Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

The synergy effect of energy security and carbon-haze collaborative management: From the perspective of biased technological progress.

It is important to ensure energy security and achieve carbon-haze collaborative management for sustainable development. Reducing imported energy dependence is necessary to maintain energy security, while its impact on environmental quality remains unclear. From the perspective of biased technological progress, this paper estimates the level of biased technological progress towards self-sufficient energy by a heterogeneous stochastic frontier analysis (SFA) function, and then empirically examines whether self-sufficient energy biased technological progress has a dampening effect on haze pollution and carbon emissions. It is found that: (1) Self-sufficient energy biased technological progress can effectively reduce haze pollution and carbon emissions, achieving a synergistic effect between energy security and carbon-haze collaborative management. (2) "Efficiency enhancement" and "quality improvement" are the essential mechanisms for the synergistic effect. (3) Environmental regulation, abundant resource and technology endowments can enhance the haze reduction effect. And the lower dependence on foreign trade and stable global economic policy environment are more conducive to achieving carbon-haze collaborative control. (4) In the Eastern and Western regions, self-sufficient energy biased technology can be sped up to alleviate haze pollution. The findings can enrich the research exploring pollution control from the perspective of biased technological progress, and provide policy recommendations for promoting high-quality development.

[Environmental protection and energy efficiency in dermatological hospitals]. / Wie können Umweltschutz und Energieeffizienz in dermatologischen Kliniken gelingen?

BACKGROUND: The German healthcare system is responsible for 5% of the national greenhouse gas emissions with inpatient medicine in particular resulting in high energy consumption. Buildings needing renovation, a high demand for consumer goods with the resulting high volume of waste, and the release of greenhouse gases from anesthetic gases contribute to climate-damaging effects in clinics. In order to achieve the goal of climate neutrality by 2045 declared by the federal government, comprehensive structural changes and the establishment of sustainable measures are therefore also necessary in dermatological clinics. OBJECTIVES: This work is intended to give dermatologists a compact overview of the possible fields of action. MATERIALS AND METHODS: Selective literature research on sustainability in clinics and development of concrete proposals for action as well as a summary of own experiences in establishing an environmental management system at the SLK Clinics of Heilbronn. RESULTS: The know-how of professional climate and environmental management is often not yet available. In order to offer motivated dermatologists and employees of clinics concrete assistance, this article shows possible courses of action in the following fields: Energy management and information technology (IT), durable and consumer goods, waste management, sustainable food supply, mobility, public relations, and diagnostics and therapy. CONCLUSION: Environmental protection measures and energy efficiency in clinics are difficult to implement due to limiting factors such as staff shortages, financial constraints, and a lack of instructions. As shown here, however, the implementation of these measures through resource savings, such as saving energy, is often also financially worthwhile and can make a significant contribution to achieving climate neutrality.

Passive climate regulation with transpiring wood for buildings with increased energy efficiency.

Buildings are significant end-users of global energy. About 20% of the energy consumption worldwide is used for maintaining a comfortable indoor climate. Therefore, passive systems for indoor temperature and humidity regulation that can respond to environmental changes are very promising to reduce buildings' energy consumption. We developed a process to improve the responsiveness of wood to humidity changes by laser-drilling microscopic holes and incorporating a hygroscopic salt (calcium chloride). The resulting "transpiring wood" displays superior water adsorption capacity and high moisture exchange rate, allowing regulation of humidity and temperature by the exchange of moisture with the surrounding air. We proved that the hygrothermal performance of transpiring wood can be used to regulate indoor climate, with associated energy savings, for various climate types, thus favoring its application in the building sector. The reduction of temperature fluctuations, thanks to the buffering of temperature peaks, can lead to an indirect energy saving of about 10% for cooling and between 4-27% for heating depending on the climate. Furthermore, our transpiring wood meets different sustainability criteria, from raw materials to the fabrication process, resulting in a product with a low overall environmental impact and that is easy to recycle.

Does the Emission Trading Scheme achieve the dual dividend of reducing pollution and improving energy efficiency? Micro evidence from China.

