Analysis Of Energy Consumption Research Articles

Analysis of Energy Consumption and Economy of Regional Gas Tri-Supply Composite System

With the development of Chinese society, there is an increasing demand for emissions reduction and the stable operation of the power grid. Regional comprehensive energy supply systems have entered the public’s view owing to their advantages of reducing capacity, unified dispatch, improving efficiency, and reducing energy consumption. This paper focuses on a system under construction in Chongqing, which adopts a combined gas tri-supply (combined cooling, heat, and power, CCHP) and dynamic ice storage cooling system as the research object. By establishing a mathematical model for the simulation research, this study examines the start–stop priority sequence of the gas tri-supply subsystem and the heat pump subsystem under the ice storage priority strategy in winter and summer and proposes corresponding optimization solutions. By comparing the annual operating energy consumption of the system, we conclude that the gas tri-supply composite system has good economic efficiency and peak-shaving capability, indicating that regional gas tri-supply composite systems have great application potential in the future. The proposed optimized operation strategy and simulated energy consumption calculation provide theoretical guidance for the construction and operation of both this project and similar projects.

Smart Power Monitoring System Using IoT

The "Smart Power Monitoring System using IoT" constitutes a pioneering solution poised to revolutionize the landscape of power management in both residential and industrial settings. Anchored by the ESP8266 NODEMCU microcontroller, this system seamlessly integrates an array of state-of-the-art sensors and modules, all working in concert to deliver real-time monitoring, controlling capabilities. The predominant objective of this innovative system is multifaceted: to elevate energy efficiency, bolster security measures and enable remote monitoring through the dynamic fabric of the IoT. The ESP8266 NODEMCU, functioning as the system's central hub, orchestrates a symphony of components, including a current sensor for precise power consumption insights and a PIR sensor to intelligently detect human motion for enhanced automation. The inclusion of a GSM module ensures timely communication of critical alerts to users via SMS, underscoring the system's responsiveness. Additionally, a relay module facilitates the remote control of appliances, promoting user-friendly power management. A half-horsepower induction motor acts as a tangible load indicator, providing users with a visual representation of power fluctuations. The system, powered by a stable 9V 1 Amp DC supply, culminates its capabilities by leveraging the “ThingSpeak” platform to graphically display power consumption trends, empowering users to make informed decisions for sustainable and efficient energy practices. In essence, the "Smart Power Monitoring System using IoT" emerges as a comprehensive and forward-thinking solution, poised to redefine the paradigm of intelligent power management. Keywords: IoT technology, real-time monitoring, energy consumption analysis.

Sustainable Energy Consumption Analysis through Data Driven Insights

Energy is the backbone of our society, supporting our daily activities and driving progress. It plays a crucial role in shaping our modern way of life. The future of global energy consumption is influenced by many factors, including demographics, economic dynamics, technological developments, political actions, environmental demands and geopolitical considerations. As the world's population continues to grow and urbanize, the demand for energy is increasing. At the same time, rapid technological innovations are shaping the energy landscape and changing production, distribution and consumption patterns. In the midst of this development, it is very important to optimize energy consumption, accurately anticipate needs, curb climate change, limit emissions of greenhouse gasses, fight against pollution and promote sustainability. This study includes an in-depth analysis of historical consumption trends, assessing the multiple benefits of renewable energy integration, estimating carbon emissions, formulating practical policy recommendations and providing empirically informed insights. The work is based on various data obtained from platforms such as Kaggle and using advanced visualization techniques such as Power BI dashboards. The study provides invaluable perspectives on future energy needs, the penetration of renewable sources into the energy mix, and the strategic needs to achieve sustainable energy use.

Theoretical Energy Consumption Analysis for Sustainable Practices in Iron and Steel Industry

Theoretical Energy Consumption Analysis for Sustainable Practices in Iron and Steel Industry

Analysis of Internal Conditions and Energy Consumption during Winter in an Apartment Located in a Tenement Building in Poland

The residential sector of existing buildings has great potential in energy savings and the improvement of indoor conditions. The modernization of buildings is of particular concern to the policies of the European Union, local governments, and building users. The aim of this paper is to present an analysis of indoor parameters and energy consumption for heating for an apartment located in a pre-war tenement building before and after thermomodernization. The analysis was conducted for winter conditions and was based on measurements and simulations. Originally, the building had not undergone any thermomodernization actions since its reconstruction after WWII. Interior, exterior, and surface temperatures were recorded to describe the thermal conditions of the apartment, while gas meter readings were used to estimate energy consumption for heating purposes. WUFI Plus software (v.3.2.0.1) was used to estimate energy consumption and perform energy simulations for the apartment over an extended period of time. The best thermomodernization effect resulted from the replacement of windows and the inefficient heating system, avoiding surface condensation and reducing final energy consumption by more than 50%. The extended options resulted in energy savings higher than 70%. The presented analysis shows the importance of retrofit measures and proves that even a small improvement can bring significant benefits.

