Energy Utilization Efficiency Research Articles

Enhancing Smart Grid Efficiency through Machine Learning-Based Renewable Energy Optimization

Managing renewable energy in smart grids poses a significant challenge due to the inherent uncertainty and variability of energy sources like solar and wind power. To address this issue, we propose a novel approach that leverages the strengths of both Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) algorithms. Our method utilizes ELM to model and predict renewable energy generation, enabling more accurate forecasting and planning. Meanwhile, PSO optimizes the parameters of the ELM algorithm, ensuring optimal performance and efficiency. We evaluated our approach using a dataset of solar energy production and compared its performance to existing optimization techniques. The results show that our ELM-PSO approach significantly improves the accuracy of renewable energy predictions and reduces energy costs in smart grids. The implications of our research are far-reaching, as our approach can be applied to various renewable energy systems, including wind turbines, solar panels, and hydroelectric power plants. By enhancing the efficiency and reliability of renewable energy utilization, we can create a more sustainable and resilient energy future.

Carbon-intercalated halloysite-based aerogel efficiently encapsulating phase change materials with excellent photothermal conversion and energy storage

Latent heat storage systems based on organic phase change materials (PCMs) are considered to be an efficient solar energy utilization strategy, but leakage vulnerability and insensitivity to sunlight of PCMs limit their further application in energy storage. In this work, a new hierarchical porous aerogel was constructed with the carbon intercalated halloysite nanotubes (CHNTs) as the assembly units for PCMs encapsulation to improve the load weight, stability and sunlight absorption. The CHNTs were first successfully synthesized through intercalation and carbonization, with the aim of increasing their specific surface area and sunlight absorption.Subsequently, the CHNTs/polyvinyl alcohol (PVA) aerogels with 3D honeycomb structure were prepared by self-assembly using a freeze-drying method, which can significantly promote the encapsulation ratio of lauric acid (LA) in the pores of both aerogels and CHNTs. The CHNTs can not only improve compressive strength of the aerogels and shape stability of the composite PCMs, but also enhance light absorption ability of the composites compared to pristine HNTs. As a support material, CHNTs aerogel (CHNP) loaded more than 92 wt% PCM (LA@CHNP) and exhibited an extremely high latent heat capacity (170.9 J/g). The LA@CHNP had good structural and thermal stability in 300 cycles without visible leakage. Besides, solar energy absorption increased from 43 % to 98 %, and the solar-to-thermal energy conversion efficiency increased to 95.88 %, which could be attributed to 3D porousness and carbon nanolayers of the CHNTs. We have successfully synthesized an eco-friendly and facile composite LA@CHNP, which will contribute to the design of advanced composites with excellent solar energy storage properties, and provide a new direction for the application of carbonized materials.

A preference adjustable capacity configuration optimization method for hydrogen-containing integrated energy system considering dynamic energy efficiency improvement and load fast tracking

The new hydrogen-containing integrated energy system proposed in this paper has complementary coupling of multiple energy forms such as electricity, gas, cold, heat, and hydrogen. For the same type of load demand, equipment with different energy input forms can be dispatched for joint supply. However, different equipment differ in dynamic response speed and energy utilization efficiency, and the reasonable configuration of equipment capacity will directly affect the overall dynamic characteristics and energy efficiency level of the system. Therefore, this paper first proposes an evaluation index that can comprehensively characterize the dynamic energy efficiency and load tracking ability of energy equipment. In addition, a preference adjustable capacity configuration optimization method based on utopian point tracking is proposed with the two optimization objectives of this indicator and the system's equivalent annualized investment cost, and the solution complexity is reduced through segmented linearization of the objective. Simulations presented here show that the capacity configuration optimization method proposed in this paper has a positive effect on improving the dynamic energy efficiency and load tracking ability of the new hydrogen-containing integrated energy system, and can also meet the preference setting needs of different investment entities for capacity configuration optimization goals.

