Future Demand Research Articles

Research on urban water demand prediction based on machine learning and feature engineering

ABSTRACT Urban water demand prediction is not only the foundation of water resource planning and management, but also an important component of water supply system optimization and scheduling. Therefore, predicting future water demand is of great significance. For univariate time series data, the issue of outliers can be solved through data preprocessing. Then, the data input dimension is increased through feature engineering, and finally, the LightGBM (Light Gradient Boosting Machine) model is used to predict future water demand. The results demonstrate that cubic polynomial interpolation outperforms the Prophet model and the linear method in the context of missing value interpolation tasks. In terms of predicting water demand, the LightGBM model demonstrates excellent forecasting performance and can effectively predict future water demand trends. The evaluation indicators MAPE (mean absolute percentage error) and NSE (Nash–Sutcliffe efficiency coefficient) on the test dataset are 4.28% and 0.94, respectively. These indicators can provide a scientific basis for short-term prediction of water supply enterprises.

Application of nanomaterials for improving zinc-ion batteries performance

Abstract Extensive endeavours are underway to devise global strategies for energy storage, aiming to fulfil the burgeoning energy demands in the foreseeable future. While lead-acid batteries (LABs) and lithium-ion batteries (LIBs) are currently two common and widely used types of batteries, they are accompanied by environmental implications and safety apprehensions. In this context, aqueous zinc-ion batteries (AZIBs) have emerged as an up-and-coming solution, exhibiting an economically viable composition and leveraging nanomaterials to enhance electrochemical performance and facilitate efficient energy storage. This passage delves into recent studies about incorporating nanoparticles in rechargeable aqueous AZIBs and evaluates their performance characteristics. A comprehensive analysis of eight studies has been conducted, scrutinizing various aspects such as electrochemical performance, stability, specific capacity, and charging rate. By juxtaposing these studies, noteworthy observations have been made regarding the impact of nanoparticles, along with insights into potential advancements for the AZIBs in the future.

Factors influencing travelers' intention by environmental changes of destination: Cross‐country evidence from Far‐East Asia

AbstractTravel intentions are influenced by not only personal background but also external factors such as national disasters, infectious diseases, and government policy. While international flights and the travel market are sharply recovering from the effects of the COVID‐19 pandemic, travel risk will affect future travel demand. This study analyzed the relationship between potential risk of destination and travel avoidance intention using the stimulus‐organism‐response (SOR) model. The survey was conducted with 303 Chinese and 253 South Korean respondents living in countries adjacent to Japan. The results reveal that travel avoidance intentions may be influenced by emotional reactions to the complexity of the geopolitical environment or the diverse protection motivation perceptions of travelers. This study proposes that nationality has a moderate effect on tentative travel avoidance intention compared to gender.

Disordered carbon structures enhance capacitive storage

Electrochemical energy storage (EES) is one of the key technologies in global research, focusing on the efficient storage and utilization of electrical energy generated from intermittent sources. The development of EES systems with high energy and power density is essential for meeting future energy demands electrochemical capacitors, such as capacitors, are capable of storing electrical energy obtained from intermittent sources and enabling the rapid energy transfer and transformation. Among these, electrical double-layer capacitances (EDLCs) within porous carbon materials (Fig. 1a), are commercially popular due to their excellent conductivity and relatively low cost. Despite their advantages, the complex structure of nanoporous carbon materials challenges in optimizing supercapacitor performance. While previous research has suggested that adjusting the pore size of nanoporous in carbon materials can enhance capacitive performance [1], conflicting reports and the lack of a definitive correlation between capacitance and pore size remain issues [2]. Understanding the relationship between the structure of carbon materials and their capacitance is crucial for the design of devices with high energy density effectively.

