Optimize Energy Use Research Articles

Energy Management System for the Campus Microgrid Using an Internet of Things as a Service (IoTaaS) with Day-ahead Forecasting

In the contemporary energy landscape, characterized by a global commitment to sustainability, the effective management and forecasting of energy consumption play pivotal roles in achieving environmental and economic goals. As nations strive to meet sustainable development targets, optimizing energy use becomes imperative. This paper addresses these challenges by focusing on load forecasting and energy management within the context of a Savona campus microgrid. In this thesis, the NNR algorithm based load profile prediction model was proposed. The development process involved a detailed exploration of the correlation between weather information and electricity consumption. Furthermore, the outputs of the load forecasting model, namely the predicted load profiles, were subsequently utilized in the Energy Management System (EMS) to optimally manage power flows in the campus microgrid using an Internet of things as a service (IoTaaS) with day-ahead forecasting model. The overall results of the model evaluation across all periods reveal a Mean Absolute Error (MAE) of 9.63 kW, a Coefficient of Determination (R2) of 0.79, and a Mean Absolute Percentage Error (MAPE) of 9.02%. These metrics provide a comprehensive assessment of the model's performance across various temperature conditions. The proposed load profile forecasting model was integrated into the Energy Management System (EMS) developed for Savona campus microgrid in Italy. The findings provide a valuable framework for optimizing microgrid operations, aligning with global sustainability objectives.

Enhancing electric vehicle efficiency through model predictive control of power electronics

This study examines the improvement of electric vehicle (EV) economy by using Model Predictive Control (MPC) in power electronics, with the goal of optimizing system performance. Experimental assessments done on different battery parameters have identified a spectrum of capacities, ranging from 55 kWh to 75 kWh, and voltages, ranging from 380V to 450V, that impact the total energy storage and power production capabilities. The efficiency percentages recorded in the battery systems ranged from 90% to 95%, suggesting differences in energy losses throughout the operations of charging and discharging. Furthermore, examinations of power electronics control configurations highlighted the significance of PWM frequencies (varying from 8 kHz to 12 kHz) and modulation indices (0.75 to 0.85) on the efficiency of power conversion. The results indicated efficiency rates ranging from 94% to 97%, emphasizing the efficacy of MPC-based techniques in improving power flow. The assessment of electric vehicle (EV) performance parameters demonstrated driving ranges ranging from 140 km to 180 km, with energy consumption rates ranging from 50 kWh to 60 kWh. The efficiency metrics ranged from 2.5 km/kWh to 3.0 km/kWh, and were directly affected by the battery properties and improvements in power electronics. Moreover, there was a little change in the link between temperature variations (ambient temperature ranging from 23°C to 29°C and battery temperature from 32°C to 40°C) and efficiency. This highlights the system's sensitivity to external variables. In summary, this relationship between battery characteristics, power electronics control, and environmental conditions in determining the efficiency of electric vehicles (EVs). The results emphasize the importance of customized setups and control techniques based on model predictive control (MPC) in optimizing energy use and increasing the distance electric cars can travel. These findings provide valuable knowledge for the development of sustainable transportation solutions in the electric vehicle industry.

R&D and Application of Smart Park Energy Control Platform Combined with MLM Technology

Abstract As the core of park intelligence, the smart park energy control platform realizes the efficient management and optimal use of park energy by integrating and applying IoT technology. This study focuses on the research development and application of a smart park energy management and control platform using IoT technology, firstly constructing the IoT smart park platform, designing the login module, the park information module, and the energy consumption analysis module, and describing the steps of the smart park energy collection algorithm, and then designing the model of the energy prediction and planning algorithms, i.e.In the construction of the LSTM heating temperature prediction model and the GRU network modeling. Testing and research can be done by using a smart park as an example. The empirical study shows that the construction of the IoT smart park platform can improve the accuracy of monitoring, and the difference between the actual collection results and the system monitoring results is no more than 0.03 kW. After optimizing the configuration, the park’s energy supply system can meet the electric heating and cooling loads with optimal efficiency. Through the comprehensive evaluation of the operation of the extensive energy information monitoring platform, the environmental pollutant emissions are reduced by about 20%, and the platform has a remarkable effect on energy saving and emission reduction.

