Utilization Of Energy Resources Research Articles

Deep understanding the formation of MnCoOx in-situ grown on foam nickel towards efficient lean methane catalytic oxidation

Methane is the second largest greenhouse gas in the world, with a stronger greenhouse effect than carbon dioxide. The efficient elimination of methane in the atmosphere has important engineering significance in slowing down global temperature rise, energy resource utilization, collaborative control of pollutants, and promoting technological innovation. In this work, a general hydrothermal synthesis method was introduced to in-situ construct four different morphologies of MnCoOx (including rod-, spherical-, filamentous- and bulk-shape) on foam nickel (NF). The synthesis of MnCoOx/NF with different morphologies was due to changes in NF pretreatment conditions and precipitants, which affected the nucleation and growth rates of MnCoOx, ultimately resulting in different morphologies. When the four different morphologies of MnCoOx/NF were used as catalysts for lean methane catalytic oxidation, MnCoOx/NF-r exhibited the best excellent catalytic activity, and the T90 occurred at 428 °C in space velocity of 48,000 mL g−1 h−1. Such an outstanding performance was attributed to the unique properties of the MnCoOx/NF-r including the larger surface area and more oxygen vacancies for providing more active sites and a higher oxygen migration rate, which was very beneficial for improving the catalytic oxidation performance of methane.

A path to carbon neutrality in construction: An overview of recent progress in recycled cement usage

The cement industries as binder materials, produces 1.6 billion metric tons’ carbon dioxide (CO2) in 2022 and cause depletion in ozone layer. The cement is being used in modern construction and cause the environmental impact. Therefore, there is a quest for a sustainable alternative binder to reduce the CO2 emission from the conventional cement production and their effective disposal methods in construction and demolished (C&D) waste is paramount. This article delves into the production of recycled cement by leveraging various methods such as mechanical activation, chemical activation, and heat treatment of C&D waste, ultimately contributing to the carbon neutrality. A comparison with traditional cement production sheds light on energy consumption, CO2 emissions, and resource utilization. The recycled cement displays increased water demand, accelerated early setting, extended final setting time, and reduced compressive strength. However, when used solely as a cementitious matrix, recycled cement can readily achieve up to 30 MPa in 28 days. Utilising recycled cement in concrete applications demonstrates its efficacy in the construction industry. The insights provided herein offer a holistic understanding of the production processes, properties, and applications of recycled cement, reinforcing its potential as a viable and environmentally friendly alternative in the realm of construction materials.

Robust optimization of transactive flexibility oriented scheduling of joint electricity and gas supplies

The power system with flexibility provision will lead to the expansion of the utilization of renewable energy resources. Providing flexibility is one of the basic challenges for power system operators and developers, which is addressed in this study. Moreover, the security of the gas supply system is important for power system operators, because the operation of gas-fired units depends significantly on the gas supply. In this study, the availability of pipelines has been considered as a gas supply uncertainty. Then, an optimal uncertainty set is proposed for security constraint unit commitment (SCUC) which is calculated for managing the uncertainty of wind and gas supply in different scenarios of risk. Robust-based optimization has been implemented to obtain the optimal uncertainty set in which both feasibility and economic optimal robustness have been guaranteed. A new robust flexibility-based SCUC model has been applied using a demand response program (RFSCUCDRP). The transactive flexibility between flexible providers, such as thermal units and DR, and uncertainty producers, such as wind resources, has been determined in the proposed model. The problem has been implemented on the 6-bus and IEEE-118-bus standard test systems with different risk percentages. The operation costs of the 6-bus system with and without robust optimization are 89,094 $ and 119,475 $, respectively. It is concluded that robust optimization is ultimately economically better.