Efficient layout optimization of offshore wind farm based on load surrogate model and genetic algorithm

Wind farm layout optimization (WFLO) has become a significant approach to enhancing the efficiency of wind energy utilization. However, load also represents a critical factor that must be considered during optimization. To enhance power generation while controlling loads within limitations, an innovative load-constrained layout optimization method that employs a surrogate model based on artificial neural networks and genetic algorithms was proposed. This paper verified the accuracy of the surrogate model and then conducted layout optimizations on a single and a full wind condition case to assess the proposed method. The results indicated that the mean absolute percentage errors of load channels can meet the precision requirements for WFLO. A comparison of layout optimization results between the method proposed in this paper and the traditional method showed that in the single-wind-condition case, the method proposed in this paper reduced the maximum load by 5.64 % compared to the traditional method, with nearly identical power output; in the full-wind-condition case, the reduction of maximum load was 1.70 %, while only sacrificing the annual power generation by 0.10 %. This study provides a load-constrained WFLO method, promising to effectively ensure the lifespan of wind turbines while increasing power generation and offering significant engineering value.

A Nanorod With Near-Perfect Absorption for Efficient Utilization of Solar Thermal Energy

Solar thermal utilization faces a significant challenge, with efficient photothermal conversion being the primary issue. Although plasma nanofluid-based direct absorption solar collectors have shown potential in providing excellent sunlight capture capabilities, their narrow-band absorption limits their efficient photothermal conversion. This study proposes an alternative solution by introducing gold nanorods that achieve nearly perfect absorption across 300–1100 nm. The study demonstrates that the spectral absorption capacity of nanorods remains unaffected by their size and material at specific concentrations and depths. The analysis of the electromagnetic distribution at the resonance absorption peak reveals that the nanorods exhibit a “lightning rod” effect and surface plasmon resonance, leading to significant absorption. Additionally, the study investigates the effect of five common plasma nanomaterials on the nanorods’ optical absorption properties. The results show that titanium nitride-based nanorods exhibit broad-spectrum absorption and require only a small concentration and depth to achieve a 95% solar weighted absorption fraction. This research provides a valuable guide for the efficient photothermal conversion of plasma nanofluids, enhancing their potential for solar thermal systems.

Interfacial engineering for the construction of iron sulfide/boride nanosheets heterostructure bifunctional electrocatalysts for high-efficiency overall water splitting

Iron-based materials have been extensively studied and applied as electrocatalysts. However, the electronic structure of iron itself is not conducive to effectively activating adsorbed intermediates, leading to slower kinetic steps. In this study, a heterogeneous composite catalyst with a nano-sheet array structure consisting of iron boron/sulfide (FeS@Fe2B/IP) was successfully prepared through a two-step process involving boriding and sulfidation of iron plates. The nanosheet array structure provides numerous catalytic active sites, promotes effective contact between electrolytes and catalytic sites, and enhances the apparent reaction rate of the catalyst. Additionally, the heterogeneous interface of FeS/Fe2B plays a crucial role in regulating the electronic structure of the metal Fe site, improving charge transfer rates, and accelerating electrocatalytic reaction kinetics. Ultimately, FeS@Fe2B/IP demonstrates outstanding water splitting performance, with only 177 and 191 mV overpotential providing 10 mA cm−2 toward HER and OER, respectively. Furthermore, it achieves a high current density of 500 mA cm−2 at 473 and 520 mV overpotentials for HER and OER, respectively. In addition to its excellent performance in water splitting applications, a bipolar alkaline cell was constructed using the FeS@Fe2B/IP bifunctional catalyst. This achieved current densities of 10 and 100 mA cm−2 at potentials of 1.63 and 1.75 V. Moreover, the catalytic stability of the FeS@Fe2B/IP system remained consistent for nearly 50 hours at current densities of 10 and 100 mA cm−2. These results confirm that FeS@Fe2B/IP is indeed a high-performance bifunctional electrocatalytic material which can significantly improve energy utilization efficiency in various applications.

Prediction method of adsorption thermal energy storage reactor performances based on reaction wave model

Adsorption thermal energy storage (ATES) is one of the most important ways to achieve efficient utilization of solar energy. The lack of effective prediction methods of reactor performance severely restricts the application of the ATES system. In this paper, the prediction method of reactor performance was proposed based on the adsorption reaction wave model. The expressions between the reactor performances and the design parameters were derived by using the wave parameters of the adsorption rate wave. The mathematical relationships of design parameters-wave parameters and wave parameters-reactor performances were established, respectively. The experiments on adsorption thermal energy storage were performed, in which the zeolite-water vapor was determined as the working pairs. The air temperature and specific humidity at the inlet and outlet of the reactor were measured, and corresponding adsorption amount, thermal energy and stable output power were obtained. A comparison of the predictions for the reactor performances with experiments was carried out. The results indicated that the proposed prediction method was capable of accurately predicting the reactor performances, with a maximum deviation of less than 6.0 %. Moreover, the proposed prediction method is superior to available methods in terms of simplicity and generality.