Resolving the negative externality of environmental pollution has always been a concern in both the theoretical and practical space. To stimulate enterprises to participate in environmental governance actively, China has implemented a series of environmental regulation policies. The Emission Trading Pilot Scheme (ETPS) is an example of such policies implemented to ensure the gradual transition toward marketization. From a micro-enterprise perspective, the study examines how this policy achieves the dual effects of reducing emissions and promoting energy efficiency. We further explore potential channels through which this policy influences the dual effects. We empirically find ETPS to reduce the pollution emissions of enterprises significantly. However, the pollution reduction effect is mainly achieved by encouraging enterprises to strengthen cleaner production rather than through end governance. In addition to bringing environmental dividends, we observe ETPS to improve fossil energy efficiency by about 7.5% indirectly. We conclude by urging policy makers and participants to optimize energy structures and adjust intermediate input as they serve as significant pathways through which ETPS can affect fossil energy efficiency. The ETPS can encourage enterprises to actively step out of their "comfort zone" of environmental governance to be viewed as an effective environmental regulation policy.

COVID-19, Green Deal and recovery plan permanently change emissions and prices in EU ETS Phase IV.

The EU emissions trading system's (ETS) invalidation rule implies that shocks and overlapping policies can change cumulative carbon emissions. This paper explains these mechanisms and simulates the effect of COVID-19, the European Green Deal, and the recovery stimulus package on cumulative EU ETS emissions and allowance prices. Our results indicate that the negative demand shock of the pandemic should have a limited effect on allowance prices and rather translates into lower cumulative carbon emissions. Aligning EU ETS with the 2030 reduction target of -55% might increase allowance prices to 45-94 €/ton CO2 today and reduce cumulative carbon emissions to 14.2-18.3 GtCO2 compared to 23.5-33.1 GtCO2 under a -40% 2030 reduction target. Our results crucially depend on when the waterbed will be sealed again, which is an endogenous market outcome, driven by the EU ETS design, shocks and overlapping climate policies such as the recovery plan.

Optimizing renewable-based energy supply options for power generation in Ethiopia.

Ethiopia unveiled homegrown economic reform agenda aimed to achieve a lower-middle status by 2030 and sustain its economic growth to achieve medium-middle and higher-middle status by 2040 and 2050 respectively. In this study, we evaluated the optimal renewable energy mix for power generation and associated investment costs for the country to progressively achieve upper-middle-income countries by 2050. Two economic scenarios: business as usual and Ethiopia's homegrown reform agenda scenario were considered. The study used an Open Source energy Modeling System. The model results suggest: if projected power demand increases as anticipated in the homegrown reform agenda scenario, Ethiopia requires to expand the installed power capacity to 31.22GW, 112.45GW and 334.27GW to cover the current unmet and achieve lower, medium and higher middle-income status by 2030, 2040 and 2050 respectively. The Ethiopian energy mix continues to be dominated by hydropower and starts gradually shifting to solar and wind energy development towards 2050 as a least-cost energy supply option. The results also indicate Ethiopia needs to invest about 70 billion US$ on power plant investments for the period 2021-2030 to achieve the lower-middle-income electricity per capita consumption target by 2030 and staggering cumulative investment in the order of 750 billion US$ from 2031 to 2050 inclusive to achieve upper-middle-income electricity consumption rates by 2050. Ethiopia has enough renewable energy potential to achieve its economic target. Investment and financial sourcing remain a priority challenge. The findings could be useful in supporting decision-making concerning socio-economic development and investment pathways in the country.

A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization.

Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO's exploration phase is enhanced, and the SHO's hunting mechanisms are modified with PSO's velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization.

Plant single-cell solutions for energy and the environment.

Progress in sequencing, microfluidics, and analysis strategies has revolutionized the granularity at which multicellular organisms can be studied. In particular, single-cell transcriptomics has led to fundamental new insights into animal biology, such as the discovery of new cell types and cell type-specific disease processes. However, the application of single-cell approaches to plants, fungi, algae, or bacteria (environmental organisms) has been far more limited, largely due to the challenges posed by polysaccharide walls surrounding these species' cells. In this perspective, we discuss opportunities afforded by single-cell technologies for energy and environmental science and grand challenges that must be tackled to apply these approaches to plants, fungi and algae. We highlight the need to develop better and more comprehensive single-cell technologies, analysis and visualization tools, and tissue preparation methods. We advocate for the creation of a centralized, open-access database to house plant single-cell data. Finally, we consider how such efforts should balance the need for deep characterization of select model species while still capturing the diversity in the plant kingdom. Investments into the development of methods, their application to relevant species, and the creation of resources to support data dissemination will enable groundbreaking insights to propel energy and environmental science forward.

Research on residual strength characteristics of high water materials based on improved H-B criterion.