Energy, Society and Sociodemographic Constraints Nexus: Development and Future Prospects

To overcome the challenges faced by the energy sector, policymakers need to prioritize the development of strategic policies that promote the adoption of waste-to-energy technologies, Tidal energy, solar power systems, and wind farms. These policies should emphasize the integration of renewable energy sources into the existing energy infrastructure, ensuring a more sustainable and resilient energy supply for urban areas. The study underscores the importance of collaboration between various stakeholders, including government bodies, private sector entities, and local communities. By fostering partnerships and creating incentives, it becomes possible to mobilize resources and expertise towards the implementation of renewable energy projects. This collaboration would not only address the energy crisis but also contribute to job creation and overall socioeconomic development. Proper utilization of renewable energy resources can resolve the dilemma of energy crises in the country. According to our results, solar stood first in the overall ranking followed by Tidal, waste-to-energy, and wind energies. Solar performed better in indigenous manufacturing and development, resource potential, and technological development. Pakistan has abundant green energy resources which could be utilized by the formation of a proper policy roadmap and mitigation of policy pathways to overcome the hazardous dependence on fossil fuels for energy requirements. The implementation of policies adopted should be assured to achieve energy sustainability

“Clustering Protocol Based on Territorial Energy for Random Deployment of Wireless Sensor Networks"

Wireless Sensor Networks (WSNs) have become increasingly essential for monitoring and gathering data in diverse environments. The efficient utilization of energy resources is crucial for prolonging the network lifetime in WSNs. This paper presents a comparative analysis of three prominent clustering protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Residual Energy Aware Clustering (REAC), and Territorial Energy-Driven Clustering Protocol (TEDC). TEDC introduces a novel approach where cluster heads are elected based on territorial dominance and energy levels, aiming to enhance energy efficiency and prolong network lifetime. The performance of LEACH, REAC, and TEDC is evaluated under various scenarios, including node density, network size, and communication range. Key performance metrics such as energy consumption, network lifetime, and packet delivery ratio are analyzed to assess the effectiveness of each protocol. The results provide valuable insights into the comparative strengths and weaknesses of these protocols, aiding network designers and researchers in selecting the most suitable protocol for specific WSN deployments. Additionally, the paper discusses potential optimizations and future research directions to further enhance the performance of clustering protocols in WSNs.

China's future wind energy considering air density during climate change

To assess the impact of air density on future wind resources under representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), the Coordinated Regional Downscaling Experiment-East Asia project was utilized to simulate the future air density characteristics of China (2021–2100). Empirical air density distributions were calculated, and fourteen probability density functions were fitted to the empirical air density distributions, determining the optimal fitted distribution functions. In this study, the effect of air density on the annual energy production and wind power density was quantified. The results indicated that only approximately 4 % of regional air density values reaches the standard air density under the three climate scenarios. The seven parameter Generalized Extreme Value-Generalized Extreme Value distribution, six parameter Gamma-Generalized Extreme Value distribution, and six parameter Exponentiated Weibull- Generalized Extreme Value distribution matched the empirical distributions well under RCP2.6, RCP4.5, and RCP8.5, respectively. Annual energy production and wind power density decreased by more than 15 % after considering air density. Under the different RCP scenarios (RCP2.6, RCP4.5, and RCP8.5), the errors of annual energy production and wind power density gradually escalate. In the Tibetan Plateau region, accounting for air density results in an overestimation of annual energy production by over 40 % and wind power density by more than 35 %. The findings of this study offer guidance for future wind energy resource planning, enabling optimal utilization of wind energy resources.

Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN

Accurate wind speed forecasting is of great significance for the utilization of Arctic wind energy resources. The traditional single model is difficult to fully depict the nonlinearity of wind speed and its wide range of variations. In this paper, a hybrid model is proposed for multi-step wind speed forecasting, which combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), convolutional neural network (CNN) and long-short term memory neural network (LSTM). The wind speed series is firstly decomposed into several intrinsic mode functions (IMF) by CEEMDAN to provide more stable data. Secondly, four-step-ahead forecasts are realized using well-tuned CNN-LSTM model for each IMF. Finally, the forecasted wind speed is obtained by reconstruction. The effectiveness and feasibility of the proposed method is validated based on thorough evaluation and step-by-step analysis. The RMSE of the proposed model is 0.4046 m/s, which are reduced by 58 % compared with 7 benchmark models. Furthermore, the average prediction interval of the proposed model is also reduced by 20 %, 16 % and 7 % compared to CEEMDAN-FCNN, CEEMDAN-CNN and CEEMDAN-LSTM respectively. The results prove that all three parts of the proposed model contribute to a better performance in wind speed forecasting.