A Review on Physical Hydrogen Storage: Insights into Influencing Parameters, Energy Consumption, Global Outlook and Bibliographic Analysis.

The transition of the global energy market towards an environment-friendly, sustainable society requires a profound transformation from fossil fuel to zero carbon emission fuel. To cope with this goal production of renewable energy is accelerating worldwide. Hydrogen is a clean energy carrier, due to its clean combustion and abundance. Nonetheless, its storage is a critical challenge to its success. Hydrogen must be stored long after being produced and transported to a storage site. Physical hydrogen storage (PHS)is vital among hydrogen storage modes, and its shortcoming needs to overcome for its successful and economic benefits. This review intends to discuss the techniques and applications of physical hydrogen storage in the state of compressed gas, liquefied hydrogen gas, and cold/cryo compressed gas concerning their working principle, chemical and physical properties, influencing factors for physical hydrogen storage, and transportation, economics, and global outlook. In addition, insights of several probable PHSsystems are highlighted. The outcomes of this review envisioned that the PHS still necessitates technological advancements despite having remarkable success. The limitation opens the door to further research, which would be helpful for efficient and long-term physical hydrogen storage.

Analyzing Energy Consumption in Universities: A Literature Review

This study investigates energy consumption in general and in universities in particular. A literature review was used as the methodology of the study to show the energy consumption purposes, major findings, research gaps, limitations, and future research about how to analyze energy consumption levels. The number of references examined was 60, with 29 references about universities and some of them are concentrating on the buildings’ consumption in the GCC area. Most of the references concentrated on investigating case studies in different countries. Analysis of the findings showed that tools such as big data analytics, machine learning, simulations, and predictive models were used. However, big data analytics was the most important one. It was found that AC systems are the most important component of energy consumption, especially in hot weather countries. The studies also showed that monitoring the consumption rates will reduce the total consumption of energy. In universities, the type of the building and the day index were found the most important factors affecting energy consumption. Factors such as number of smart devices, location, and floor space were found to have different positive and negative impacts on energy consumption from one study to another. Recommendations for future research were also presented.

Experimental platform for studying energy regeneration in electric vehicle powertrains

AbstractInvestigation into the energy consumption in electric vehicles (EVs) plays a pivotal role in determining their autonomy and assessing the electric system performance across diverse operational scenarios. This study focuses on the concept of energy regeneration, encompassing the recovery and storage of kinetic mechanical energy during braking or descent in EVs. Employing control systems in power electronics becomes necessary to establish a seamless workflow across operational quadrants to ensure efficient energy regeneration in an electric machine functioning as both a motor and a generator. To seamlessly integrate new technologies into practical applications, it is essential to conduct thorough evaluations in laboratories prior to deployment. This paper introduces an experimental platform specifically designed to analyze energy consumption and storage in EVs by emulating their powertrains in a controlled laboratory environment. The platform comprises key components for emulating the powertrain of a single-motor electric vehicle with single-axle traction, including a power converter configured in two quadrants, an energy storage system, a primary rotating electric machine, and a mechanically coupled point load torque (another motor). This paper provides a detailed guide on implementing such a laboratory and for facilitating the testing of diverse motor technologies and controllers under varied operational conditions. This comprehensive approach allows for the assessment of electromechanical system efficiency, focusing on both energy recovery and comprehensive control of electric power converters. Validation tests conducted under urban conditions and on steep terrains demonstrate the effectiveness of the platform in analyzing the energy efficiency of both the induction machine and the power controller.

Intelligent Event-Based Lip Reading Word Classification with SNN Using Spatio-Temporal Attention Features and Triplet Loss

Lip reading is a visual alternative to enhance the intelligence of traditional speech recognition, which can benefit from retina-like event cameras that focus on dynamic movements. Spiking Neural Networks (SNNs) are inherently well-suited to cooperate with event cameras. However, employing SNN for event-based lip reading presents significant challenges, particularly in effectively extracting spatio-temporal features from input events and distinguishing between phonetically similar elements. To address these challenges, this paper proposes a novel event-based lip-reading model that leverages the SNN framework, enriched by a designed Spatial-Temporal Attention Block (STAB) and a triplet loss. Specifically, STAB comprises both spatial and temporal attention branches, dynamically emphasizing those spatial and temporal characteristics most reflective of lip movements. STAB also includes a fusion mechanism to integrate these spatial and temporal insights, providing a comprehensive and focused representation of lip movements. In addition, we enhance the model by incorporating triplet loss into the SNN training process, further improving the model's ability to distinguish between visually similar words. Experimental results show the superior performance of our SNN and validate the effect of STAB and triplet loss. We also conduct the energy consumption analysis to affirm the model's energy efficiency.