How does energy transition improve energy utilization efficiency? A case study of China's coal‐to‐gas program

AbstractImproving energy efficiency by adjusting the structure of energy consumption types is of great significance for reducing carbon emissions in the short term. The present paper constructs new data envelopment analysis models for evaluating energy utilization under different structural conditions and calculating potential emissions reductions. We conducted empirical research on 30 provinces in China from 2003 to 2019—a time frame that coincides with the instituting of China's “coal‐to‐gas” program. Our results show that technological progress is the main way for China to reduce carbon emissions and that it is possible to reduce the total amount of carbon emissions by 35%. Additionally, optimizing the energy consumption structure following the coal‐to‐gas program guidelines could reduce the country's carbon emissions by a further 25%. Finally, this paper provides specific policy recommendations based on the efficiency analysis results to guide each province in reducing carbon emissions under the conditions of energy demand growth.

Spatiotemporal trajectory of energy efficiency in the Guangdong-Hong Kong-Macao Greater Bay Area and implications on the route of economic transformation.

The Guangdong-Hong Kong-Macao Greater Bay Area has attracted attention for its extraordinary pace of economic development and is considered to be leading the way in China's transformation from a manufacturing to an innovation cluster. However, due to rapid economic expansion and rapid urbanization, the Great Bay Area still struggles with low energy efficiency and environmental degradation, which has slowed down the pace of development. Therefore, in order to alleviate energy pressure, promote the country's sustainable development and gain a competitive advantage in the global market, researching energy efficiency and improving energy utilization efficiency is crucial. In this study, macro-level energy efficiency indicators are constructed using energy consumption data from various cities in the Greater Bay Area for the period from 2000 to 2020, and the spatio-temporal evolution of energy efficiency is analysed. The results show that all cities in the Greater Bay Area experienced an increasing trend in energy efficiency from 2000 to 2019, with significant variation in growth rates and magnitudes between cities. Compared to the nine cities in Guangdong province, Hong Kong and Macao exhibited significantly superior energy efficiency, with Foshan recording the highest growth rate of 14%. In 2020, most cities experienced a decline in energy efficiency due to the COVID-19 pandemic, with Macao experiencing the greatest decrease at 57%. Hong Kong and Macao are both in the "low consumption and high efficiency" target region, while Guangzhou, Shenzhen and Zhuhai are consistently in the "both high" region. Changes in the industrial upgrading index correspond significantly with changes in energy efficiency trajectories, with the transition from primary to secondary and tertiary industries playing a more substantial role. There is no significant association found between the strength of environmental regulation and changes in energy efficiency. The study's findings indicate that the most effective way to achieve economic transformation in the majority of China's regions is to combine adequate environmental legislation with industrial structural adjustment.

Exploration of new function for thermal energy storage: Temperature stabilizer

Thermal energy storage (TES) is a technology that stores thermal energy by heating or cooling a storage medium so that the stored energy can be used when needed. TES is usually used in greenhouse heating, centralized solar power, and industrial waste heat recovery to improve the efficiency of energy utilization. This article explores a new application of TES, which relies on its constant outlet temperature to provide stable working conditions for other components. A simulation consisting of 10 cycles with a total length of 100 h is designed. Each cycle contains 5 h of discharge and 5 h of charge, achieved by adjusting the inlet temperature. Rock and NaNO3 are selected as materials for sensible and latent heat storage. This paper discusses the temperature distribution and thermal storage capacity of TES under alternating inlet temperature. The results indicate that the outlet temperature of sensible heat storage will fluctuate and the maximum amplitude is 4.56 K. For latent heat storage, the outlet temperature is constant, and fluid temperature and liquid phase ratio distribution do not change with the number of cycles. There is a significant temperature difference between fluid and solid in phase transition stage, with a maximum value of 10.72 K. Mathematical relationship between latent heat storage parameters and the minimum length of TES is obtained.