Wind power forecasting technologies: A review

This study addresses the critical role of wind power forecasting in ensuring stable and reliable power system operations. Wind Power Forecasting is critical for the efficient operation of plant, time scheduling, and it’s balancing of power generation with grid integration systems. Due to its dependency on dynamic climatic conditions and associated factors, accurate wind power forecasting is challenging. The research delves into various aspects, including input data, input selection techniques, data pre-processing, and forecasting methods, with the aim of motivating researchers to design highly efficient online/offline models on weather-based data. The overarching goal is to enhance the reliability and stability of power systems while optimizing energy resource utilization. The analysis reveals that hybrid models offer more accurate results, highlighting their significance in the current era. This study investigates different Wind Power Forecasting (WPF) models from existing literature, focusing on input variables, time horizons, climatic conditions, pre-processing techniques, and sample sizes that affect model accuracy. It covers statistical models like ARMA and ARIMA, along with AI techniques including Deep Learning (DL), Machine Learning (ML), and neural networks, to estimate wind power.

Enhancing online health consultations through fuzzy logic-integrated attribute-based encryption system

Online health consultations are becoming more popular as a result of technological improvements. Patients routinely look for information about medical disorders online, which could jeopardize the privacy of medical records and increase the workload of healthcare professionals. Nonetheless, academics continue to be extremely concerned about issues related to the quality characteristics that relate to the current architectural models, such as energy consumption, latency, resource utilization, scalability, and packet loss. This method, however, also results in a significant strain being placed on medical experts who must sort through vast amounts of medical records to extract certain information. This paper presents a novel ciphertext policy attribute-based encryption method coupled with fuzzy logic to overcome these issues. This solution uses a hybrid structure of IPFS and blockchain to store data and enables complex bidirectional access control. Before being added to IPFS, medical records are encrypted. To ensure data integrity, related IPFS hash indexes are then added to the blockchain. Utilizing attribute-based technology, users’ data is encrypted to give them fine-grained bidirectional access control. A thorough security analysis proves the system’s resilience, especially when faced with chosen plaintext assaults inside the random oracle model. Tests for this study were conducted using 10–50 attribute sets. This paper’s technique solely makes use of a hash operation. All things considered; the study demonstrates that the proposed design is more efficient than earlier schemes. Thus, from the comparison study above, it can be concluded that the system presented in this work is more efficient. Results from simulations provide additional support for the suggested methodology by highlighting the improved computing efficiency of users as compared to baseline conventional systems. This study demonstrates how technological advancement and healthcare requirements can coexist harmoniously, paving the way for secure and effective online medical consultations that are powered by fuzzy logic.

From resource curse to green growth: Exploring the role of energy utilization and natural resource abundance in economic development

AbstractThis study delves into the profound repercussions of the resource curse hypothesis within the Brazil, Russia, India, China, and South Africa (BRICS) nations from 1991 to 2022, examining the intricate interplay among natural resource abundance, energy consumption, and economic development (ED). Methodologically, it employs the cross‐sectionally augmented Dickey–Fuller test to assess stationarity and utilizes the Westerlund cointegration technique to analyze cointegration. Subsequently, the cross‐sectionally augmented autoregressive distributive lag model is deployed to explore the impact of natural resource availability, renewable and non‐renewable energy utilization, and carbon emissions on ED within these countries. The findings reveal a stark reality wherein both carbon emissions and non‐renewable energy consumption wield a consistently positive influence on short‐ and long‐term economic growth across the BRICS economies. Particularly striking is the dominant impact of non‐renewable energy consumption. However, this comes in stark contrast to the adverse effects identified with excessive resource and coal rents, signifying potential economic setbacks arising from rampant natural resource exploitation. Furthermore, the suboptimal utilization of renewable energy resources hints at a detrimental effect on ED. These results transcend the confines of developing nations, underscoring the universality of the resource curse hypothesis, affecting both developing and developed countries. The study illuminates the grave risks inherent in overreliance and overexploitation of natural resources, elucidating heightened competition that severely impedes the ED trajectory of the BRICS countries in both short and long terms. Policymakers must prioritize economic diversification, implement sustainable resource management, and invest in innovative technologies to mitigate the resource curse in BRICS nations, fostering resilience and sustainable economic growth. In conclusion, This study highlights the severe impact of the resource curse in BRICS nations, stressing the imperative for adept resource management to counter the risks linked with overdependence on non‐renewable resources and bolster sustainable economic growth.