To develop a new gangue polymer filling material with low compressive ratio, this paper intends to add high water cementing material to the gangue for backfilling. Uniaxial and tri-axial bearing experiments were conducted to study its bearing characteristics and residual strength. Based on Hock-Brown model theory, it is proposed that friction angle φr can be introduced to substitute model parameter mi, and the degree of cohesion loss can characterize the value of s. So the improved H-B model is established to characterize the residual strength of materials with ductile failure characteristics. The results show that the compressive strength of high water filling material increases linearly corresponding to the rise of confining pressure, and its strength characteristics conform to Mohr-Coulomb strength criterion. The ductile failure characteristics of the sample endow it with high residual strength, which in turn qualifies it for underground filling. After the introduction of cohesion and friction angle, the improved H-B criterion can fit the residual strength curve of the high water filling material more competently. The fitting coefficient of the samples with three water contents is 1.00, 0.99, and 1.00, respectively. The improved H-B model of residual strength demonstrates the change rule of residual strength of the samples corresponding to the change of confining pressure; under tri-axial loading, the angle between fracture surface and axial direction becomes larger as the confining pressure rises; and the failure mode of the material transforms from splitting failure to shear failure.

Metal-Free Multilayer Hybrid PENG Based on Soft Electrospun/-Sprayed Membranes with Cardanol Additive for Harvesting Energy from Surgical Face Masks.

Disposable surgical face masks are usually used by medical/nurse staff but the current Covid-19 pandemic has caused their massive use by many people. Being worn closely attached to the people's face, they are continuously subjected to routine movements, i.e., facial expressions, breathing, and talking. These motional forces represent an unusual source of wasted mechanical energy that can be rather harvested by electromechanical transducers and exploited to power mask-integrated sensors. Typically, piezoelectric and triboelectric nanogenerators are exploited to this aim; however, most of the current devices are too thick or wide, not really conformable, and affected by humidity, which make them hardly embeddable in a mask, in contact with skin. Different from recent attempts to fabricate smart energy-harvesting cloth masks, in this work, a wearable energy harvester is rather enclosed in the mask and can be reused and not disposed. The device is a metal-free hybrid piezoelectric nanogenerator (hPENG) based on soft biocompatible materials. In particular, poly(vinylidene fluoride) (PVDF) membranes in the pure form and with a biobased plasticizer (cardanol oil, CA) are electrospun onto a laser-ablated polyimide flexible substrate attached on a skin-conformable elastomeric blend of poly(dimethylsiloxane) (PDMS) and Ecoflex. The multilayer structure of the device harnesses the piezoelectricity of the PVDF nanofibers and the friction triboelectric effects. The ultrasensitive mechanoelectrical transduction properties of the composite device are determined by the strong electrostatic behavior of the membranes and the plasticization effect of cardanol. In addition, encapsulation based on PVDF, PDMS, CA, and parylene C is used, allowing the hPENG to exhibit optimal reliability and resistance against the wet and warm atmosphere around the face mask. The proposed device reveals potential applications for the future development of smart masks with coupled energy-harvesting devices, allowing to use them not only for anti-infective protection but also to supply sensors or active antibacterial/viral devices.

Date pits based nanomaterials for thermal insulation applications-Towards energy efficient buildings in Qatar.

Air-conditioning systems make the most significant part of energy consumption in the residential sector. There is no denying that it is essential to produce a comfortable indoor thermal environment for residents in a building. The actual goal is to achieve thermal comfort level without putting too much cost on the ecological system by trying to conserve the amount of energy consumed. An effective way to help achieve such a goal is by incorporating thermal insulation in buildings. Thermal insulations help reduce thermal energy gained during the implementation of a desired thermal comfort level. This study aims to use an environmentally friendly nanoparticle of date pits to create thermal insulations that can be used in buildings. Different ratios of the nanoparticle of the date pits and sand composite were investigated. Fourier transform infrared spectroscopy and scanning electron microscopy were used to characterize the new materials. The material with nanoparticles of date pits and 50% by-volume epoxy provided good thermal insulation with thermal conductivity of 0.26 W/mK that could be used in the existing buildings. This has the potential to reduce the overall energy consumption by 4,494 kWh and thereby reduce CO2 emissions of a 570 m2 house by 1.8 tons annually. In conclusion, the future of using nanoparticles of date pits in construction is bright and promising due to their promising results.

A tree-planting decision support tool for urban heat mitigation.