Nanotechnology approaches for Enhancement in Biohydrogen Production

The rapid surge of renewable energy sources has been influenced by the high rate of energy consumption and the low sustainability of traditional energy sources. Being an excellent energy source, hydrogen does not leave any negative carbon footprint as it only produces water during the combustion process. It is carbon neutral which can be produced from a variety of waste feed stocks or biomass, making it the most efficient and environmentally friendly form of energy amid all biofuels. To meet the future hydrogen demand, biological processes like bacterial fermentation, are considered to be environmentally favourable option. Since biomass is abundant, cheap, and biodegradable it is considered profitable for biohydrogen production. Though photo-biological and dark fermentation methods are regarded as successful in generating biohydrogen, their lower yields pose significant challenges for its commercial production. Studies are being conducted to improve efficiency, and here is where nanomaterials come into play by influencing biological processes at the cellular level. They can act as catalysts speeding up the reactions that create hydrogen and making the process more sustainable. Owing to their distinct properties such as stability, crystalline nature, high ratio of surface to volume, adsorption ability, and increased electroconductivity significantly enhance hydrogen generation. In this paper, the applications of nanomaterials such as metals, metal alloys, metal oxides, nanocomposites, and inorganic nanoparticles to improve biohydrogen production have been studied.

Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks

AbstractEstimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per‐capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm‐season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.

Strategy-based optimization and life cycle environmental impact assessment of a stable photovoltaic power-to-hydrogen system

With the growing global emphasis on reducing greenhouse gas emissions and transitioning to a low-carbon economy, renewable hydrogen is expected to play a vital role in the chemical industry. However, there is a contradiction between the continuous and stable hydrogen demand of chemical process systems and the intermittent and fluctuating nature of renewable energy systems when coupling them together in the industrial chemical production. There are currently few studies addressing year-round stable hydrogen load systems while considering renewable energy intermittency and capacity optimization. Moreover, the comprehensive life cycle environmental impact study of such stable renewable-hydrogen production system is extremely limited. Therefore, this study focuses on a photovoltaic power-to-hydrogen system that can operate stably for 8,760 h per year, specifically targeting its application in the chemical industry. Two operating strategies of the hydrogen system were considered: fully off-grid and ultra-light grid-connected scenarios. The capacity configuration under each scenario was optimized using gird search algorithm, with the objective of minimizing the levelized cost of hydrogen. Based on the optimization results, the energy flow network, cost-effectiveness, and comprehensive life cycle environmental impacts of such system were analyzed. The results show that the ultra-light grid-connected hydrogen production system, due to the support of the power grid, has improved the reliability of the hydrogen production system and significantly reduced the capacity configuration of system components, thereby demonstrating better performance in terms of economics and overall lifecycle environmental impact compared to a completely off-grid hydrogen production system. Its global warming potential (GWP) and levelized cost of hydrogen under electricity sales scenario are 12% and 31% lower, respectively, compared to fully off-grid hydrogen production. Considering China’s future demand for large-scale renewable hydrogen, ultra-light grid-connected hydrogen production may become the primary transitional pathway in the near to medium term.

Phase change material application in solar cooking for performance enhancement through storage of thermal energy: A future demand

Phase change material application in solar cooking for performance enhancement through storage of thermal energy: A future demand

MODELLING DOMESTIC WATER DEMAND IN MALAYSIA TO IDENTIFY INFLUENCING FACTORS: A COMPARATIVE ANALYSIS

Water crises are often experienced by many developing countries worldwide. Predicting future domestic water demand and identifying the influential factors are vital to managing water supply effectively. This study aims to determine the best predictive models among Multiple Linear Regression (MLR), Multi-layer Perceptron (MLP), and Radial Basis Function (RBF) Neural Networks as well as to identify the significant influential factors towards domestic water demand. Based on the yearly records from 2000 to 2018 obtained from the Malaysian Water Association, the Department of Environment, and the Department of Statistics Malaysia the analysis results indicate an increasing pattern of domestic water in Malaysia with the demand for non-domestic water twice lower than domestic water. Based on RMSE and R-squared, Multi-layer Perceptron is the best model for predicting domestic water demand. The MLR model shows that the two most significant influential factors towards domestic water demand are price and design capacity, with negative and positive relationships. The results describe that an increase in price affects a decrease in water demand, while an increase in design capacity will reduce the water demand. The findings suggest that the water utilities in Malaysia should focus more on these identified factors.

