Reduce Energy Waste Research Articles

Effects of dynamic characteristics of a pipe network on indoor temperature of households

District heating has always been regarded as a convenient and environmentally friendly heating method. However, the district heating process involves a considerable amount of wasted energy. To improve the operational strategies of heating networks and heating quality, reduce energy waste, promote the sustainable development of resources and cities, it is necessary to investigate the dynamic characteristics of pipe networks. This paper describes the dynamic characteristics of a pipe network in which the intermittent operation of the primary pipe network were adjusted. The effects of the dynamic characteristics of the pipe network on the indoor temperature of households are numerically analyzed. When the primary network stops and the secondary network continues working, the indoor temperature begins to decrease, slowly at first, and then more quickly. The indoor temperature of households in the system remains unchanged for a short period before starting to drop with the increase of the time. It takes about 150–245 min for the indoor temperature in the heating system to drop to 16°C. The smaller the room area, the larger the area of the envelope structure, and the faster the room temperature drops. The higher the intermittent heating temperature, the longer it takes for the indoor temperature to drop to 16°C. An intermittent heating temperature of 5°C or above ensures that every household in the heating system retains an indoor temperature above 16°C after 2 h.

Energy management system in smart buildings based coalition game theory with fog platform and smart meter infrastructure

This paper proposes a central energy management system (EMS) in smart buildings. It is based on the coalition method for optimal energy sharing between smart buildings. Game theory is applied to obtain an optimal allocation of the building's surplus energy on the deficient energy buildings using the Shapley value, which enables the unequal energy distribution based on the energy demand. The main objective is reducing energy waste while preserving the generation/demand balance. The fog platform with memory storage is applied, which handles all the measured data from the smart buildings through Wi-Fi-based communication protocol and performs the EMS program. The smart meter links the smart buildings with the fog-based EMS central unit. Two scenarios are implemented based on the difference between total deficient and surplus energy. Coalition game theory is applied for optimal surplus energy allocation on deficient buildings when the total energy surplus is lower than the total energy deficient. Also, there is a one-to-one relationship between the surplus and deficient building; if the surplus energy is larger than the deficit, the extra surplus energy is stored for further usage. The proposed EMS is applied and tested using a smart city with 10 buildings in the MATLAB program. A comparison between the result obtained with and without applying the proposed method is performed. The performance of the fog platform is introduced based on the run and delay time and the memory size usage. The results show the effectiveness of the proposed EMS in a smart building.

Tribological Behavior of Bioinspired Surfaces

Energy losses due to various tribological phenomena pose a significant challenge to sustainable development. These energy losses also contribute toward increased emissions of greenhouse gases. Various attempts have been made to reduce energy consumption through the use of various surface engineering solutions. The bioinspired surfaces can provide a sustainable solution to address these tribological challenges by minimizing friction and wear. The current study majorly focuses on the recent advancements in the tribological behavior of bioinspired surfaces and bio-inspired materials. The miniaturization of technological devices has increased the need to understand micro- and nano-scale tribological behavior, which could significantly reduce energy wastage and material degradation. Integrating advanced research methods is crucial in developing new aspects of structures and characteristics of biological materials. Depending upon the interaction of the species with the surrounding, the present study is divided into segments depicting the tribological behavior of the biological surfaces inspired by animals and plants. The mimicking of bio-inspired surfaces resulted in significant noise, friction, and drag reduction, promoting the development of anti-wear and anti-adhesion surfaces. Along with the reduction in friction through the bioinspired surface, a few studies providing evidence for the enhancement in the frictional properties were also depicted.

