Reduce Energy Waste Research Articles

Electrochemical treatment of azo dye wastewater by dynamic flow diaphragm system: Response surface methodology optimization and energy consumption analysis

In this experiment, the degradation process of azo dye wastewater was studied based on the diaphragm double chamber electrochemical reactor, taking the mono azo dye amaranth as an example. Through the circulation flow of appropriate volume of wastewater in the cathode and anode chamber, the simultaneous action of cathode and anode was realized, and the degradation and decolorization efficiency of azo wastewater was improved while reducing energy waste. The optimal reaction conditions optimized by response surface methodology were as follows: the voltage was 20.74 V, the plate spacing was 4.89 cm, and the cycle speed was 60.66 rad / min. The optimal decolorization rate was 99.66 %. The energy consumption analysis of the dynamic flow diaphragm system electrochemical method and the static flow diaphragm system electrochemical method for the treatment of azo dye wastewater was compared and analyzed. In addition, the possible degradation process of azo dye wastewater by dynamic flow electrochemistry was inferred by ultraviolet spectroscopy and mechanism prediction analysis. The experimental results show the great potential of the dynamic flow membrane system process in the treatment of azo dyes.

Research on Electricity Balance and Measurement Optimization of New Energy Power System Considering Renewable Energy Consumption Mechanism

With the rapid development of renewable energy, the new energy power system is facing the challenge of large-scale grid connection and consumption of renewable energy. In order to achieve efficient utilization and stable power supply of renewable energy, this study proposes a renewable energy consumption mechanism based on optimization methods. By establishing a power and electricity balance model, consider the relationship between different types of renewable energy generation and electricity demand. Various optimization strategies have been proposed for energy consumption issues in different scenarios, including power generation scheduling, energy storage optimization, and flexible load management. Validate the effectiveness of the proposed mechanism in terms of electricity balance and metering optimization through a model. The experimental results indicate that this mechanism can effectively enhance the renewable energy consumption capacity of the new energy power system, reduce energy waste, promote energy cleanliness and sustainable development, and has certain theoretical and practical significance for promoting the sustainable development of the new energy power system and responding to energy transformation.

Viscous heating and thermal gyration of magneto-micropolar fluid particles through an isothermal porous fixed channel with internal non-uniform heat generation: An analytical investigation

The experimental and theoretical analysis of viscous dissipation and heat generation/absorption management is crucial in engineering, medical, biological, and exploration activities. This study investigates the analytical solution of viscous heating and thermal gyration of magneto-micropolar fluid particles across a resistive medium with internal non-uniform heat generation/absorption. The formulated partial differential equations were appropriately converted into coupled ordinary differential equations employing similarity variables along with the boundary conditions. The simulation of the resultant equations is carried out using the collocating weighted residual scheme, and the result is validated using the shooting technique via the Runge-Kutta method of order four, Adomian decomposition technique, variational iteration scheme, differential transform method, and quasi-linearization technique as the control methods. Tabular and graphical representations are provided to illustrate the flow characteristics. Taking from the results, the existence of a magnetic field retards the gyration of the fluid particle motion. The spin gradient and vortex viscosity terms reveal the reverse occurrence of micro-rotation distribution, and it is seen that the porosity term suppresses the velocity field. Furthermore, it is observed that the parameters that enhance internal heat generation decrease the viscosity of the fluid in the region. Hence, these findings will assist scientists and engineers to effectively manage heat generation/absorption in industries where effective heat transfer activities are crucial to achieving ideal performance and reducing energy waste.