Fostering Collaboration and Team Effectiveness in Educational Leadership: Strategies for Building High-Performing Teams and Networks

This study examines transformative leadership within Philippine educational settings to discern its impact on staff development, and institutional effectiveness. Utilizing a descriptive correlational design, the analysis involved diverse stakeholders and revealed that transformative leadership diverges significantly from traditional management, emphasizing innovation, empathy, and adaptability. It fosters a culture where staff confidence and autonomy lead to professional growth and improved educational outcomes. Additionally, the findings suggest that such leadership is essential for educational institutions navigating the complexities of modern education, including technological advancements and societal changes. Effective communication and systematic evaluation were identified as key for transformative leaders to measure and enhance student success despite facing resistance to change. The study concludes that transformative leadership will become increasingly central in education, driving institutions towards inclusivity and a forward-thinking ethos. It is recommended that Philippine educational institutions adopt transformative leadership to bridge disparities, enhance quality, and prepare for future educational demands. This approach is particularly beneficial for public schools, higher education, TVET institutions, private educational providers, policymakers, and community learning centers, promising to elevate the quality of education in a rapidly evolving global landscape

The crucial role of renewable energy in achieving the sustainable development goals for cleaner energy

The modern world is rapidly transforming into an interconnected global community, driven by escalating energy demands. Despite this evolution, Earth's resources remain finite. The growing need for energy, coupled with the imperative to address human development, well-being, and health, necessitates a robust response to climate change. A decisive shift towards renewable energy sources is crucial and must be sustainable to meet future energy demands. This study explores opportunities presented by renewable energy sources, focusing on solar, wind, geothermal, and hydrogen energy, alongside net-zero sustainability through carbon and methane capture. These sources enhance energy security, improve energy access, promote social and economic development, mitigate climate change, and reduce environmental and health impacts. However, challenges hinder the sustainable adoption of renewable energy, including market failures, limited access to information, the need for essential raw materials, and rising carbon emissions. To address these, the paper proposes measures and policy recommendations aimed at advancing the renewable energy agenda, reducing emissions, mitigating climate change, and ensuring a clean environment and sustainable energy for future generations.

Smart grid enterprise decision-making and economic benefit analysis based on LSTM-GAN and edge computing algorithm

As the next-generation power system, smart grid presents challenges to enterprises in managing and analyzing massive data, meeting complex operational and decision-making demands, and predicting future power demand for grid optimization. This paper aims to proposed a fusion algorithm for smart grid enterprise decision-making and economic benefit analysis, enhancing the accuracy of decision-making and predictive capability of economic benefits. The proposed method combines techniques such as Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and edge computing. The LSTM model is employed to model historical data of the smart grid. The GAN model generates diverse scenarios for future power demand and economic benefits. The proposed method is evaluated on four public datasets, including the ENTSO-E Dataset, and outperforms several traditional algorithms in terms of prediction accuracy, efficiency, and stability. Notably, on the ENTSO-E Dataset, the proposed algorithm achieves a reduction of over 46.6% in FLOP, and a decrease in inference time by over 48.3%, and an improvement of 38% in MAPE. The novel fusion algorithm proposed in this paper demonstrates significant advantages in accuracy and predictive capability, providing a scientific basis for smart grid enterprise decision-making and economic benefit analysis while offering practical value for real-world applications.