Discussion on key technologies of big data in financial budget performance management in low-carbon economy

Reducing energy consumption, pollution and waste emissions are the basic elements of the concept of low-carbon economy. There is an inseparable link between low-carbon economy and financial work in enterprises. In terms of financial work, it is an indispensable element in the development of enterprises. In order to help enterprises improve their financial performance with pertinence, comparability and applicability, this paper selects clear enterprise performance evaluation indicators for analysis from the financial perspective. It aims to help enterprises save energy in a low-carbon economic environment. In this study, the performance evaluation system and financial allocation method of Anhui enterprises’ financial expenditure are studied. In the empirical analysis, 10 ordinary enterprise undergraduate colleges in Anhui Province are selected as samples. This model covers the performance evaluation scope of most financial expenditures of general enterprises in Anhui Province, and is analyzed in the process of DEA model analysis. In the process of DEA model analysis, it can better explain the input-output performance of Anhui enterprises. In this paper, financial expenditure performance evaluation indicators designed based on principal component analysis, data envelopment analysis and other analysis methods can focus on reflecting the input and output of enterprises. It can realize the standardization of evaluation results and better compare the efficiency of financial capital expenditure between enterprises. In other words, the concept of low carbon in enterprises has given a certain standard for the development of financial work. The financial department of the enterprise must fully implement the concept of low carbon during the budget period. Only in this way can we effectively promote the development of enterprises towards low-carbon and environmental protection. Finally, it will lay a solid foundation for the sustainable development of the enterprise.

Anomaly detection based on joint spatio-temporal learning for building electricity consumption

The use of electric energy is an integral part of people's daily life. Anomaly detection of electricity consumption data, as a classification problem, has always been a hot research topic of scholars. Anomaly detection can not only reduce energy waste, but also prevent small problems from becoming overwhelming problems. At present, most anomaly detection algorithms mainly focus on the time series information of electricity consumption data, while ignoring the spatial feature of electricity consumption data. To fill this research gap, the paper proposes an Anomaly Detection based on Joint Spatio-Temporal learning (ADJST) method for building electricity consumption. First, a Multi-Scale Graph Convolutional Network (MS-GCN) is proposed to learn the spatial features of building electricity consumption data. Specifically, two types of graphs are constructed to extract short-term correlation features and long-term regularity features of building electricity consumption data. Second, a Multi-Scale Temporal Convolutional Network (MS-TCN) is proposed to learn the temporal features of building electricity consumption data. Adopt a multi-scale vanilla convolution structure to extract multi-scale time series information from building electricity consumption data. Third, the combination of temporal features and spatial features detects anomalous electricity consumption of marked users. Final, taken the user electricity consumption data collected by the State Grid Corporation's smart meter as examples, compared with a variety of classical anomaly detection algorithms, the results of F1-score and AUC of the proposed method are 0.935 and 0.977 respectively, which proves the superiority of the method. The model shows good stability in dealing with extreme imbalance of data, and is proved to be generalized by experiments and can be transferred to other datasets.

High-Accuracy and Low-Latency Tracker for UAVs Monitoring Tibetan Antelopes

As the habitat areas of Tibetan antelopes usually exhibit poaching and unpredictable risks, combining target recognition and tracking with intelligent Unmanned Aerial Vehicle (UAV) technology is necessary to obtain the real-time location of injured Tibetan antelopes to better protect and rescue them. (1) Background: The most common way to track an object is to detect each frame of it, and it is not necessary to run the object tracker and classifier at the same rate, because the speed for them to change class is slower than objects move. Especially in the edge reasoning scene, UAV real-time monitoring requires to seek a balance between the frame rate, latency, and accuracy. (2) Methods: A backtracking tracker is proposed to recognize Tibetan antelopes which generates motion vectors through stored optical flow, achieving faster target detection. The lightweight You Only Look Once X (YOLOX) is selected as the baseline model to reduce the dependence on hardware configuration and calculation cost while ensuring detection accuracy. Region-of-Interest (ROI)-to-centroid tracking technology is employed to reduce the processing cost of motion interpolation, and the overall processing frame rate is smoothed by pre-calculating the motions of different objects recognized. The On-Line Object Tracking (OLOT) system with adaptive search area selection is adopted to dynamically adjust the frame rate to reduce energy waste. (3) Results: using YOLOX to trace back in the native Darkenet can reduce latency by 3.75 times, and the latency is only 2.82 ms after about 10 frame hops, with the accuracy being higher than YOLOv3. Compared with traditional algorithms, the proposed algorithm can reduce the tracking latency of UAVs by 50%. By running and comparing in the onboard computer, although the proposed tracker is inferior to KCF in FPS, it is significantly higher than other trackers and is obviously superior to KCF in accuracy. (4) Conclusion: A UAV equipped with the proposed tracker effectively reduces reasoning latency in monitoring Tibetan antelopes, achieving high recognition accuracy. Therefore, it is expected to help better protection of Tibetan antelopes.