Experimental and numerical investigations with multifunctional heat transfer fluid to evaluate the performance of a thermal energy storage system

In the upcoming decades, concentrated solar power (CSP) technology is expected to be one of the most promising methods of producing electricity.Along with the reduction in energy waste coupled with an increase in efficiency, the thermal energy storage (TES) systems are vital in refining the dependability and efficacy of the energy infrastructure and in reducing gap between supply and demand. This study aims to evaluate a parabolic trough collector by means of experimental investigation and mathematical modelling. Nanofluid is employed as an energy absorption material and transport medium from concentrating tube to storage tank. At consistent flow rate of 3.0 L per minute, a comparative evaluation is conducted between water and ZnS/ water-quantum dots at different volume concentrations (0.1, 0.2, 0.3, and 0.4 %). Heat exchanger-delivered sensible heat, thermal efficiency, heat removal factor, and energetic efficiency are used to examine the qualitative and quantitative performance of the system. The ZnS/ water-quantum dots ratio of 0.3 % (wt%) has been found to yield the maximum thermal and energetic efficiencies. At a solar intensity of 1210 W/m2, the highest thermal efficiency of 57.81 % is reflected by the experimental observations. At the same intensity, there is a 20.63 % increase in the energy efficiency. The charge and discharge of phase-changing material (PCM) are highly influenced by the flow rate of heat transfer fluid (HTF). By increasing the HTF flow rate from 1 to 3 L per minute, the solidification and melting times are shortened by 30 % and 10 %, respectively. This study emphatically reflects a high potential of employing nanofluid in TES to enhance the performance of energy infrastructure coupled with energy waste reduction.

How can the digital economy reduce carbon emissions? Empirical evidence from China.

China is transitioning into the digital economy era. The advancement of the digital economy could offer a fresh mechanism to attain carbon peak and carbon neutrality objectives. Applications of the digital economy, such as smart energy management, intelligent transport systems, and digital agricultural technologies, have significantly reduced carbon emissions by optimizing resource use, reducing energy waste, and improving production efficiency. This research does so by devising a theoretical model that looks into the multi-faceted power of the digital economy under a two-sector paradigm. Utilising a panel model, a mediation effect model and a spatial Durbin model to assess the digital economy's power on carbon emissions. This research has determined that the digital economy can significantly diminish carbon emissions, with green tech innovations and industrial transformation being key contributors. The spatial spillover effect was used for the digital economy to aid in lowering carbon emissions in adjacent districts and upgrading better environmental stewardship. The influence of the digital economy has better performance in lowering carbon emissions in mid-western China than in the eastern area. This paper deepens understanding of the drivers of low-carbon growth and the significance, mechanism and regional disparities of the digital economy's effect on reducing carbon emissions. It offers valuable policy insights and guidance for globally achieving digital economy growth, reducing carbon emissions and reaching carbon peak and neutrality goals.

Research Progress and Visualization Analysis of Spontaneous Combustion of Water-Immersed Coal

ABSTRACT One of the most catastrophic events that can occur during coal mining is a fire triggered by spontaneous combustion of the coal. Due to complex geological conditions, some coal mines in China have the characteristics of shallow burial and small spacing between coal seams. With the increase of mining depth, there will be many cracks in the overlying goaf. Surface water and groundwater flow into the goaf along these fissures. After long-term water immersion, the increase of ground temperature will lead to the spontaneous combustion of coal. A thorough analysis of the impact of water immersion on the spontaneous combustion of coal necessitates a review of the most recent findings in this field. The review will promote the further development of the theory and prevention of spontaneous combustion of water-immersed coal and provide theoretical help for monitoring and preventing spontaneous combustion of water-immersed coal. The creation of water-immersed coal and the reason for spontaneous combustion is investigated, as are the characteristics of the water accumulation in the goaf and their origins. The research status of spontaneous combustion of water-immersed coal is presented and illustrated domestically and internationally. On this basis, the theoretical and experimental research directions, problems, and prospects of spontaneous combustion of water-immersed coal are put forward. This study can provide a theoretical basis for monitoring, preventing, and controlling the spontaneous combustion of water-immersed coal, and provide a reference for reducing energy waste.

Occupancy Estimation from Blurred Video: A Multifaceted Approach with Privacy Consideration

Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings’ energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants’ privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants’ privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants’ visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants’ visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system.