SUSTAINABLE FOOD PACKAGING IMPACT TO THE REDUCTION OF TRANSPORT COSTS

People dealt with many new issues related to sustainable development goals. Different decisions oriented to sustainable food packing would be very useful, if they could bring significant effect through the reduction of CO2 emission. The paper focuses on sustainable food packing and its effect on reduction of freight transportation costs. This topic has many research gaps and requires theoretical and practical investigations. Integration of sustainability into long term process has clear implications for the environment. Meherishi et al. (2019) highlight that the process of creating sustainable packaging guarantees the optimal use of materials and energy and is efficiently regenerated without wasting of natural resources. The impact of food packaging on transportation is clear and depends on a number of factors, which are shaped by the characteristics of the food packaging. In addition, among benefits of sustainable food packaging are such main functions as product safety and its identification. Modern complex solutions covering sustainable packing and transport costs reduction are important for increasing environmental sustainability. The study aims to analyze the impact of packaging on the reduction of transport costs. The aim was reached by performing sustainable food packaging and its impact on the reduction of transport costs theoretical justification and by conducting biometric analysis. The overview of the literature of sustainable food packaging and its impact on the supply chain was conducted, and it was found that the food packaging described different types of packaging and new packaging technologies which have an impact on the supply chain and are determined by some factors related to the characteristics of food packaging and the nature of the supply chain. Equally the overview of literature on the transport of food dynamics and the role of transport costs reduction on sustainability was also conducted. It was found that there is a need to develop new technologies and methods for food management and CO2 emissions for longer transport routes reduction. Either the research was conducted by biometric analysis for 2022-2023 to identify current trends, and it was found that the number of analyzed articles, included four clusters, which show that topics are oriented to delivery, flow, algorithms, structure, and role. The bibliometry analysis results show that product packing has links with transport and delivery and algorithms. The authors provided investigations under the topic as it requires knowledge to fill the existing gaps.

Prediksi Daya Listrik Pada Pembangkit Listrik Siklus Gabungan Berdasarkan Kondisi Lingkungan Menggunakan Metode Machine Learning

The utilization of machine learning methods in energy simulation enables the optimization of energy use and improves energy efficiency. In this research, the modeling of predicting power output was conducted under full load conditions in a Combined Cycle Power Plant (CCPP) based on the surrounding environmental conditions. Historical data of CCPP operation were used to model and predict power output under various environmental conditions. In this study, four machine learning algorithms, namely Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), were compared and evaluated for their performance. The evaluation metrics used to measure the model performance were Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-Squared. The research results indicate that the Random Forest (RF) model achieved the best performance compared to other models with MAE of 2.314, RMSE of 3.372, and R-squared of 0.961. Additionally, the RF model also performed the best compared to other models in external testing with new data, where RF obtained values of MAE 2.579, RMSE 3.315, and R-squared 0.957. These results are consistent with the previous testing, indicating that RF has stable and reliable performance in predicting larger and more diverse datasets. This research contributes to understanding the potential application of machine learning in the power generation industry, especially in CCPP.

A many-to-many pick-up and delivery problem under stochastic battery depletion of electric vehicles

ABSTRACT The study extends the traditional pick-up and delivery problems (PDPs) to address the specific challenges of urban logistics and electric vehicle (EV) adoption. These challenges include the limited range of EVs, energy consumption along the route, and uncertainty in traffic conditions. To overcome the limited range of EVs, the study includes battery swapping stations to ensure sufficient energy to complete delivery routes. Vehicle energy consumption is considered to reduce range anxiety and optimize energy use. The study also considers the unpredictability of traffic conditions that affect energy consumption and delivery schedules. To address these concerns, the study proposes an approximate Quadratic Chance-Constrained Mixed-Integer Programming (QC-MIP) model with a linear approximation, a constructive heuristic and a meta-heuristic. These quantitative models incorporate comprehensive EV energy estimation approaches, enabling more accurate energy predictions. The proposed approaches provide valuable insights and strategies for improving energy efficiency and delivery performance in urban logistics environments.

Parametric Assessment to Evaluate and Compare the Carbon Footprint of Diverse Manufacturing Processes for Building Complex Surfaces