Combining Urban Fleet Vehicle Operation with Reducing Energy Wastage in Light Rail Systems

Fleet electric vehicles offer excellent potential for vehicle-to-grid operation, as a result of their predictable use patterns. This paper investigates the potential for linking up urban fleet electric vehicles with wasted energy from urban light rail networks. Vehicle-to-grid charging could be deployed to reduce energy wastage from regenerative braking, while serving as temporary energy storage on the light rail system to reduce energy requirements during acceleration and providing an energy supply for fleets of electric vehicles based in urban areas. This paper uses GPS data from real light rail journeys to estimate regenerative braking energy availability for Edinburgh's light rail network. Findings indicate that electric vehicle charging linked to Edinburgh's light rail network would be beneficial for both parties.

Smart Grid Management System Based on Machine Learning Algorithms for Efficient Energy Distribution

This abstract describes the smart grid management system is an emerging technology that utilizes machine learning algorithms for efficient energy distribution. The paper presents an overview of the architecture, benefits, and challenges of smart grid management systems. The paper also discusses various machine learning algorithms used in smart grid management systems such as neural networks, decision trees, and Support Vector Machines (SVM). The advantages of using machine learning algorithms in smart grid management systems include increased energy efficiency, reduced energy wastage, improved reliability, and reduced costs. The challenges in implementing machine learning algorithms in smart grid management systems include data security, privacy, and scalability. The paper concludes by discussing future research directions in smart grid management systems based on machine learning algorithms.

ПОВЫШЕНИЕ ЭФФЕКТИВНОСТИ ЭЛЕКТРОСНАБЖЕНИЯ СИСТЕМ ТРАНСПОРТА НЕФТИ НА ОБЪЕКТАХ НЕФТЕДОБЫЧИ

The article examines the most important aspect of improving the efficiency of power supply of oil transportation systems at oil production facilities, and also examines the existing infrastructure, identifies bottlenecks and areas of inefficiency. Various solutions are proposed to improve efficiency, including the integration of renewable energy sources, the introduction of energy storage systems, the introduction of smart grid technologies, the optimization of energy infrastructure design, the implementation of energy efficiency measures, and the digitalization and analysis of data. Ву implementing these solutions, oil companies can reduce energy wastage, improve reliability and improve overall operational efficiency. The article highlights the importance of implementing sustainable and efficient practices in the oil industry to optimize energy supply and ensure uninterrupted oil transportation.

Design Optimization of Standalone PV-Battery System for LED Street Lighting in Magetan Indonesia

The study focuses on optimizing standalone PV-battery systems for LED street lighting in Magetan Regency, Indonesia, emphasizing the importance of PV panels and lithium battery selection for energy efficiency. Minimizing energy not captured leads to effective, sustainable, and eco-friendly street lighting, reducing energy waste and promoting a greener urban environment. The optimization process involves choosing the appropriate PV panel size, battery capacity, and solar charge controller to maximize energy capture while meeting load requirements and design constraints. Solar resource assessment using PVGIS ensures system feasibility analysis. Simulation results reveal optimized PV panel peak power (200 Wp), battery capacity (1002 Wh), and a 16.67A MPPT solar charge controller. The average energy not captured is 144.21 Wh per month, showcasing the system’s efficiency. The discussion emphasizes the significance of this optimized configuration in enhancing energy capture and storage capacity, resulting in reliable and eco-friendly street lighting solutions.