Analyzing energy consumption due to occupant interaction with manual and automatic window blinds in multiple climates

Proper daylight management through automated or manual blind systems can enhance user comfort and energy conservation. Furthermore, the influence of weather conditions, coupled with occupant behavior, significantly impacts energy consumption, rendering weather forecasts invaluable in conducting individual building energy simulations to identify energy-efficient strategies for the future. Also, I wonder if it is possible to move some items from room 308 until you can provide items from other resources. Future weather conditions and their impact on building performance can be evaluated using Representative Concentration Pathways (RCP) scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). This research aimed to compare the energy usage of manual and automated window blinds with internal blinds in mid-size office buildings across four main climate zones (2B Hot-Dry; 2A Hot-Humid; 7 Very Cold; 4A Mixed-Humid) in the USA under current and future climate change scenarios (RCP 4.5, 8.5). Utilizing simulation as the methodology and comparing the energy use resulting from the RCP 4.5 and 8.5 scenarios revealed that the most significant decrease in heating energy usage was happening in the Hot-Humid zone with Automated blinds, which is a more efficient alternative than manual Blinds. In contrast, the Very Cold zone experienced almost no changes in either heating or cooling energy consumption. In Climate Zones 2B Hot-Dry and 7 Very Cold, rising temperatures lead to increased cooling energy requirements and reduced heating demands due to climate changes. Climate Zone 4A Mixed-Humid saw reduced energy consumption for cooling but increased usage for heating, particularly under RCP 8.5. In Zone 2A Hot-Humid, a high demand for heating energy was found.

A Study Article of DLMS to IOT Protocol

Abstract: The DLMS (Device Language Message Specification) protocol plays a crucial role in energy metering and management. It provides a standardized communication framework for exchanging data between utility meters and data collection devices. The protocol enables interoperability, remote meter reading, and supports efficient management of energy consumption, billing, and grid operations. The DLMS protocol is essential for Internet of Things (IoT) applications as it provides a standardized method for communication between Internet of Things devices and energy meters. This facilitates the seamless integration of energy data into IoT platforms, enabling efficient monitoring, analysis, and management of energy consumption in smart grid and smart city deployments. To this end, we have developed a prototype DLMS to IoT gateway that is based on either STM32 or any other IoT module. However, we encountered a challenge in testing the prototype since we couldn't get any digital meter to conduct hands-on testing..

Comparative analysis on the mechanism of climate responsiveness strategies on indoor thermal performance of Yaodong dwellings in the Loess Plateau region of China

The vast rural architecture, while contributing significantly to enormous building energy consumption, also brings about substantial energy-saving potential. This underscores the need for a profound understanding of sustainable and intelligent green construction practices for vernacular dwellings. This study focuses on investigating the mechanisms of climate-responsive strategies on the indoor thermal environment of traditional dwellings in the Loess Plateau region, using the cave dwelling, known as Yaodong in Yulin, as a case study. Through on-site measurements, the study examines the effectiveness of climate responsiveness strategies on the indoor thermal conditions of different types of Yaodong (cliff-side style and independent style). Simultaneously, dynamic simulations are employed to analyze energy consumption of heating season (ECHP) and indoor temperature on the coldest day, while adjusting variable parameters of various passive strategies to identify the influences of factors affecting the indoor thermal performance of different types of Yaodong. The results indicate that the traditional Yaodong, employing passive design methods can contribute to enhancing indoor comfort under adverse climate conditions. The average indoor temperatures of cliff-side and independent Yaodong bedrooms are −1.9 °C and −3.2 °C, respectively, compared to the outdoor temperature of −12.8 °C, outperforming modern brick dwellings with an average indoor temperature of −9.3 °C. For cliff-side Yaodong, factors such as orientation, window-to-wall ratio (WWR), the aspect ratio, and sunspace prove to be the four most critical elements influencing indoor thermal performance. For independent Yaodong, the ranking of key factors is orientation, the aspect ratio, wall thermal resistance, and window-to-wall ratio. This study can provide guidance and reference for the preservation and revitalization of cave dwellings in the Loess Plateau region.