Energy consumption forecast model of CNC machine tools based on support vector regression optimized by improved artificial hummingbird algorithm

With the development of the manufacturing industry, energy consumption is growing rapidly, which makes the energy crisis and environmental problems become more and more serious. CNC machine tools play an essential role and are the primary energy consumption devices in the manufacturing industry. The accurate prediction of machine tool energy consumption can provide support for energy production plans and reduce energy waste. This paper proposes a novel energy consumption prediction model based on support vector regression (SVR) optimized by an improved artificial hummingbird algorithm (IAHA). Firstly, as the artificial hummingbird algorithm (AHA) may easily get trapped in a local optimum, an improved AHA based on chaotic mapping and local backtracking exploitation strategy is proposed. The chaotic mapping is used to initialize individual positions, which is good for maintaining population diversity. The local backtracking exploitation strategy is employed to improve the local optimization ability. The effectiveness and feasibility of the IAHA algorithm have been verified through 12 benchmark functions. Then, the IAHA algorithm is employed to optimize the parameters of the SVR, and the IAHA-SVR energy consumption prediction model is established. Finally, the effectiveness and feasibility of the IAHA-SVR model are verified through a case study.

Load management design and techno-economic analysis for an islanded hybrid Pv-Teg microgrid

Integration of various renewable energy sources (RES) is one of the critical factors behind microgrid (MG) capacity development and MG expansion. Distributed energy resources with and without RES, electrical loads, controllers, and storage units constitute the essential components of MGs. On the other hand, with the addition of different power generation sources, load prediction on the user side of the MG becomes an important issue. Demand response solutions and changing end-user behavior are the other sources of uncertainty in the performance of MGs. Therefore, matching supply and demand is a very important issue that must be done immediately at every moment of the operation. A load management system (LMS) in a community MG is essential to ensure the system’s adaptability. Fuzzy logic (FL) controllers and energy management systems will ensure optimum use of energy resources and efficient operation of MG and reduce energy waste. In this study, an 85 kW photovoltaic (PV) and thermoelectric generator (TEG) hybrid MG system (PVTEG-MG), which can operate as an island grid in all regions of the World with hot water resources, has been designed for the first time and its techno-economic analysis has been made. The state of charge (SOC) for the proposed PVTEG-MG remained stable at 40% charge state by adding charge management. The average value of the Levelized Cost of Energy (LCOE) is $0.456/kWh for PV, $0.456/kWh for TEG, and 0.399/kWh over the total energy produced, 219,000 kWh for the PV and 493,650 kWh for the TEG. Overall, this PVTEG-MG system is a promising solution for hot water regions that can be used as an island grid to reduce electricity costs, increase efficiency and reliability, and provide a more sustainable energy source.

Research on the energy efficiency and sustainability

Mechatronics systems, by integrating mechanical, electronic, and computer technologies, offer a comprehensive solution to optimize the generation, transmission, storage, and utilization of energy, thus reducing energy waste. This paper begins by introducing the applications of mechatronics systems in renewable energy systems, encompassing the efficient capture and management of resources such as solar and wind energy to meet energy demands. Secondly, this paper discusses the critical role of mechatronics systems in smart grids, enhancing the efficiency and reliability of power systems through advanced monitoring and control technologies. Furthermore, this study emphasizes the value of mechatronics systems in designing and implementing energy-saving devices, including high-efficiency motors, intelligent lighting, and heat recovery technologies, to reduce resource wastage and environmental impact. Finally, by reducing carbon emissions and decreasing reliance on traditional energy sources, mechatronics systems actively contribute to sustainability, promoting the development of sustainable cities and buildings, and simultaneously achieving the United Nations Sustainable Development Goals.