Simulation analysis of tube-shell heat exchanger

With the continuous development of social economy, the heat exchanger in refrigeration equipment is also changing with each passing day. However, the traditional heat exchanger has some problems, such as low utilization rate of heat exchange tubes, low work efficiency, and large energy waste. In view of the above problems, we have carried out a series of optimization designs: for the fluid in the tube side, we have used the U-shaped heat exchange tube to increase the distance of the fluid from the inlet to the outlet, and made full use of all the heat exchange tubes by using the baffle double compartment, which has reduced the phenomenon that the heat exchange tubes do not go through the fluid in the traditional heat exchanger; For the air fluid in the shell side, we have added two-thirds of baffle plates in the shell side. The baffle plates are sleeved on the heat exchange tubes and are located in the middle of the inlet and outlet (not more than the inlet and outlet). Six baffle plates are used to change the path of fluid flow in the shell side, slow down the fluid speed in the shell side, make their time from the flow inlet to the flow outlet increase significantly, thus making the heat exchange time longer, The heat exchange efficiency increases. After our analysis and design, compared with the traditional heat exchanger, my new shell and tube heat exchanger has higher work efficiency, greatly improved heat transfer efficiency, and significantly reduced energy waste.

IOT-Based Energy Monitoring System for Energy Conservation

The aim of this project is to develop an Internet of Things (IoT) based Energy Monitoring System to reduce energy waste and cutting down energy cost. Therefore, it needs a system which provide efficient energy consumption management. For the starting of designing an IoT-based Energy Monitoring System, different sensor and calculated AC measurement methods were studied. The development of this system is to integrate each of the sensor, energy measuring device and IoT system into one complete module. The concept of Wireless Sensor Network (WSN) was implemented in this project. The WSN obtained data information from sensor and send them to the cloud through the IoT network for cloud storage of the ThingSpeak platform. The system contains two input signal which is voltage and current. The device that transfers the data or information from the energy monitoring device to the cloud storage is the ESP8266 Wi-Fi module. The output is the energy consumption that has been used according to the real-time data measurement. Lastly, the data that is transmitted to the cloud can be monitored through the mobile application ThingView. In conclusion, this system is necessary because it can control and manage energy consumption to avoid wastage and promote energy conservation.

A cost-efficient DC active load laboratory solution

The increased use of DC renewable energy resources and DC storage systems, combined with the necessary reduction of energy waste, is boosting the development of DC smart grids. In this scenario, DC load emulation is of great importance. From the hardware point of view, DC buses stability of smart grids and the different DC/DC converter topologies must be tested. From the software point of view, smart grid strategies and job schedulers must be tested with different power absorption profiles. Moreover, DC load emulation can be useful for many other purposes, such as battery characterization, power supply testing, photovoltaic I-V curve measurements, etc. In this work, a cost-efficient DC Active Load (AL) solution is proposed. The principle of the circuit topology is a buck-boost-derived converter. This solution can be designed and tested considering the required voltage, current, and maximum input power. Both simulation and experimental results are shown on a 400 W size prototype. Thermal and electrical results validate the simulation model and the AL feasibility.

Composites with a Novel Core–shell Structural Expanded Perlite/Polyethylene glycol Composite PCM as Novel Green Energy Storage Composites for Building Energy Conservation

Phase change materials (PCMs) in the thermal storage of construction can reduce energy waste by shrinking the diurnal daily temperature changes inside. The thermal energy storage (TES) wood-plastic composites (WPC) are manufactured by employing expanded perlite (EP) stabilized PEG as PCM and wood powder/high-density polyethylene (WF/HDPE) as a matrix. The novel type of shell-core PCM (E-shell PCM) was prepared through cation exchange and layer upon layer self-assembly built organic network structure, which was featured by the very thin shell and ultra-high content of active core materials. The unique protective shell gave the PCM high latent heat potential (136.40 J/g), good structural stability, and broad prospects for sustainable energy application. The TES WPC has excellent morphological stability, high phase transition heat and significant thermal stability, heat storage performance, and good mechanical strength. With the gradual increase of E-Shell PCM, the latent heat of TES WPC phase transition increases gradually, and the melting enthalpy and crystallization enthalpy reach 76.06 J/g and 74.61 J/g, respectively. Simultaneously, the introduction of EP promoted the composite heat and smoke suppression ability, suggested as a building material used for temperature control. Latent heat WPC may be utilized as biological building materials with high latent heat and strong mechanical properties to cut down on energy use and enhance interior comfort.