A Study of Asymmetric Hyperbolic Heat Storage Unit

ABSTRACTDue to the advantages of high energy density and constant temperature, phase change energy storage technology has attracted much attention in energy saving and efficient utilization of energy. In this paper, Fluent software is used to simulate and analyze the heat storage characteristics of six new thermal storage units: corrugated tube, hyperbolic‐shape, non‐hyperbolic‐shape, symmetric frustum‐shape, non‐frustum‐shape, and bow‐shape. The results show that the melting rate of the non‐hyperbolic‐shape phase change unit is significantly higher than that of the circular tube, the heat storage time is shortened by 22.52%, and Ra* reaches 0.0086. Second, the effects of the radius difference (δ) between the inlet and outlet of the non‐hyperbolic‐shape phase change unit and the asymmetric position (s) on the heat storage process are studied. The results show that when δ increases from 2 to 10 mm, the melting time of phase change material (PCM) is shortened by 22.89%, that is, the average heat transfer rate between PCM and heat transfer fluid increases with the increase of δ. On the other hand, the average heat storage rate of the heat storage unit decreases first and then increases with the increase of s. When δ = 10 mm, s = 35 mm is the best working condition, the average heat storage rate of the non‐hyperbolic‐shape thermal storage unit reaches 34.3 J/s. This study can provide new ideas and references for the optimization design of latent heat storage units and the progress of experiments.

Interface-rich porous Fe-doped hcp-PtBi/fcc-Pt heterostructured nanoplates enhanced the C[sbnd]C bond cleavage of C3 alcohols electrooxidation

Efficient CC bond cleavage and the complete oxidation of alcohols are key to improving the efficiency of renewable energy utilization. Herein, we successfully prepare porous Fe-doped hexagonal close-packed (hcp)-PtBi/face-centered cubic (fcc)-Pt heterostructured nanoplates with abundant grain/phase interfaces (h-PtBi/f-Pt@Fe1.7 PNPs) via a simple solvothermal method. The open porous structure, abundant grain/phase interface and stacking fault defects, and the synergistic effect between intermetallic hcp-PtBi and fcc-Pt make h-PtBi/f-Pt@Fe1.7 PNPs an effective electrocatalyst for the glycerol oxidation reaction (GOR) in direct glycerol fuel cells (DGFCs). Notably, the h-PtBi/f-Pt@Fe1.7 PNPs exhibit an excellent mass activity of 7.6 A mgPt−1 for GOR, 4.75-fold higher than that of commercial Pt black in an alkaline medium. Moreover, the h-PtBi/f-Pt@Fe1.7 PNPs achieve higher power density (125.8 mW cm−2) than commercial Pt/C (81.8 mW cm−2) in a single DGFC. The h-PtBi/f-Pt@Fe1.7 PNPs can also effectively catalyze the electrochemical oxidation of 1-propanol (17.1 A mgPt−1), 1,2-propanediol (7.2 A mgPt−1), and 1,3-propanediol (5.2 A mgPt−1). The in-situ Fourier-transform infrared spectra further reveal that the CC bond of glycerol, 1-propanol, 1,2-propanediol, and 1,3-propanediol was dissociated for the complete oxidation by the h-PtBi/f-Pt@Fe1.7 PNPs. This study provides a new class of porous Pt-based heterostructure nanoplates and insight into the intrinsic activity of different C3 alcohols.

Performance analysis and prediction of a modified precompression transcritical CO2 power generation cycle

The efficient utilization of biomass energy and the optimal operation of integrated renewable energy sources in virtual power plants have become critical issues to achieve carbon neutrality targets in rural areas. In the rural areas of Northeast China, a large amount of electricity can be generated from biomass. It is very important to develop biomass power generation equipment for this rural area. Due to the high thermal efficiency and its compact system without freezing process like water, CO2 thermal cycle is an appropriate option for this kind of power generation equipment. According to whether the flow is separated, CO2 cycles can be divided into the single flow layouts and split flow layouts. Compared to the single flow layouts, split flow layouts have higher efficiency, but more complex structures and lower heat transfer power per unit area. Considering the climatic characteristics of Heilongjiang Province and the seasonality of straw biomass utilization, this study proposes a modified scheme for the precompression transcritical CO2 cycle in the single flow layout to meet the simple structure requirement of the small power plants in cold areas like Northeast China. Through energy analysis, the study calculates the thermal efficiencies of the two cycles when the inlet pressure of the main compressor is 5 MPa, the turbine inlet temperature is 550 °C and the pressure ratios of the main compressor are in the range of 4.5 to 5.5. The optimal efficiency of the traditional precompression cycle is 43.55% when the main compressor pressure ratio is 5.5, while the modified precompression cycle is 44.86% when the pressure ratio is 5.0266. The modified precompression cycle has a higher efficiency than the traditional precompression cycle at a lower pressure ratio of the main compressor.