A Low-Cost Energy Monitoring System with Universal Compatibility and Real-Time Visualization for Enhanced Accessibility and Power Savings

Energy management is important for both consumers and utility providers. Utility providers are concerned with identifying and reducing energy wastage and thefts. Consumers are interested in reducing their energy consumption and bills. In Pakistan, residential and industrial estates account for nearly 31,000 MW of the maximum total demand, while the transmission and distribution capacity has stalled at about 22,000 MW. This 9000 MW gap in demand and supply, as reported in 2022, has led to frequent load shedding. Although the country now has an excess generation capacity of about 45,000 MW, the aging transmission and distribution network cannot deliver the requisite power at all times. Hence, electricity-related problems are likely to continue for the next few years in the country and the same is true for other low- and middle-income countries (LMICs). Several energy monitoring systems (EnMS) have been proposed, but they face limitations in terms of cost, ease of application, lack of universal installation capability, customization, and data security. The research below focused on the development of an economical, secure, and customizable real-time EnMS. The proposed EnMS comprises low-cost hardware for gathering energy data with universal compatibility, a secured communication module for real-time data transmission, and a dashboard application for visualization of real-time energy consumption in a user-preferred manner, making the information easily accessible and actionable. The experimental results and analysis revealed that approximately 40% cost savings in EnMS development could be achieved compared to other commercially available EnMSs. The performance of the EnMS hardware was evaluated and validated through rigorous on-site experiments. The front-end of the EnMS was assessed through surveys and was found to be interactive and user-friendly for the target clients. The developed EnMS architecture was found to be an economical end-product and an appropriate approach for small and medium clients such as residential, institutional, commercial, and industrial consumers, all on one platform.

AUTOMATIC LABORATORY LIGHTING SYSTEM WITH AN INFRARED BASE

The "Infrared-Based Automatic Laboratory Lighting System" represents a novel way to improve energy efficiency and stoner comfort in lab environments. Conventional laboratory lighting systems often warrant rigidity and efficacy, leading to unnecessary energy usage and inadequate lighting. In order to overcome these obstacles, this design incorporates a smart lighting control system that uses infrared (IR) detectors to identify the presence of mortals inside the laboratory. The primary operation of the system entails placing IR detectors in strategic locations across the lab space. These detectors monitor individuals continuously and relay the data they find to a central control unit that is based on a microcontroller platform. The control unit initiates the activation or adaptation of lighting institutions in the vicinity of detected movement upon detection of a mortal presence. This guarantees that the lighting is optimized for continuous conditioning, providing suitable lighting while reducing energy waste during periods of unoccupied occupancy. Crucial features of the proposed system include real-time responsiveness and rigidity to varying residency patterns. The integration of IR detectors with the microcontroller-grounded control unit enables a nippy and accurate discovery of mortal presence, allowing the system to acclimate lighting situations instantly as individuals enter or leave the laboratory space. Additionally, the system can be configured to generate lighting configurations based on particular circumstances, including ambient lighting for common spaces or task lighting for lab workstations. Apart from enhancing energy efficiency, the method places emphasis on the safety and experience of stoners. The technology lowers the risk of accidents or crimes related to dim illumination by providing appropriate and safe lighting conditions that are tailored to the functional needs of the laboratory. Similarly, the automation of lighting control eliminates the need for handcrafted solutions, allowing laboratory workers to focus on research or experimentation. All things considered, the "Infrared-Based Automatic Laboratory Lighting System" provides stoner-friendly and environmentally friendly outcomes to improve lighting operations in laboratory settings. By promoting energy conservation, perfecting the stoner experience, and ensuring safety, this system contributes to the advancement of effective and sustainable practices in laboratory operations. Keywords: LED (light emitting diode), microcontroller, Internet of Things, sensors, relay mechanism, and LCD (liquid crystal display).

Air leaks fault detection in maintenance using machine learning

PurposeThe objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.Design/methodology/approachEffective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.FindingsBased on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.Practical implicationsEarly air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.Originality/valueTo the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.