Long-term solar radiation forecasting based on LSTM and attention mechanism: a case study in Algeria

The growing adoption of photovoltaic (PV) energy in urban areas underscores its capability to meet energy demands effectively. The accurate forecasting of meteorological parameters, particularly global solar radiation, is paramount for the efficient management and utilization of solar energy resources. Solar radiation forecasting methods typically fall into two main categories: cloud imagery combined with physical approaches, and Artificial Intelligence (AI) based methods. Due to the non-stationary nature of solar radiation and the high nonlinearity of atmospheric conditions, conventional forecasting approaches often exhibit poor accuracy. Artificial intelligence based approaches, such as machine learning and deep learning algorithms, are extensively employed in global solar radiation forecasting research, demonstrating impressive accuracy. In this respect, this paper presents a novel approach for long term monthly forecasting of global solar radiation using a Bidirectional Long Short-Term Memory (LSTM) architecture augmented with an attention mechanism that includes a Squeeze and Excitation (SE) block (SE-BiLSTM). The effectiveness of this model is extensively evaluated for long-term monthly solar radiation forecasting using meteorological data collected over a 20-year period (from Jan 2001 to Dec 2020) from the National Aeronautics and Space Administration (NASA). The proposed SE-BiLSTM model is compared with well-established forecasting models including Naïve, Autoregressive Moving Average (ARMA), Multi-layer Perceptron (MLP), LSTM, and Bidirectional LSTM. Through rigorous simulation tests, our model demonstrates superior performance, achieving the lowest mean absolute percentage error (MAPE) of 4.52% and mean absolute error (MAE) of 7.89 kW/m2. This advancement holds significant promise for enhancing solar energy forecasting accuracy and its practical application in renewable energy systems.

Exploring the Influence of Dynamic Indicators in Urban Spaces on Residents’ Environmental Behavior: A Case Study in Shanghai Utilizing Mixed-Methods Approach and Artificial Neural Network (ANN) Modeling

The main aim of this article is to evaluate the impact of dynamic indicators associated with urban spaces on the environmental behavior of residents in Shanghai, China. With the city experiencing rapid urbanization and increasing environmental concerns, it is crucial to understand how the design and management of urban spaces can encourage pro-environmental attitudes and actions among the population. The study specifically focuses on dynamic indicators, namely vitality, accessibility, quality, and walkability, to develop a comprehensive understanding of the utilization and experience of urban spaces. The research outcomes will make valuable contributions towards Shanghai’s objectives of achieving environmental sustainability, while also providing insights that have broader relevance to sustainable urban development globally. As the built environment significantly influences energy consumption, resource utilization, pollution generation, and overall human wellbeing, attaining urban sustainability is of paramount importance. The findings of this study will aid in informing strategies and policies that promote sustainable practices, not only benefiting Shanghai but also serving as a valuable resource for urban development initiatives worldwide. Sustainable urban design principles, including compactness, density, mixed land use, greening, and walkability, have been associated with pro-environmental behaviors, including reduced reliance on automobiles, increased walking and cycling, and heightened environmental consciousness. Nevertheless, the relationship between the built environment and sustainability behaviors is intricate and is influenced by multiple factors. Consequently, further research is necessary to comprehend how specific spatial and temporal dynamics impact environmental behaviors within urban settings. In this study, an artificial neural network (ANN) was developed to estimate the quality and walkability of an area and environmental behaviors by considering the augmented vitality and accessibility factors. The ANN’s predictions demonstrate that higher levels of vitality and accessibility positively contribute to improved walkability and environmental behaviors. The accuracy of the ANN’s predictions was assessed using linear regression, which yielded acceptable error rates when compared with experimental results.

Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran

This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model’s performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model’s performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.

Climate change and crop production nexus: assessing the role of technological development for sustainable agriculture in Vietnam

PurposeThis study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.Design/methodology/approachSeveral econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.FindingsThe results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.Research limitations/implicationsThis study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.Originality/valueTo the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

Interactive demand response and dynamic thermal line rating for minimizing the wind power spillage and carbon emissions

Spilling has already occurred as a result of rising the penetration of intermittent renewable generation, and it is anticipated that the level of renewable energy curtailment will continue to soar. This leads to an increase in operating costs, CO2 emissions, and not good utilization of renewable energy resources. A bi-level multi-objective optimization model is proposed in this paper to reduce wind power spillage (WPS) based on demand response (DR) and dynamic thermal line rating (DTLR). In the upper level, multiple objectives will be satisfied based on the optimal allocation and time of DR programs considering DTLR obtained in the lower level. The minimization of WPS, load shedding, power losses, and CO2 emissions are the objectives of this level. While the lower-level aims to maximize social welfare under different scenarios and overall system constraints. Under the uncertainty of the wind power and load demand, a collection of lower-level problems that represent the market clearing conditions is used to constrain the upper-level. The effectiveness of the proposed algorithm is examined on a modified two-area IEEE 24-bus test system. Results depict that the suggested bi-level model enables considerable reductions in the WPS by up to 32.7 %. Also, there is an enhancement in load shedding, power losses, and CO2 emissions by 28.93 %, 23.07 %, and 13.9 % respectively. Finally, the social welfare increased by up to 36.6 %.

Transforming Turkish electricity system in the context of circular economy and green deal: impacts on steel and agricultural production

ABSTRACT Two important but conflicting goals in Turkey’s energy policy have been ensuring energy security through increased indigenous energy resource utilization and meeting the 2053 net zero emission commitment. Based on this, this work explores emission mitigation pathways for Turkey’s electricity system through circularity approaches of CO2 utilization and framework material recycling (FMR). Using mathematical models, eight life cycle impact potentials are evaluated including global warming potential (GWP). The extended impact on steel and agricultural production was also examined in the Green Deal context. The circularity approaches investigated showed that the GWP of Turkey’s wind, solar, and lignite energy sources reduce from the base values of 7.3 gCO2eq./kWh, 29.5 gCO2eq./kWh and 1130 gCO2eq./kWh to 2.72 gCO2eq./kWh, 21.08 gCO2eq./kWh and 241.26 gCO2eq./kWh, respectively. Fifty percent recycling ratio is also determined as the optimum for CO2 utilization and FMR. With this ratio, 21.84% CO2 emission reduction corresponding to 0.083 GT annual CO2 savings is achieved in the Turkish electricity mix. The decarbonization of electricity results in 25.0% and 27.0% GWP impact reductions in the agricultural and steel sectors, respectively. Hence, the decarbonization of electricity mix can significantly ease the negative impacts of the Green Deal on Turkey’s economy. Additionally, promoting and increasing the share of renewable energy sources in the electricity mix can further enhance these environmental benefits.

A silicon photoanode protected with TiO2/stainless steel bilayer stack for solar seawater splitting

Photoelectrochemical seawater splitting is a promising route for direct utilization of solar energy and abundant seawater resources for H2 production. However, the complex salinity composition in seawater results in intractable challenges for photoelectrodes. This paper describes the fabrication of a bilayer stack consisting of stainless steel and TiO2 as a cocatalyst and protective layer for Si photoanode. The chromium-incorporated NiFe (oxy)hydroxide converted from stainless steel film serves as a protective cocatalyst for efficient oxygen evolution and retarding the adsorption of corrosive ions from seawater, while the TiO2 is capable of avoiding the plasma damage of the surface layer of Si photoanode during the sputtering of stainless steel catalysts. By implementing this approach, the TiO2 layer effectively shields the vulnerable semiconductor photoelectrode from the harsh plasma sputtering conditions in stainless steel coating, preventing surface damages. Finally, the Si photoanode with the bilayer stack inhibits the adsorption of chloride and realizes 167 h stability in chloride-containing alkaline electrolytes. Furthermore, this photoanode also demonstrates stable performance under alkaline natural seawater for over 50 h with an applied bias photon-to-current efficiency of 2.62%.