Comprehensive Analysis for State-of-the-Art Adsorption Refrigeration Technologies

With the development of science and technology, society needs more and more energy currently, and the energy utilization efficiency has become a topic that cannot be neglected. In many cases, it is still a common phenomenon in society that a large amount of energy is wasted, which may cause more fossil energy to be burned and the greenhouse effect to be strengthened. In addition to directly improving the utilization rate of energy, we can also reduce energy waste through the recovery and reuse of waste energy. Thermal energy sorption refrigeration technology, divided into adsorption refrigeration technology using solid adsorbent and absorption refrigeration technology using liquid absorbent, could utilize waste energy or low-grade heat sources for refrigeration. Through the introduction of adsorption refrigeration technology, the research and analysis of the application of solid adsorption refrigeration technology in industry, vehicles, ships and buildings, this paper introduces the basic principle and current situation of this technology, summarizes the advantages, disadvantages and development direction of adsorption refrigeration technology, and provides reference for those who do not comprehend this technology or have doubts about the scope of its use.

Frequency regulation in adaptive virtual inertia and power reserve control with high PV penetration by probabilistic forecasting

The large-scale deployment of sustainable energy sources has become a mandatory goal to reduce pollution from electricity production. As photovoltaic (PV) plants replace conventional synchronous generators (SGs), their significant inherent rotational inertia characteristics are reduced. The high penetration of PV results in reduced system inertia, leading to system frequency instability. Virtual inertial control (VIC) technology has attracted increasing interest because of its ability to mimic inertia. Adoption of the energy storage system (ESS) is hindered by the high cost, although it can be used to provide virtual inertia. The determined forecast gives PVs the ability to reserve power before shading and compensate the power when a system power drop occurs, which can increase system inertia. Nevertheless, it has forecast errors and energy waste in a stable state. To improve the stability of the microgrid and improve the ESS efficiency, this study proposes an adaptive forecasting-based (AFB) VIC method using probabilistic forecasts. The adaptive power reserve and virtual inertia control are proposed to reduce energy waste and increase system inertia. The simulation results reveal that the proposed method has adaptive system inertia to reduce the reserved power, required ESS power capacity, and battery aging.

Experimental Validation of an Enhanced MPPT Algorithm and an Optimal DC–DC Converter Design Powered by Metaheuristic Optimization for PV Systems

Nowadays, photovoltaic (PV) systems are responsible for over 994 TWH of the worldwide energy supply, which highlights their relevance and also explains why so much research has arisen to enhance their implementation; among this research, different optimization techniques have been widely studied to maximize the energy harvested under different environmental conditions (maximum power point tracking) and to optimize the efficiency of the required power electronics for the implementation of MPPT algorithms. On the one hand, an earthquake optimization algorithm (EA) was introduced as a multi-objective optimization tool for DC–DC converter design, mostly to overcome component shortages by optimal replacement, but it had never been tested (until now) for PV applications. On the other hand, the original EA was also taken as inspiration for a promising EA-based MPPT, which presumably enabled a solution with simple parametric calibration and improved dynamic behavior; yet prior to this research, the EA-MPPT had never been experimentally validated. Hence, this work fills the gap and provides the first implementation of the EA-based MPPT, validating its performance and suitability under real physical conditions, where the experimental testbed was optimized through the EA design methodology for DC–DC converters and implemented for the first time for PV applications. The results present energy waste reduction between 12 and 36% compared to MPPTs based on perturb and observe and particle swarm optimization; meanwhile, the designed converter achieved 7.3% current ripple, which is between 2.7 and 12.7% less than some industrial converters, and it had almost 90% efficiency at nominal operation. Finally, the EA-MPPT proved simple enough to be implemented even through an 8-bit MCU (ATmega328P from Arduino UNO).