Research on short-term load forecasting based on clustering and deep learning

In power systems, power load forecasting is essential to ensure the reliability and efficiency of power supply. Since power load is affected by many factors, including weather, seasonality, and social activities, its patterns and changes are complex and diverse, and traditional forecasting methods may make it difficult to meet demand. In this background, this study combines the K-means clustering algorithm and deep learning model. First, through K-means clustering, we grouped historical load data, dates, and temperatures to identify different load patterns. This grouping helps adapt to different load changes, thereby improving the adaptability of the forecast. Then, a deep learning model is applied, combining a convolutional neural network (CNN) and a two-layer bidirectionally gated recurrent unit (BIGRU). These two models are used to deal with spatiotemporal characteristics and sequence dependence in load data respectively. CNN is used to capture the spatial features in the load data, while BIGRU processes the time series information in the data to effectively capture the complex dynamic nature of the load. K-means clustering information is entered as additional data into the CNN and BIGRU models. The experimental results of the study show that by fully considering different load patterns and data characteristics. Reducing load demand and providing more reliable short-term load forecasts improve the efficiency of the power supply, reduce energy waste, and reduce the burden on the power system.

MMNet-NILM: Multi-Target MobileNets for non-intrusive load monitoring

Energy is a critical resource for daily activities and lifestyles with direct impacts on the economy, health and environment. Therefore, monitoring its efficient use is essential to reduce energy waste and lessen related concerns such as global warming and climate change. One of the prominent and evolving solutions is Non-Intrusive Load Monitoring (NILM) smart meters, which enables consumers to track their per-appliance energy consumption more effectively. Some recent approaches have proposed deep learning as a powerful tool for energy disaggregation. However, it is difficult to employ these models in resource-constrained end devices for effective energy monitoring. In this paper, we explore and evaluate a lightweight improved model for multi-target non-intrusive load monitoring based on MobileNet architectures. With extensive experiments using the ENERTALK dataset, the results show that MobileNetV3-large is the most appealing for energy disaggregation as it requires about 55% less storage for trained model and about 6% less training time than MobileNetV2 with almost the same performance. On average, version 3 large has a 17.63% reduction in SAE and requires 54.21% and 8.93% less space and less training time than version 2, respectively. Moreover, the average performance is boosted using an ensemble multi-target MobileNet model across all houses, leading to significant reduction of MAE, SAE, and RMSE errors of about 6%, 48%, and 4%, respectively. In comparison to other work, the proposed MMNet-NILM shows superior performance for the majority of appliances in terms of all considered evaluation metrics.

An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings

The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES’s parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%.

Flow coupling analysis and evaluation of different working conditions for crude oil storage tanks under dynamic heating

AbstractDuring periods of sharp demand increase, there is a need for significant expansion in the fundamental requirements for crude oil storage. As demand rapidly escalates, the prerequisites for crude oil storage necessitate substantial expansion. To scientifically and effectively reduce energy waste associated with heating oil, it is essential to study the heating methods and efficiency of crude oil storage tanks. Considering environmental temperature, solar radiation, and the physical properties of the oil, we have proposed a new heating model to elucidate the dynamic heating process of large floating roof storage tanks equipped with coils. Computational fluid dynamics (CFD) software was used to analyze the impact of the dynamic heating model on the temperature and velocity fields within the floating roof tank, taking into account different initial oil temperatures, oil levels, wind speeds, and types of crude oil during the winter heating process. Additionally, the research suggests the optimal maintenance heating temperature and duration for crude oil storage tanks during the winter season, introducing heating efficiency and inhomogeneity of temperature field as two evaluation metrics to compare the advantages of the dynamic heating method over traditional heating methods. This study provides fresh insights into the coil heating domain in crude oil storage tanks.

Analysis of safe electricity consumption on load side based on attack and defence game model

AbstractWith the improvement of digitalization and intelligence in the power system, the safe operation of the power system is facing enormous challenges. The safe use of electricity on the load side is the key to achieving safe and reliable power system operation. The detection party needs amounts of human and material resources when the power network is attacked. In response to the current difficulties of low detection ability and high detection costs, this paper proposes an attack and defence game model that considers the differences between different nodes, ensuring the safety and economy of electricity consumption while reducing energy waste. At first, the structure of smart meters and the attack characteristics of intruders are summarized, and a basic attack and defence game model is constructed. The Nash equilibrium is then solved, and the optimal strategy for the game between the defender and the intruder is given to balance the relation between detection performance and energy consumption. In response to the differences generated by each node, strategies for attackers to launch attacks on different nodes and the setting of optimal thresholds for other nodes in the defence system are explored. Finally, case studies verify that the proposed model could reduce the cost of intruder detection while ensuring a specific detection rate.