New technology research of efficient utilization of geothermal energy

Abstract Geothermal energy is being widely valued as a clean energy source. Underground structure has a controlling influence on the geothermal development. If the underground structure can be visualized, it is the key technology to improve the geothermal development. Through the streaming potential detection technology, under the action of external pressure, the liquid can be forced to flow through the solid pores to generate directional flow, and the flow potential can be generated at both ends of the solid pores. Different streaming potential phenomena under pressure can help engineer to distinguish different geological structures. A set of laboratory tests was designed to prove the feasibility and accuracy of this proposed detection methods, and tests were conducted using the core injection system and the streaming potential tester to carry out injection on sandstone specimens of two different structures, to study the effect of different injection pressures and different salinity on variation of streaming potential in sandstone. The laboratory test results show that the flow potential is accompanied by the liquid injection process in the sandstone specimen, and the flow potential produced by the sandstone with different porosity is obviously different, so the flow potential accompanying the actual rock injection process can be used to distinguish different geological structures, and this study provides a new idea for studying geological structures, and are expected to improve the efficiency of geothermal development.

Fault diagnosis and intelligent maintenance of industry 4.0 power system based on internet of things technology and thermal energy optimization

The application of Internet of Things technology provides a new opportunity for the fault diagnosis and maintenance of power system. This study aims to explore how to improve the fault diagnosis ability of power system through Internet of Things technology, and realize the efficient utilization of heat energy, so as to achieve the goal of intelligent maintenance. Based on the analysis of current power system operation status, this paper determines the key role of thermal energy management in improving system operation efficiency and reducing energy consumption. It then uses iot sensors and data analytics to monitor heat flow and loss in the power system in real time, identifying potential points of failure through big data. In order to realize intelligent maintenance, this paper designs a fault prediction model based on thermal energy optimization, combined with machine learning algorithm, to further improve the accuracy of fault diagnosis. The experimental results show that the fault diagnosis method combined with the Internet of Things technology can significantly reduce the fault incidence and optimize the efficiency of heat energy use. By applying this model, the overall operating cost of the power system is reduced and the maintenance efficiency is improved.

Polyelectrolyte Additive-Modulated Interfacial Microenvironment Boosting CO2 Electrolysis in Acid.

Acidic CO2 electrolysis offers a promising strategy to achieve high carbon utilization and high energy efficiency. However, challenges remain in suppressing the competitive hydrogen evolution reaction (HER) and improving product selectivity. High concentrations of potassium ions (K+) can suppress HER and accelerate CO2 reduction, but they still inevitably suffer from salt precipitation problems. In this study, we demonstrate that the sulfonate-based polyelectrolyte, polystyrene sulfonate (PSS), enables to reconstruct the electrode-electrolyte interface to significantly enhance the acidic CO2 electrolysis. Mechanistic studies reveal that PSS induces high local K+ concentrations through electrostatic interaction between PSS anions and K+. In situ spectroscopy reveals that PSS reshapes the interfacial hydrogen-bond (H-bond) network, which is attributed to the H-bonds between PSS anions and hydrated proton as well as the steric hindrance of the additive molecules. This greatly weakens proton transfer kinetics and leads to the suppression of undesirable HER. As a result, a Faradaic efficiency of 93.9% for CO can be achieved at 250 mA cm-2, simultaneous with a high single-pass carbon efficiency of 72.2% on commercial Ag catalysts in acid. This study highlights the important role of the electrode-electrolyte interface induced by polyelectrolyte additives in promoting electrocatalytic reactions.

Polyelectrolyte Additive‐Modulated Interfacial Microenvironment Boosting CO2 Electrolysis in Acid

Acidic CO2 electrolysis offers a promising strategy to achieve high carbon utilization and high energy efficiency. However, challenges remain in suppressing the competitive hydrogen evolution reaction (HER) and improving product selectivity. High concentrations of potassium ions (K+) can suppress HER and accelerate CO2 reduction, but they still inevitably suffer from salt precipitation problems. In this study, we demonstrate that the sulfonate‐based polyelectrolyte, polystyrene sulfonate (PSS), enables to reconstruct the electrode‐electrolyte interface to significantly enhance the acidic CO2 electrolysis. Mechanistic studies reveal that PSS induces high local K+ concentrations through electrostatic interaction between PSS anions and K+. In situ spectroscopy reveals that PSS reshapes the interfacial hydrogen–bond (H–bond) network, which is attributed to the H–bonds between PSS anions and hydrated proton as well as the steric hindrance of the additive molecules. This greatly weakens proton transfer kinetics and leads to the suppression of undesirable HER. As a result, a Faradaic efficiency of 93.9% for CO can be achieved at 250 mA cm–2, simultaneous with a high single‐pass carbon efficiency of 72.2% on commercial Ag catalysts in acid. This study highlights the important role of the electrode‐electrolyte interface induced by polyelectrolyte additives in promoting electrocatalytic reactions.

Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design

With the rise of sustainable building design, the traditional design methods are often unable to fully consider the optimal allocation and application of thermal energy resources. This study aims to explore the application of heat resource utilization and sample image restoration technology based on machine vision simulation technology in interior design, and provide practical solutions for optimizing indoor environment. In this paper, machine vision technology is used to monitor and model indoor space in real time, and heat energy flow and distribution under different design schemes are simulated. At the same time, the influence of different designs on thermal energy utilization efficiency is analyzed by using sample image restoration technology. In the study, a set of comprehensive evaluation indexes was established, which comprehensively considered the factors of heat efficiency, indoor comfort and energy consumption. The experimental results show that the simulation technology based on machine vision can accurately predict the indoor heat distribution and identify the best design scheme. At the same time, the sample image restoration technology significantly improves the evaluation accuracy of heat utilization efficiency. Through these methods, the optimized design scheme has higher thermal energy utilization than the traditional design, and significantly improves the indoor comfort. This study shows that the combination of machine vision simulation and sample image restoration technology can effectively improve the utilization efficiency of thermal energy resources in interior design, and provide new ideas and methods for sustainable building design.

Energy Efficiency and Greenhouse Gas Emissions: A Panel ARDL Model of Top Five Emitters in Africa

The Paris Agreement has mandated drastic decarbonization by developed and developing economies. Many of the top African emitters are now exploring drastic restructuring of the production processes in a way that promotes energy utilization efficiency. This is critical for ensuring an environmentally benign growth process. This study presents a unique addition to the body of existing knowledge by analysing the effect of some indicators of energy efficiency on three indicators of environmental quality among the top five emitters of GHG in Africa. The data were from the World Development Indicators (WDI), covering the 1990-2020 periods for South Africa, Egypt, Nigeria, Algeria, and Libya. The panel Autoregressive Distributed Lag (ARDL) model was used for data analysis. The results showed that all the included series were stationary at first difference. The optimum lag lengths were determined independently in STATA 17 software, and the most frequent lags across the countries were selected. The Hausman test revealed that the pooled mean group (PMG) estimation was preferred to other estimators. The results showed that in the long-run, particulate emission damages, CO2 intensity, and energy use intensity positively impacted GHG emissions, while GDP per capita and renewable energy utilization produced mixed results. In the short run, the error correction mechanism parameters were all significant (p<0.01) in Libya for all emission indicators, while Algeria showed significance (p<0.01) in total GHG and CO2 per capita models. The coefficient of particulate emission damages significantly reduced (p<0.01) total GHG emissions in Libya and Nigeria and renewable energy significantly increased (p<0.01) GHG emissions in South Africa but reduced them in Nigeria. Also, CO2 emission intensity significantly (p<0.01) and positively impacted GHG emissions in South Africa, Egypt, Libya, and Nigeria, while electricity power transmission losses increased total emissions and CO2 emissions in Libya and Algeria. It was concluded that initiatives to reduce GHG emissions should focus on developing countries’ sensitivity to emission damages, and promotion of initiatives for using renewable energies. Also, promotion of energy efficiency across industries offers a prospect for ensuring environmental sustainability.

Are hybrid approaches combining EKF-UKF and artificial neural networks key to unlocking the full potential of sustainable energy technologies and reducing environmental footprint?

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed. The results indicate a substantial 8.02 % reduction in estimation error compared to standalone EKF and UKF methods, highlighting the enhanced predictive capabilities of the hybrid approach. Moreover, the integration of ANNs facilitated a 12.52 % increase in renewable energy utilization efficiency, leading to a notable 5.14 % decrease in carbon emissions. These compelling outcomes underscore the critical role of hybrid approaches in maximizing the efficiency of sustainable energy technologies while simultaneously reducing environmental footprint. By harnessing the synergies between traditional filtering techniques and machine learning algorithms, hybrid EKF-UKF with ANNs emerges as a key enabler in accelerating the transition towards a more sustainable and resilient energy landscape.