At present, building design is faced with a need to properly manage complex geometries and surfaces. This fact is not only driven by the increased demand for visually stunning spaces but also stems from the rise of new design paradigms, such as “user-centred design”, that include bespoke optimization approaches. Nevertheless, the escalating adoption of customized components and one-off solutions raises valid concerns regarding the optimal use of energy and resources in this production paradigm. This study focuses on the Life Cycle Assessment of a novel Cement–Textile Composite (CTC) patented material. It combines a synthetic reinforcing textile with a customized concrete matrix, to generate rigid elements that are able to statically preserve complex spatial arrangements, particularly double-curvature surfaces. Moreover, the CTC offers a low-volume cost-effective alternative for custom-made cladding applications. The study performed a comparative carbon footprint assessment of the CTC production process in contrast to other technologies, such as CNC milling and 3D printing. To facilitate meaningful comparisons among diverse construction alternatives and to derive generalized data capable of characterizing their overall capacity, independent of specific production configurations, the present study implemented a generalized parametric shape of reference defined as a bounding box (BBOX), which encloses the volume of the target shape. Comparing different production technologies of the same shape with the same BBOX results in a significant carbon saving, up to 9/10th of the carbon footprint, when the CTC technology is adopted. The study therefore highlights the potential environmental advantages of CTC in the fields of architectural design and building engineering.

Optimal battery scheduling in solar-plus-storage grid-connected microgrid for profit and cost efficiency: A use case on an Israeli microgrid

This paper presents an optimal energy management algorithm for solar-plus-storage grid-connected microgrid simulated on a real full-scale small town microgrid test-case, taking into account the daily solar energy generation as well as the electricity demand to ensure that the battery is charged and discharged at the optimal times to balance energy supply and demand. The optimization is performed using dynamic programming and finding the minimum-cost-path, to minimize the total cash-flow. The formulation is utilized with two variations such that two business models are considered, optimizing cost efficiency in both aspects of minimizing cost and maximizing profits, according to the scenario. The first model maximizes self-consumption, and the second model is the case of retail electricity providers. The results are compared to real data from a solar-plus-storage grid-connected microgrid located in Israel, that is currently managed by a rule-based scheduling procedure. The results show that by using the optimal control schedule, the revenue increases up to 8% during the spring and autumn when the weather conditions are typically moderate. In summer and winter, where the climate conditions are more extreme and the demand for energy is at its peak, the full revenue potential can be realized by using the suggested optimal control algorithm, offering an increased profit of up to 30%. Unlike prior studies which are restricted to small use-cases, this paper leverages a unique and private large-scale data-set from a small town, enabling a thorough exploration of the optimization’s real-world viability and scalability. Through rigorous simulations, the findings underscore the method’s exceptional potential in effectively integrating PV-plus-storage assets and optimizing energy use in power grids.

Optimized configuration and operation model and economic analysis of shared energy storage based on master-slave game considering load characteristics of PV communities

As a new form of energy storage, shared energy storage (SES) is characterized by flexible use and high utilization rate, and its application in photovoltaic (PV) communities has not yet been promoted because of the unclear operation mode and revenue effect. This paper focuses on the configuration, operation and economic benefits of SES in PV communities, comparing the differences in electricity consumption behavior and cost of electricity in PV communities with and without SES scenarios. First, a trading framework between SES and PV community is constructed by combining the reality of China's power market. SES provides charging and discharging services as well as dynamic capacity rental service, and PV community chooses the capacity as well as the time to use SES according to its own situation. Then, the decision-making advantage of SES is considered, and a SES-PV community model based on the master-slave game is established, where the upper level optimizes the system capacity configuration and operation with the goal of minimizing the cost of SES, and the lower level optimizes the electricity consumption behavior with the goal of minimizing the cost of electricity consumption in the PV community. Finally, the adopted model is simulated to obtain the optimal configuration capacity, operation strategy of SES and load optimization results for PV community. The respective costs of SES and PV community are further measured. The results show that SES is only profitable with subsidies, and the cost of electricity in the PV community with SES is reduced by 17.16 % compared to the community without SES. In addition, a sensitivity analysis is conducted to address the benefits of SES, which can be realized through the use of more cost-effective construction materials, access to the electricity market, appropriately increasing the price of dynamic rental capacity, and selecting the operation in the area where the PV output and the community load are more compatible. The research can provide reference for the development mode of SES and the optimization of energy use in PV communities.