The effect of normative-based feedback messaging on room air conditioner usage in university dormitory rooms in winter season

Reducing energy waste in building operation phases is a crucial approach to mitigating global warming. Maintaining a comfortable indoor thermal environment is considered one of the major contributors to the final building energy consumption, largely influenced by actual occupant behaviours. Normative-based feedback messaging containing social comparison-based energy feedback is able to impact energy behaviour at homes. However, its real-world effectiveness on usage of room air conditioners (RACs) and their energy consumption together with corresponding impact on occupants’ subjective thermal evaluation has been rarely investigated. This study reports results of a preliminary investigation into the effect of feedback messaging on occupants’ RAC usage and thermal evaluations in a real-world residential setting, the factors that influence the effectiveness of feedback messaging, and the attitudes towards feedback messaging. A three-week field investigation was conducted with 101 subjects from 32 university dormitory rooms. The results suggest that messaging may potentially reduce RAC energy consumption while thermal sensation votes were mostly remained within the acceptable range, though no significant difference was found in comparison to periods without messaging. Meanwhile, the effect of feedback messaging on RAC usage demand was potentially impacted by people’s emotional stability, but a further investigation is recommended to confirm this finding.

A literature review of energy waste in the manufacturing industry

In order to respond to climate change, different governments set their own carbon neutrality targets by considering their own realities. Under such a circumstance, manufacturing companies are pressured to improve their energy performance so that their carbon emissions can be mitigated. Reducing energy waste from production systems is therefore critical. Many studies refer to energy waste, but few have illustrated the characteristics of energy waste, resulting in difficulty for those practitioners to continuously identify and eliminate such energy waste. This study conducts a literature review, covering publication from 1976 to February 2022, to propose narrow and broad definitions of energy waste to reduce or even eliminate energy waste, as well as a theoretical framework. A co-word-based thematic analysis and a co-citation analysis are conducted to understand the contents of energy waste and explore the links with existing energy-related concepts. The results reveal that many efforts have been made to reduce unnecessary energy consumption while there is no clear definition of energy waste, and the term “energy waste” has not been commonly recognized in the literature. Most of the previous studies proposed to reduce unnecessary energy consumption through management techniques, while in the meantime, digital technologies can facilitate such management. Both narrow and broad definitions of energy waste are proposed based on the findings, which contain management and technology perspectives. Design for energy waste elimination is further introduced. Aligning with the socio-technology system theory, we develop a theoretical framework that aims to comprehensively reduce energy waste. Future research directions include further understanding energy wastes by conducting case studies and developing a more detailed framework on energy waste.

Will energy efficiency improvements reduce energy consumption? Perspective of rebound effect and evidence from beijing

Improving energy efficiency is one of the most effective ways to reduce energy waste and achieve carbon neutrality, but it may also bring the energy rebound effect and thus fail to achieve the desired energy conservations. In this paper, we examine the energy rebound effect of Beijing at the level of overall economy, industry and sector based on the Cobb-Douglas production function and the idea of Solow residual, so as to clarify the true impact of technological progress on energy-saving target. Results suggest that: (1) The overall economy of Beijing shows partial rebound effect in most years and appears backfire effect in some years, but the degree of the rebound effect has been gradually mitigated; (2) The energy rebound degree of the tertiary industry is higher than that of the secondary industry on the whole, but the rebound effect of the secondary industry fluctuates more obviously; (3) Energy savings at the sector level is also not ideal, and in most cases, sectors with low energy efficiency have a higher rebound effect than sectors with high energy efficiency. Based on these results, suggestions are provided to alleviate the energy rebound effect of Beijing.