A travel demand modeling framework based on OpenStreetMap

Demand modeling is an important part of the setup of a traffic model for a city. All travel demand models rely on land use data as the demand for traveling fundamentally stems from activities occurring at different locations; however, many cities lack these data, or experience in estimating travel demand in their region. In response, this study develops a methodology for generating highly detailed land use data in the form of points of interest (POIs) specifically aimed at travel demand estimation purposes. The framework includes a procedure to extract, clean, enhance, and categorize freely available land use data from OpenStreetMap (OSM) into different POI categories, such as residences, schools, and shops. These residential and activity POIs, which are typical origins and/or destinations of trips, serve as the starting point for estimating travel demand. This paper demonstrates the framework’s utility through three case studies across different cities in Belgium. It validates the effectiveness of OSM-derived POIs for travel demand estimation by replicating Antwerp’s existing demand model, examines the POIs classification’s suitability for various travel demand purposes in Leuven, and assesses the transferability of correlations between OSM data and travel demand from Antwerp to Ghent. Beyond the applications illustrated in this paper, the framework provides opportunities for future research on the consistent disaggregation of existing zonal demand estimates and design-based research in which future demand is estimated given the development of POIs. The framework is openly available as a Python tool called Poidpy.

Resilient and robust management policy for multi‐stage supply chains with perishable goods and inaccurate forecast information: A distributed model predictive control approach

AbstractAn efficient supply chain (SC) management requires that decisions are taken to minimize the effects of parametric uncertainties and unpredictable external disturbances. In this article, we consider this problem with reference to a multi‐stage SC (MSSC) whose dynamics is characterized by the following elements of complexity: perishable goods with uncertain perishability rate, an uncertain future customer demand that is only known to fluctuate inside a given compact set. The problem we face is to define a resilient and robust Replenishment Policy (RP) such that at any stage the following requirements are satisfied: the fulfilled demand is maximized, overstocking is avoided, the bullwhip effect (BE) is mitigated. These objectives should be pursued despite the mentioned uncertainties and unexpected customer demand behaviors violating the bounds of the compact set. Robustness is here intended with respect to uncertainty on the perishability rate, and resiliency as the ability to quickly react to the mentioned unforeseen customer demands. We propose a method based on a distributed resilient robust model predictive control (DRRMPC) approach. Each local robust MPC (RMPC) involves solving a Min‐Max constrained optimization problem (MMCOP). To drastically reduce the numerical complexity of each MMCOP, we parametrize its solution by means of B‐spline functions.

Applying High-Temperature SuperconducTechnologies into Power Systems

implementing of distribution and transmission power systems for urban areas and for future renewableenergy to meet future demand is a challenging task whenconventional cables and transformers are considered.Therefore, there are several methods, that have applied inconational power systems to reduce power losses, voltage regulator issues , capital cost of whole system and increasingpower delivering in urban areas and from offshore farms,such as carefully sited and operated distributed generation(DG) and distributed control techniques. However, thesetechniques may raise others challenges in networks such as stability, voltage regulators issues and increasing capital costof networks. Since High Temperature Superconductor(HTS) cables exhibit zero resistance when cooled to theboiling point of liquid nitrogen (77Keliven), they have thepotential to be used to address these issues in distribution and transmission networks. Consequently, this paperreviews super-conductor power systems: the workpreviously achieved in the DC and AC superconductorpower systems and describes the materials and methodswhich they used. In addition, it shows how the superconductor technologies can address these issues whenthey are used for distribution and transmission powe r systems.

Mode of Action of Antimicrobial Potential Protease SH21 Derived from Bacillus siamensis.

Global public health is facing a major issue with emerging resistance to antimicrobial agents. Antimicrobial agents that are currently on the market are strong and efficient, but it has not been ruled out that these medications will eventually cause resistance to bacteria. Exploring novel bioactive compounds derived from natural sources is therefore, crucial to meet future demands. The present study evaluated the mode of action of the antimicrobial potential protease enzyme SH21. Protease SH21 exhibited antimicrobial activity, strong heat stability (up to 100 °C), and pH stability (pH 3.0 to 9.0). In terms of mode of action, we found that protease SH21 was able to disrupt the bacterial cell membrane as the results of the nucleotide leakage and cell membrane permeability assay. In addition, we also checked inner membrane permeability by PI uptake assay which suggested that protease SH21 has the ability to enter the bacterial cell membrane. Our results revealed that the antimicrobial protease SH21 might be a promising candidate for treating microbial infections.