Environmental Conservation and Sustainability: Strategies for a Greener Future

The quest for environmental conservation and sustainability is an urgent and multifaceted challenge, pivotal for securing a greener and more sustainable future. This abstract encapsulates key strategies that are fundamental in addressing the diverse aspects of human impact on the environment, with a focus on renewable energy, energy efficiency, sustainable agriculture, water conservation, habitat preservation, and public education and awareness. Renewable energy is at the forefront of this transformation. The traditional dependence on fossil fuels has led to significant environmental problems, including climate change, air pollution, and natural habitat destruction. Transitioning to renewable energy sources such as solar, wind, and hydroelectric power is imperative. These sources not only reduce greenhouse gas emissions but also mitigate the impacts of climate change, thus providing a cleaner, more sustainable energy solution. Improving energy efficiency is another critical strategy. Enhanced energy efficiency in buildings, transportation, and industrial processes can substantially lower energy consumption and related emissions. Implementing better insulation, energy-efficient appliances, and smart technologies that optimize energy use are practical approaches to achieve this goal. In the realm of agriculture, sustainable practices are essential. Techniques like crop rotation, organic farming, and permaculture promote soil health, conserve biodiversity, and decrease reliance on harmful chemicals. These practices contribute to more sustainable food production systems that are less detrimental to the environment. Water conservation is equally vital. Efficient use and management of water through technologies that support water recycling and rainwater harvesting can help sustain this essential resource, particularly in regions experiencing water scarcity. The preservation of natural habitats and biodiversity is also crucial. Protecting forests, oceans, and other ecosystems from threats such as pollution and deforestation is key to maintaining biodiversity and ecological balance, which are fundamental for life on Earth. Public education and awareness are indispensable in fostering change.

Development Status and Recommendations of the Energy Service Industry


 The essay examines the energy-saving industry's current state of development in China, identifies its issues, and suggests appropriate solutions. To achieve optimal energy use, energy saving refers to preserving energy while minimizing energy consumption. Contractual energy management was brought to China in the 1970s and developed there with the support of pertinent state policies. The growth of the market for energy-saving services and the quick industrial scale expansion have, however, also presented this industry with new hurdles as a result of the global concern for energy security. It primarily shows itself as uneven technological standards, questionable incentive programs, and funding challenges. On this foundation, solutions to the existing issues are suggested as countermeasures. Therefore, it should advance science and technology, advance technology, advance the development of product quality standards, advance the implementation of stricter market access requirements, and advance the establishment of an ideal industry management system. On this foundation, pertinent policy suggestions are made in an effort to support the steady and orderly growth of the energy-saving services sector. The growth of China's energy services business has been aided by the adoption of the aforementioned programs.

Energy analysis and economic evaluation of trigeneration system integrating compressed air energy storage system, organic Rankine cycle with different absorption refrigeration systems

In recent years, a concerted effort has been made to reduce reliance on conventional fossil fuels by advancing the use of renewable energy. Nonetheless, the use of renewable energy presents inherent challenges, prominently including intermittency and unpredictability. These obstacles impede the seamless integration and optimal use of renewable energy. A promising solution to address the issues of intermittency and unpredictability in renewable energy is compressed air energy storage (CAES) technology. The performance of CAES can be significantly enhanced through the implementation of a trigeneration system. Some studies have been performed to develop strategies in order to improve the round-trip efficiency (RTE) of the CAES. However, no studies have evaluated the effect of integrating different types of absorption refrigeration systems (ARSs) (different effects and different working media). Steady-state models for CAES, organic Rankine cycle (ORC) and different types of ARSs were developed in Aspen Plus®. All the models were validated in Aspen Plus®. Then process study was conducted to analyse the effects of recuperator outlet temperature and ARS mass flowrate on CCHP system performance. Economic evaluation was performed using Aspen Process Economic Analyser® (APEA). The double-effect ARS is superior to single-effect ARS with the same working medium. The LiBr-H2O is more suitable than NH3-H2O as working medium. The CCHP implemented with double-effect ARS using LiBr-H2O can produce 206 MWe of electrical energy, 7.26 MWth of heating and 27.28 MWth cooling capacity with coefficient of performance (COP) 1.36. The process analysis indicates that COP and RTE increased with the ARS mass flowrate. The levelized cost of electricity (LCOE) of the CCHP-3 system is 31.01 $/MWh, the payback period of the CCHP-3 system is 12.9 years. Overall, this first systematic study comparing different types of ARS for the CCHP system will provide important guidance on the process design and analysis of CCHP.