Research on Multi-Objective Optimization Design of University Student Center in China Based on Low Energy Consumption and Thermal Comfort

Ensuring optimal building performance is vital for enhancing student activity comfort and fostering energy-saving initiatives toward low-carbon objectives. This paper focuses on university student centers in China, aiming to diminish building energy consumption while enhancing indoor thermal comfort. Parametric modeling of typical cases is executed using the Grasshopper 1.0.0007 software package, and the simulation of building energy consumption and indoor thermal comfort relies on the Ladybug and Honeybee plug-in. Employing a multi-objective optimization design method and the Octopus multi-objective optimization algorithm, this study integrates numerical simulations and on-site surveys to analyze how factors like building form, orientation, envelope structure, and others impact the indoor and outdoor environment. A comprehensive optimization design approach is implemented for the building’s exterior components, including the walls, windows, roof, and shading system. After conducting a comparative analysis of the annual comprehensive energy consumption and indoor thermal comfort before and after the optimization plan, it is determined that implementing these measures reduces the annual comprehensive energy consumption of the building under study by 58.8% and extends the duration of indoor thermal comfort by 53.0%. This study presents a practical optimization design methodology for university student center architecture in China, aiding architects in decision making and advocating for energy-efficient building designs.

Energy consumption, power generation and performance analysis of solar photovoltaic module based building roof

Building energy intensity (BEI) of typical office buildings in Malaysia ranges from 200 to 250 kWh/m2/year, wherein a substantial portion is due to the cooling system. This study evaluates of the performance and suitability of double-laminated monocrystalline solar photovoltaic (PV) glass in comparison to traditional solar PV systems installed on roofs in Malaysian conditions. To address this aim, simulation studies have been conducted to examine the efficacy of Building Attached Photovoltaic (BAPV) and Building Integrated Photovoltaic (BIPV) systems. Key parameters such as heat transfer across the building envelope, cooling demand, daylight utilization, and solar irradiance were analyzed. Additionally, an evaluation of economic viability, including Return on Investment (ROI) and payback period (PBP), was undertaken. Analysis showed that more heat was transferred through the roof on the BIPV building as the U-value of the solar PV glass was higher than the metal deck roof on the BAPV building. The lighting energy in the BIPV building can be saved by up to 80 % as sufficient daylight was entering the building. More than 30 % of the building area had a daylight factor of 1.0 %–3.5 %. From economic viewpoint, the double-laminated monocrystalline solar PV glass had a longer payback period as the initial capital cost was much higher even though the one-off savings and annual savings in the BIPV building were more than the BAPV building.

Energy efficiency and performance analysis of a legacy atomic scale materials modeling simulator (VASP)

AbstractThis work tackles the performance and energy consumption analysis of a legacy scientific application, the VASP (Vienna Ab-initio Simulation Package), an application commonly used by physicists and chemists for modeling materials at the atomic scale. Many of these scientific applications have been implemented in Fortran, where energy metrics instrumentation is not straightforward. We obtained performance figures (execution time and energy consumption) by instrumenting the source code using EML. This energy measurement library has been modified to introduce Fortran interfaces for these metrics. The analysis was carried out using different matrix algebra libraries, parallelization techniques, and hardware platforms, emphasizing on the MPI, OpenMP, and CUDA parallel implementations of the algorithms used in VASP. We employ various material specifications (atomic structures) and molecular sizes of a silicon-based crystal to create a set of benchmarks for these specifications, leading to some recommendations for final users regarding performance improvements. The proposed benchmarking technique assists the user in selecting the right combination of problem size, compilers, and parallelization options available in VASP. For a given system platform, the user will be able to determine not only the architecture to use (GPU or multicore processors), but also the appropriate library and parallelization according to the atomic structure and molecular size.

Fabrication and Development of Solar Powered Cart

Electric vehicles (EVs) are becoming increasingly popular due to their eco-friendliness and low operational costs. However, the use of fossil fuels to generate electricity to charge these EVs undermines their sustainability goals. Combining solar energy with EV charging can create a sustainable and clean transportation system, which can reduce our dependency on non-renewable energy sources. The overall objective of the project is to develop and fabricate an optimized, efficient solar powered electric cart charging with bi-directional smart inverter control that can be used on campus in transporting and carrying Stationery loads. Subsystems such as the frame, drive train, power, suspension, Brakes and Steering that are modeled and analyzed through quantitative methods are purchased and fabricated. The cart is fabricated as per its design for 250kg load carrying capacity, acceleration of 1.2 m/s2 and maximum achievable speed of 15km/h with a power rating of about 1.5kW. Secondary support software such as Excel, and solar analyzing software were used to assist in the planning of the various subsystems as well as data collection and assortment. The proposed model provides the IOT-based smart solar energy consumption analysis and control model by using solar photovoltaic micro grid. The proposed IOT design must meet product and process requirements. Key Words: Power optimization, golf cart, solar transport, Photovoltaic, Solar-powered, Emission Analysis, Environmental. Solar power.