Modeling the Nexus between European Carbon Emission Trading and Financial Market Returns: Practical Implications for Carbon Risk Reduction and Hedging

The carbon–financial nexus helps firms evaluate susceptibility to carbon risk more effectively. This is the first research article to model the short- and long-run co-integrating association between European financial markets, the CBOE oil price volatility index (OVZ) and the European carbon emission trading system (EU-ETS) by using the daily returns from 1 October 2013 to 1 October 2023. We utilize co-integration test followed by the ARDL framework with an error correction mechanism (ECM). Moreover, we utilize the DCC-GARCH-t copula framework to estimate the hedge ratio and to select an optimal portfolio weight for carbon risk hedging. Overall, the findings suggested that EU-ETS (OVZ) has a consistent positive (negative) short-term influence on all the equity returns of Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Spain and the stock indices of the whole Eurozone. However, in the long term, EU-ETS has a positive (negative) effect on the stock returns of France and the Eurozone (Belgium and Spain). Belgian and Spanish companies could implement long-term carbon reduction policies. Belgian and Spanish firms should focus on the utilization of green energy resources and the internalization of carbon emission-free mechanical processes as this may offer a safeguard against the additional pressure arising from escalating carbon prices. Finally, an optimal portfolio weight selection strategy based upon the DCC-GARCH-t copula approach aims for higher hedging effectiveness (HE) than the hedge ratio strategy when adopting short-term positions in Italian and Danish equity markets to reduce the risk of long-term EU-ETS volatility.

A sustainable and trusted solution for IoT-based federated learning using feedback behavior

Federated learning is defined as one of the solutions that use distributed clients to train and aggregate the target model without sharing the private information of clients in the network. Hence, federated learning may easily handle and tackle the generated information in a more convenient, feasible and secure manner, while involving the intelligent devices to perform testing and training at the local edges. However, the design of such federated learning solutions faces multiple difficulties due to the cost and complexity of information computation, along with a high energy consumption. Furthermore, the identification of resource-constrained devices which ensures significant power consumption along with privacy and security to the devices makes it critical to attain a sustainable federated learning environment. The aim of this paper is to integrate feedback-based and objective trust models to accurately identify the behavior of communicating devices in the network. The secure transmission of information forwarded by trusted IoT devices is computed by the objective model. In addition, the energy consumption required for measuring the authenticity of a device can be further diminished using a behavior and feedback trusted model. In addition, the past history behavior and feedback received from neighboring devices may also contribute to achieving an efficient and sustainable security mechanism with reduced communication steps and computational delay in the network. The proposed solution is evaluated to analyze the feasibility and performance in terms of energy consumption and resource utilization, while also considering various security metrics for comparison against existing methods. The proposed mechanism out-performed 87% improvement in terms of security metrics in comparison of existing approaches.

Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy

Accurate wind power forecasting helps to maximize the utilization of wind energy resources, enhance wind power generation efficiency, and optimize grid operation. This study proposes an innovative mixed-frequency modeling and interpretable base model selection-based ensemble wind power forecasting system. Specifically, the data preprocessing module preprocesses wind speed and wind power data at different frequencies. The mixed-frequency modeling module then constructs 12 mixed-frequency and machine learning models to predict wind power, with a comprehensive evaluation metric to determine their optimal lags. Subsequently, the base model selection module effectively combines the elastic net and Shapley additive explanation methods to identify individual models that contribute significantly to the prediction target as base models. Finally, the ensemble module integrates the optimization algorithms with a machine learning model to ensemble the selected base models. The key findings are as follows: (1) mixed-frequency wind speed and wind power data effectively improve forecasting performance, and (2) the proposed base model selection strategy greatly enhances the accuracy and interpretability of the modeling process. This model could robustly predict two datasets from Inner Mongolian wind farms, with average absolute percentage errors of 2.4505% and 4.8270%, respectively, establishing this as a useful technique for wind power prediction.