Development of a PV/Battery Micro-Grid for a Data Center in Bangladesh: Resilience and Sustainability Analysis

Energy resiliency plays an important role in the proper functioning of data centers as they heavily rely on an uninterrupted power supply to ensure smooth operation. In the case of a power outage, the data center’s operation might be hampered, which results in system downtime, data, and economic loss. This issue is severe in developing countries where power supply infrastructures are inadequate and conventional. Microgrids can be an effective solution in this regard. Although several studies developed microgrids to observe the energy resilience benefit for some critical facilities, critical facilities like data centers are often overlooked. In addition, sustainability analysis of a microgrid is also scarce in the present literature. Therefore, one new resilience and sustainability indicator has been developed and implemented in this analysis to fill this gap. For this, new indicators, such as the resilience cost index (RCI) and renewable energy penetration (REP), were used in this study. This study used HOMER version 3.13.3 and REopt software to simulate a robust photovoltaic (PV) and battery microgrid for a hypothetical data center in Bangladesh. A random (48 h) outage was assigned to witness the adaptability of the modelled micro-grid. The suitable size of PV and battery was found to be 249,219 kW and 398,547 kWh, respectively. The system’s USD 18,079,948 net present value (NPV) demonstrates the economic potential of utilizing PV and battery microgrids for data centers. The RCI of the system is found to be 35%, while the REP is 87%. The energy consumption saving of the system is USD 21,822,076. The system emits 652% less CO2 than the grid. The result of this system is also compared with a diesel-based system. After comparison, it is found that the developed PV/battery microgrid provides better environmental and economical service than the diesel generator. During blackouts, the system keeps the data center powered up without interruption while improving energy resilience and lowering carbon emissions. The outcome of this current analysis can serve as a blueprint for other microgrid projects in Bangladesh and other developing countries. By integrating PV/battery microgrids, data centers can cut costs, reduce emissions, and optimize energy use. This will make data centers less reliant on grid services and more flexible to forthcoming development.

Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact.

Agile and integrated workflow proposal for optimising energy use, solar and wind energy potential, and structural stability of high-rise buildings in early design decisions

Postponing performance analysis of buildings until the final stage of the design process often leads to unexpected results, introducing errors and disrupting the architect's workflow. The literature review identifies prior research limitations, motivating this study to develop a high-rise design optimisation workflow, automate design preparation, explore alternative airflow simulation methods, and compare Optimisation (Opt) algorithms with Genetic Algorithms (GAs) for broader design considerations. We thus propose an integrated computational workflow for sustainable building design optimisation with four objectives. This study applied it in the early design phase of a practical energy efficiency-oriented redevelopment project involving a cluster of seven high-rise office buildings in Nanjing. The project aimed to decrease annual energy use, increase solar and wind energy potential, and reduce structural displacement. Our methodology primarily utilises the surrogate-model algorithm Radial Basis Function Muti-objective Optimisation (RBFMOpt) algorithm, enabling quick identification of self- and new- energy demands and cost-effective structural solutions for tall buildings while optimising energy performance. Different from traditional genetic algorithm-driven optimisation procedures or with a separated post-optimisation, our workflow follows a pre-design approach. Test results are automatically optimised in a loop without post-processing, aligning with the architect's research objectives and improving the decision-making process. Our study yielded a 12% increase in energy use efficiency and a 23% improvement in structural stability. However, achieving these gains resulted in a 32% decrease in solar energy potential. Furthermore, we provide a comparative study between RBFMOpt and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), offering critical guidance for designers when selecting optimisation algorithms to address efficiency and precision challenges in energy-related design. These findings contribute to an integrated research-for-design workflow, providing data-driven evidence to support early design decisions.

Ten questions concerning planning and design strategies for solar neighborhoods

Planning of neighborhoods that efficiently implement active solar systems (e.g., solar thermal technologies, photovoltaics) and passive solar strategies (e.g., daylight control, sunlight access through optimized buildings' morphology, cool pavements, greeneries) is increasingly important to achieve positive energy and carbon neutrality targets, as well as to create livable urban spaces. In that regard, solar neighborhoods represent a virtuous series of solutions for communities that prioritize the exploitation of solar energy, with limited energy management systems. The ten questions answered in this article provide a critical overview of the technical, legislative, and environmental aspects to be considered in the planning and design of solar neighborhoods. The article moves from the categorization of “Solar Neighborhood” and the analysis of the state-of-the-art passive and active solar strategies to the identification of challenges and opportunities for solar solutions’ deployment. Insights into legislative aspects and lessons learned from case studies are also provided. Ongoing trends in solar energy digitalization, competing use of urban surfaces, and multi-criteria design workflows for optimal use of solar energy are outlined, emphasizing how they generate new opportunities for urban planners, authorities, and citizens. A framework is introduced to guide the potential evolution of solar neighborhoods in the next decade and to support the design of urban areas and landscapes with architecturally integrated solar energy solutions.

