas. The findings have major policy implications for the nations that make up the BRICS bloc. These implications pertain to the enhancement of tourist development, the digital economy (DIG), and financial institutions, all of which have the potential to improve environmental quality.

Does Higher Institutional Quality Result in Environmental Sustainability? A Comparative Study of India and China

There is a lack of comprehensive research comparing India and China in terms of institutional quality and environmental degradation. This special issue aims to make threefold distinct contributions. By comparing India and China, we can learn from each other's experiences and gain insight into the shared difficulties both nations face when trying to achieve environmental sustainability in the face of high-quality institutional frameworks. The impact of corruption, economic development, ecological risk, and renewable energy sources on carbon dioxide emissions in China and India is investigated in this study. For this reason, we also employ the ARDL model and pairwise Granger causality to prove that something caused another. Applying the ARDL long-run bound test, the empirical results reveal a long-run connection between variables in both China and India. In the case of China and India, the estimated results also reveal a negative correlation between carbon emissions and the usage of renewable energy sources, but a positive correlation between carbon emissions and economic growth, corruption, and environmental risk. Improving institutional quality is the goal, and the present theoretical and applied discourse on comparative environmental sustainability is a good place to start. As a result, policymakers in China and India should oppose unfair practices that undermine competition laws and policies by instituting stringent anti-corruption measures and environmental rules. Energy efficiency measures that reduce carbon emissions without slowing economic growth should also be the government's primary focus.

The Intricate Impression of Tourism, Patent, And Renewable Energy on Environmental Sustainability

The growing global attention to indicating climate conversion and plummeting carbon dioxide (CO2) emanations has spurred researchers to examine the influence of several factors on promoting environmental sustainability. This research examines the intricate role of tourism, patent applications, rent from natural resources, and utilization of renewable power in carbon dioxide secretions in Bangladesh. For this purpose, data has been collected from 1995 to 2019 from World Development Indicators (WDI). Augmented Dickey-Fuller (ADF) and the Phillips-Perron (PP) and the ARDL bound cointegration procedures are employed to determine the existence of stationary data and cointegration among variables respectively. The Autoregressive Distributive Lag (ARDL) procedure is used to consider the effects of the chosen variables on CO2 emissions over the short and long run. VIF test, White's test, and the Breusch-Godfrey test are employed to check multicolinearity, homoscedasticity, and autocorrelation respectively. The research discloses that it is vital to expand the usage of renewable energy sources while simultaneously decreasing natural resource rent by limiting natural resource extraction for a sustainable environment. Although the statistical insignificance of the long-term coefficient of tourism has been observed, the study reveals a negative coefficient for tourism, suggesting the possible efficacy of sustainable tourism practices for mitigating CO2 emissions. The findings also demonstrate the long-term contribution of innovation activities to environmental degradation through the positive coefficient of patent applications. The research results offer significant insights for policymakers and stakeholders involved in the pursuit of a sustainable and ecologically conscious future in Bangladesh.

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.