A novel attention-based long short term memory and fully connected neutral network approach for production energy consumption prediction under complex working conditions

Continuously growing demands due to the predictable faults or abnormal events of the flexible production line are becoming a challenge to easily cause the energy waste, which needs to accurately predict the real-time energy consumption of the actual production process. By reviewing the related works about prediction model approach and energy consumption analysis in the actual production application, it is necessary to overcome two main challenges: optimal prediction selection and internal relation mining of energy consumption under different equipment working conditions. To deal with the existing challenges, the paper proposes a novel attention-based Long and Short-Term Memory (LSTM) approach to predict energy consumption with temporal series under various equipment working conditions. This method offers forecasting support to form a complete prediction framework for the automatic Miniature Circuit Breaker (MCB) production line, which can be used to predict the specific working condition based on attention-based LSTM approach. By comparing various prediction approaches for energy consumption under the working condition prediction with various equipment of different production line, the Fully Connected Neutral (FCN) network approach can achieve a high accuracy and effectiveness based on MAE, R2 and MSE evaluations.

NeTrainSim: a network-level simulator for modeling freight train longitudinal motion and energy consumption

AbstractAlthough train modeling research is vast, most available simulation tools are confined to city- or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by developing the NeTrainSim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. The main objective of this simulator is to enable a comprehensive analysis of energy consumption and the associated carbon footprint for the entire train system. Four case studies were conducted to demonstrate the simulator’s performance. The first case study validates the model by comparing NeTrainSim output to empirical trajectory data. The results demonstrate that the simulated trajectory is precise enough to estimate the train energy consumption and carbon dioxide emissions. The second application demonstrates the train-following model considering six trains following each other. The results showcase the model ability to maintain safe-following distances between successive trains. The next study highlights the simulator’s ability to resolve train conflicts for different scenarios. Finally, the suitability of the NeTrainSim for modeling realistic railroad networks is verified through the modeling of the entire US network and comparing alternative powertrains on the fleet energy consumption.

Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream

The proposed privacy-preserving framework based on fog computing for securing IoT data was examined through 10 experiment trials, with each trial dissecting a number of performance related metrics. Specifically, across the trials, latency values varied between 45 and 55 milliseconds, which signified that communication overhead was minute and that data were processed efficiently. Throughput values also varied considerably, yet only between 95 and 110 megabits per second, which signalled that the framework would allow processing data at high speeds. The rates of resource utilization measured in terms of MHR from CPU and Mused in the memory of specific fog nodes, varied between 58% and 76% . Regarding the scalability of the proposed framework, it was assessed based on the data collected and divided into the corresponding categories. From the energy consumption analysis, the values varied between 470 and 530 joules , which was recognized as the change caused by the shifting performance of the proposed IoT solution. Finally, communication overhead values varied from 970 to 1050 bytes, showing the differences in the effects which privacy-preserving frameworks have on data transmission. In conclusion, the results indicate that the proposed complex is immensely efficient in terms of protecting sensitive IoT data, ensuring a high level of security, preserving privacy, maintaining the current performance, and being adjusted to the new threats and security challenges.

A sustainable cooling/lubrication method focusing on energy consumption and other machining characteristics in high-speed turning of aluminum alloy

Understanding energy implications and machining performance standards is vital as industries move toward sustainability. Modern machining techniques including high-speed turning are used in the study on aluminum alloy materials. Therefore, this study focuses on the energy consumption and in-depth analysis of machinability criteria in the context of eco-friendly high-speed turning of aluminum alloy. The experiments were performed under ecofriendly cutting conditions dry and minimum quantity lubrication (MQL) conditions and the intricate relationship between machining parameters and cooling conditions was investigated in terms of energy consumption, tool wear, surface roughness and chips morphology. The result reveal that the Ra values exhibited 2.4 times increase in dry machining and an average increase of 2.3 times in MQL machining when the feed rate was increased from 0.2 to 0.4 mm/rev. Moreover, the MQL cooling is helpful in lowering the energy consumption as well as tool wear and surface roughness during the machining operations. MQL machining resulted in a 35% reduction in flank wear and 20% reduction in energy consumption compared to dry machining.