Heat poses an urgent threat to public health in cities, as the urban heat island (UHI) effect can amplify exposures, contributing to high heat-related mortality and morbidity. Urban trees have the potential to mitigate heat by providing substantial cooling, as well as co-benefits such as reductions in energy consumption. The City of Boston has attempted to expand its urban canopy, yet maintenance costs and high tree mortality have hindered successful canopy expansion. Here, we present an interactive web application called Right Place, Right Tree-Boston that aims to support informed decision-making for planting new trees. To highlight priority regions for canopy expansion, we developed a Boston-specific Heat Vulnerability Index (HVI) and present this alongside maps of summer daytime land surface temperatures. We also provide information about tree pests and diseases, suitability of species for various conditions, land ownership, maintenance tips, and alternatives to tree planting. This web application is designed to support decision-making at multiple spatial scales, to assist city officials as well as residents who are interested in expanding or maintaining Boston's urban forest.

Performance of Exoelectrogenic Bacteria Used in Microbial Desalination Cell Technology.

The tri-functional purpose of Microbial Desalination Cell (MDC) has shown a great promise in our current scarcity of water, an increase in water pollution and the high cost of electricity production. As a biological system, the baseline force that drives its performance is the presence of exoelectrogens in the anode chamber. Their presence in the anodic chamber of MDC systems enables the treatment of water, desalination of seawater, and the production of electrical energy. This study reviews the characteristics of exoelectrogens, as a driving force in MDC and examines factors which influence their growth and the performance efficiency of MDC systems. It also addresses the efficiency of mixed cultures with certain predominant species as compared to pure cultures used in MDC systems. Furthermore, the study suggests the need to genetically modify certain predominant strains in mixed cultures to enhance their performance in COD removal, desalination and power output and the integration of MDC with other technologies for cost-effective processes.

Increasing water and energy efficiency in university buildings: a case study.

Nowadays, humanity is consuming unsustainably the planet's resources. In the scope of energy resource consumption, e.g., the intense use of fossil fuels has contributed to the acceleration of climate changes on the planet, and the overriding need to increase energy efficiency in all sectors is now widely recognized, aiming to reduce greenhouse gases (GHG) emissions by 69% in 2030. Largely due to climate changes, water has also become a critical resource on the planet and hydric stress risk will rise significantly in the coming decades. Accordingly, several countries will have to apply measures to increase water efficiency in all sectors, including at the building level. These measures, in addition to reducing water consumption, will contribute to the increase of energy efficiency and to the decrease of GHG emissions, especially of CO2. Therefore, the nexus water energy in buildings is relevant because the application of water efficiency measures can result in a significant contribution to improve buildings' energy efficiency and the urban water cycle (namely in abstraction, treatment, and pumping). For Mediterranean climate, there are few studies to assess the extent and impact of this nexus. This study presents the assessment of water-energy nexus performed in a university building located in a mainland Portugal central region. The main goals are to present the results of the water and energy efficiency measures implemented and to assess the consequent reduction of water, above 37%, and energy (30%) consumption, obtained because of the application of water-efficient devices and highly efficient light systems in the building. The water efficiency increase at the building level represents at the urban level an energy saving in the water supply system of 406 kWh/year, nearly 0.5% of the building energy consumption, with a consequent increase in the energy efficiency and in the reduction of GHG emissions. Complementarily, other energy-efficient measures were implemented to reduce the energy consumption. As the building under study has a small demand of domestic hot water with no hydro pressure pumps and has a small water-energy nexus, it was concluded that the significant reduction of the building energy consumption did not influence the indoor comfort.

Incorporating Injury Prevention Into Energy Weatherization Programs.

Clients receiving weatherization/energy services with an added injury prevention home assessment with modifications/repairs experienced a decline in falls and thus fall-related costs. Interventions in 35 homes were associated with significant reductions in falls from baseline to 6 months postintervention (from 94% to 9%; P < .001) and falls with calls for assistance (from 23% to 3%; P < .02). The decline in falls with calls for assistance in the intervention group was significant when adjusted for a comparison group effect (P = .07). At a median cost of $2058 per home, the addition of an injury prevention component led by an occupational therapist offers the potential to avoid expensive fall-related medical costs (lift assistance, hospital transport and admission, long-term care). Integration of injury prevention into weatherization work, which targets lower-income seniors with high energy use, offers potential to reduce costly hospitalizations and poor health outcomes.