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

Building Energy Management Systems (BEMS) play a crucial role in enhancing energy efficiency and sustainability in buildings. This abstract provides a comprehensive review of BEMS, focusing on its components, benefits, challenges, and future trends. BEMS is a centralized system that monitors and controls building services, such as heating, ventilation, air conditioning, lighting, and other systems, to improve energy efficiency and occupant comfort. The key components of BEMS include sensors, controllers, communication networks, and user interfaces. These components work together to collect data on building performance, analyze energy consumption patterns, and optimize system operation. One of the primary benefits of BEMS is its ability to reduce energy consumption and costs. By monitoring and controlling building systems based on occupancy schedules and environmental conditions, BEMS can significantly reduce energy waste. Additionally, BEMS can improve occupant comfort and productivity by maintaining optimal indoor environmental conditions. Despite its benefits, BEMS faces several challenges, including high upfront costs, complexity of installation, and integration issues with existing building systems. However, advancements in technology, such as the Internet of Things (IoT) and cloud computing, are addressing these challenges and making BEMS more accessible and cost-effective. Looking ahead, the future of BEMS is promising, with continued advancements in technology driving its adoption and integration into smart building systems. The integration of artificial intelligence and machine learning algorithms into BEMS is expected to further improve its energy-saving capabilities and enhance building performance. In conclusion, BEMS is a key technology for improving energy efficiency and sustainability in buildings. By leveraging its components and capabilities, BEMS can help reduce energy consumption, lower costs, and create healthier and more comfortable indoor environments.

Performance analysis and multi-objective optimization of the offshore renewable energy powered integrated energy supply system

Offshore renewable energy technologies offer a promising prospect for energy supply on offshore islands. However, traditional wind and solar energy sources exhibit strong variability and cannot fully meet the energy demands of users. At the same time, the energy demand of users also has great volatility, so the supply side and demand side of energy cannot match well. Existing renewable energy systems often utilize thermal power generation as a stabilizer for the system, however, this approach is difficult to apply in offshore conditions. This study introduces an innovative multi-energy complementary system that integrates seawater freezing desalination, refrigeration and power-hydrogen-ammonia co-generation, driven by ocean thermal energy, wind energy, and solar energy. The system incorporates the use of a new developed small temperature difference ejector refrigeration cycle to stabilize the entire system and produce low-temperature refrigeration. By utilizing the seawater freezing desalination process to minimize energy conversion losses and balancing fluctuations in multiple energy inputs and user demands through ammonia-hydrogen co-production, this approach effectively minimizes energy waste, enhances energy utilization efficiency, and reduces excess power handling. The purpose is to optimize the configuration of the energy conversion subsystem within the multi-energy complementary multi-effect co-generation system, thus improving the economic feasibility and reliability of the system in providing energy and freshwater supply, ultimately achieving the goals of reducing energy waste, achieving high conversion efficiency, and minimizing equipment investment. This study conducts an economic analysis and optimization of a multi-energy complementary system, utilizing a 2 MW-level OTEC subsystem as a stabilizer. Both equipment investment and operating costs are considered in the economy analysis. The carbon emission reduction performance of this system is evaluated. The performance optimization results show that the system achieves an energy storage efficiency of 65.96 %, and the curtailment rate of this system is only 3.99 %, significantly lower than the 10 % curtailment rate in traditional system. The proposed system can meet the annual electricity demand of 33.09 million kWh, as well as a cooling demand of 546.81 million kWh. Under typical conditions, the ocean thermal energy conversion cycle exhibits a power efficiency of 0.85 % and a refrigeration efficiency of 29.10 %. Meanwhile, it annually reduces CO2 emissions by 2.54 million tons, demonstrating significant environmental benefits. This proposed system is mainly applicable to tropical waters with high surface water temperatures and depths of no less than 600 m worldwide. This study offers a practical and economically beneficial solution for energy and freshwater supply on offshore islands.