Case Study - Concrete Mixer’s Power Consumption and Wear Resistance on Mixing Blade

Abstract: Concrete mixers play a pivotal position in the construction industry, facilitating the efficient blending of cement, aggregates, water, and admixtures to create the fundamental building material of our modern world. However, the process of mixing concrete is energy-intensive and exerts significant wear and tear on the mixing blades, leading to maintenance costs and environmental concerns. This abstract explores strategies and innovations aimed at reducing power consumption and minimizing wear on mixing blades in concrete mixers, contributing to sustainability in construction practices. To address power consumption, advancements in motor efficiency, coupled with variable frequency drives (VFDs), have emerged as effective tools for optimizing energy use. In terms of blade wear, engineering developments in blade materials have been instrumental in enhancing durability. Hardened steel alloys and innovative composite materials have been introduced to mitigate wear and prolong blade lifespan.

Energy Performance of Room Air-Conditioners and Ceiling Fans in Mixed-Mode Buildings

Studies show that people can tolerate elevated temperatures in the presence of appreciable air movement (e.g., from using ceiling fans). This minimises the use of air-conditioners and extends their set-point temperature (Tset), resulting in energy savings in space cooling. However, there is little empirical evidence on the energy savings from using ceiling fans with Room Air-Conditioners (RACs). To address this gap, we analysed the energy performance of RACs with both fixed-speed compressors and inverter technology at different set-point temperatures and ceiling fan speed settings in 15 residential Mixed-Mode Buildings (MMBs) in India. Thermal comfort conditions (as predicted by the Indian Model for Adaptive Comfort-Residential (IMAC-R)) with minimum energy consumption were maintained at a set-point temperature (Tset) of 28 and 30 ∘C and a fan speed setting of one. Compared with a Tset of 24 °C, a Tset of 28 and 30 °C resulted in energy savings of 44 and 67%, respectively. With the use of RACs, a configuration with a minimum fan speed was satisfactory for an optimal use of energy and for maintaining the conditions of thermal comfort. In addition, RACs with inverter technology used 34–68% less energy than fixed-speed compressors. With the rising use of RACs, particularly in tropical regions, the study’s outcomes offer a significant potential for reducing space-cooling energy consumption and the resultant greenhouse gas (GHG) emissions.

Low carbon and environmental preservation of residential buildings: MOESOM

Introduction: The crucial transition toward carbon neutrality is developing and adopting low-carbon buildings and communities to achieve the recycling and reuse of resources and to minimize the damage to the natural environment by humans. Energy saving for residential buildings is essential for enhancing cost-effectiveness and redundant energy drain. Considering the increasing attention to energy conservation and the accessibility of sustainable energy sources, common energy-saving solutions expose inherent inadequacies limiting their effectiveness. The ineffectual use of traditional energy sources can result in waste, greater operating costs, and excessive energy consumption in residential structures.Methods: Hence, a Multi-Objective Energy-Saving Optimization Method (MOESOM) has been proposed to optimize energy use and conservation in residential buildings in southern Anhui, China. The proposed approach examines lower operational costs and carbon emissions by using green energy sources and encouraging effective energy consumption habits. The suggested Multi-Objective Energy-Saving Optimization Method technique offers insight into energy saving by utilizing green energy sources and confining energy uses. The multi-objective turns around energy saving and resource usage for decreasing operational costs and averting carbon emissions. Thus, the suggested technique is verified utilizing the Osprey Optimization Algorithm (OOA); the detailed goal is recognized utilizing the multiple objectives described. Based on the progress of low-carbon emissions and energy saving, the number of iterations for augmenting Osprey agents is identified. This agent-based optimization is executed if the novel augmented agent fulfills any of the trailing progression. The emission control level and energy-saving factor are assessed considering the variance between new and old agent progression. This encourages the various objectives to be fulfilled under similar criteria balancing their outcomes.Results and discussion: The output from different Osprey agents is induced for consecutive objectives and optimization factors. Then, the system ensures 8.97% energy savings and 8.04% high objectives compared to the other methods.

Optimal energy use for robot-assisted laparoscopic myomectomy

Myomectomy is a surgical option for patients who wish to preserve fertility when removal of uterine fibroids is necessary. Robot-assisted laparoscopic surgery provides increased surgical dexterity during minimally invasive myomectomy (MIM) and permits greater accuracy with a layered closure of the uterine defect. However, uterine rupture following myomectomy has been reported more frequently with MIM than with laparotomy, including robot-assisted laparoscopic myomectomy (RALM). This article reviews the mechanisms of frequent uterine rupture following MIM and the optimal energy use during RALM.