Determinant Factors Affecting the Development of Digital Competencies of Primary School Students

The global trend of Industry 4.0 has significantly accelerated the digital transformation process. In the field of education, digital transformation is bringing substantial changes to the educational policies and orientations of many countries, especially developing nations. This necessitates equipping students with comprehensive knowledge, skills, and digital competencies to enhance teaching quality and meet future societal demands, even starting from primary education. Although the development of digital competence is expected to yield positive outcomes, it also poses considerable challenges for educators in terms of thorough preparation and policy formulation. The purpose of this study is to explore the factors influencing the development of digital competence in primary school students. The validity and reliability of the measurement tools were assessed using SPSS analysis to perform exploratory factor analysis (EFA), determine reliability, and extract prominent factors. Factor analysis using Varimax rotation retained 16 items in the questionnaire. Analysis of the results from 86 survey samples revealed three factors influencing the development of digital competence, with a KMO measure of sampling adequacy of 0.903; Bartlett's test indicated a Chi-Square significance level of 0.000 < 0.05. These findings provide direction for teachers in identifying priorities to form and develop digital competence in primary school students.

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame.

Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

Power Management System for a Libyan Distribution Network to Meet Future Demand

Abstract— The continuation of increasing the power demand inLibya leads to raise the voltage regulation issues especially indistribution networks. This requires integrating more distributedgenerators (DGs) into distribution networks to meet suchdemand. However, operating several DGs into distributionnetworks raises stability, power management and voltageregulation challenges in Libyan power systems. This is due to theconfliction of operation between the new integrated DGs and theexisting equipments such as On Load Tap Changes Transformers(OLTCT). This paper introduces a potential solution to operateadditional DG with existing OLTCT using Power ManagementSystem (PMS) supervision to overcome such challenges. ThePMS controller works to supervise controllers of existing OLTCTand controllers of Automatic voltage regulator (AVR) andGovernor (GOV) for additional DG to avoid the voltageregulation issue and to meet future demand.

Potential Improvements in Crop Production in Egypt and Implications for Future Water and Land Demand

Similar to numerous water- and data-scarce regions, Egypt confronts a critical challenge in sustaining food production for its rapidly growing population. Consequently, the country’s water and land resources are under considerable stress and require careful management. About half of Egypt’s both annually harvested areas and renewable freshwater are allocated for cultivating rice, maize, wheat, and berseem clover. However, the extent to which crop production might be improved and how this would impact future water and land requirements remains poorly understood. We analyzed potential improvements in the production of these crops and quantified their future water and land requirements under different scenarios. Potential improvements were detected through percentile analysis in three remote sensing-derived performance indicators for each crop in the Nile Delta’s Zankalon region: (i) crop yield, (ii) crop water productivity, and (iii) transpiration fraction (transpiration to actual evapotranspiration, T/AET). We applied detected improvementsto construct plausible scenarios for Egypt’s water and land requirements to sustain domestic crop production until 2050. Our findings indicate limited potential to improve T/AET (< 4%). However, improvements of up to 27% for crop yields and up to 14% for water productivity are possible. To meet the production targets by 2050, national production must increase by 128, 78, 69, and 71% above the 2016–2020’s average for rice, maize, wheat, and berseem, respectively. Depending on the improvement levels in the developed scenarios, a total harvested land area between 5.3 and 6.4 million ha will be required by 2050, with 18% allocated to rice, 28% to maize, 36% to wheat, and 18% to berseem. Associated freshwater requirements will amount to 59–68 billion cubic meters, divided into 23% for rice, 34% for maize, 28% for wheat, and 15% for berseem. Interventions increasing yields and water productivity will benefit more the summer (rice and maize) than the winter crops (wheat and berseem). We discuss likely interventions for meeting these requirements and for sustaining the supply of these crops in Egypt.