Optimize Energy Use Research Articles

Intelligent Lighting Control Systems for Energy Savings in Hospital Buildings Using Artificial Neural Networks

Lighting control systems are essential in modern building automation and smart homes, efficiently managing illumination to enhance energy conservation and user comfort. This project tackles energy consumption challenges in hospital buildings by introducing Intelligent Lighting Control Systems (ILCS) that take natural light and occupancy into account, driven by Artificial Neural Networks (ANN) and diverse machine learning algorithms. In our study, we collected sensor data, processed it, and designed a lighting control system employing a feedforward neural network and various machine learning algorithms. Surprisingly, our research found that a linear regression algorithm surpassed the ANN-based system in this context. We implemented a prototype, tested it on hardware, and obtained the expected results. This research marks progress towards optimizing energy use in hospital buildings and contributing to sustainability endeavors. By combining ILCS and machine learning, it offers a promising approach for more efficient and eco-friendly lighting systems

The impact of thermal comfort requirements on household heating energy consumption

Urban residential buildings consume a large amount of energy. In most homes, heating runs on a set schedule. Therefore, the installation continues to operate irrespective of whether the residents' thermal comfort requirements have been fulfilled. This process results in considerable wasted energy and CO2 emissions. To address this issue, in this study, data on heating energy use and carbon emissions from 2302 field surveys in the core, main, and suburban areas were quantified. Furthermore, the impact of different thermal comfort requirements on the carbon emissions of self-heating households was explored based on the PMV-PDD model. The study revealed that (1) Beijing's per capita household energy consumption is 613.80 kgce·a−1, and heating accounts for a significant portion of energy consumption and carbon emissions; (2) carbon emissions from household energy consumption differ among districts in Beijing, with suburban districts emitting approximately 1.6 times more than main urban areas; (3) suburbs have less need for thermal comfort, but they emit more carbon than other areas with similar heating costs due to limited heating infrastructure, poor access and the high cost of clean energy; and (4) warmer thermal comfort requirements elevate energy consumption and thus positively impact heating costs and carbon emissions, especially in the main urban area. The energy waste caused by heating is quantified, and the impact of thermal comfort requirements on carbon emissions is clarified. The findings are informative for optimizing energy use in residential buildings and proposing targeted low-carbon heating policies.

Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks

ABSTRACT Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs’ mobility can disrupt energy consumption and network performance. Effective path improvisation algorithms are needed for MANs to optimize energy use, reduce data loss, and maintain network connectivity in dynamic WSN environments. To overcome these issues, Topological Information Embedded Convolutional Neural Network based Lotus Effect Optimization for Path Improvisation of the Mobile Anchors in Wireless Sensor Networks (TIECNN-PIMA-OAC-WSN) was proposed. The approach establishes a robust network setup and energy model, employing TIECNN for initial cluster formation and cluster head selection. The chosen cluster head, termed the Mobile Anchor, undergoes optimization using the Lotus effect optimization algorithm to determine the most efficient and shortest path. This work enhances both the topological information processing and energy efficiency of mobile anchor paths. The simulation outcomes prove the proposed technique attains 33.12%, 39.56%, and 42% higher network lifespan for sensor nodes density 40; 38.22%, 29.66%, and 41.33% higher network lifespan for sensor nodes density 60; 37.45%, 35.55%, and 43.67% higher network lifespan for sensor nodes density 80; 32.45%, 29.45%, and 46.66% higher network lifespan for sensor nodes density 100 analysed to the existing methods.

Predicting thermal comfort of residential shower rooms for optimal showering energy use

Showering is a typical daily activity directly related to people's comfort and health, dominating thermal energy consumption in residential buildings. This study developed a mathematical model to predict people's mean skin temperature (MST) and dynamic thermal sensation (DTS) during undressing and standing showering periods to identify the comfortable showering environment for different occupants. The model's validity was supported by comparing the calculated MSTs with the results collected by a previous experimental study. Moreover, 31 subjects (of different sexes, weights, and heights) were involved in the case studies. Results of the case studies showed that female and underweight subjects' MST and DTS varied more significantly than male and overweight subjects during undressing and showering periods. Additionally, it was found that both water and air temperatures significantly impact occupants' MST and DTS during showering, and the impact of water temperature was much more significant than the impact of air temperature. Moreover, by combining a previously developed energy consumption model, an optimal showering environment can be identified where both maintaining comfort and saving energy can be achieved. Based on the simulation results, the water temperature during showering was suggested to be not higher than 40 °C, especially for longer showering, to avoid thermal discomfort and energy overconsumption. However, the model was established based on theoretical deduction, with assumptions made. Additional measurements were needed to validate the model further and improve it.

Direct/indirect optimal use of shallow geothermal energy in floor radiant cooling system coupled with air-handling system

Shallow geothermal energy is used as the cold source of floor radiant cooling systems (RFCSs). A physical simulation test bench was built to study the cooling and dehumidification characteristics of an RFCS to evaluate its feasibility. Three operation modes involving direct-supply chilled water, direct-supply chilled-water coupling air-handling system (AHS), and indirect cascade utilization were evaluated. Results showed that the room could be cooled rapidly in the direct-supply chilled-water mode. When the direct-supply chilled-water temperature was lower than 16 °C, condensation occurred on the floor surface. The AHS effectively avoided condensation, and the maximum humidity ratio difference between the air inlet and outlet was 13.61 g/kg. However, the temperature difference between the chilled water and the floor surface was reduced. Under the cascade utilization mode, the cooling efficiency of the system improved, and the indoor temperature and relative humidity were maintained at 20.6–28.1 °C and 57.0–76.1 %, respectively. This study provides valuable insights into the direct application of geothermal energy in RFCSs.

In pursuit of thermal comfort: An exploration of smart heating in everyday life

Smart Home Heating Technologies (SHHT) have been designed to improve demand flexibility and energy conservation. SHHT rely on rational theories of energy use postulating that people will use less energy when the energy cost is higher. The inclusion of AI within SHHT is poised to optimise energy use in the future as the introduction of lower carbon energy sources place new demands on the grid. When SHHT is introduced in the home, however, they become situated in temporal heating practices that are shaped by an interplay of materiality, meanings, and competencies. We report findings from a mixed methods field study involving eleven households utilising an AI-enabled SHHT probe ‘Squid’. Taking a temporal focus throughout, our study contributes a new lens as to why households may not fully engage with SHHT's rational design, given that energy conversation is already embedded in their ongoing socio-material practices with heating. Focusing on the AI-human relation, we articulate the necessity for human agency where heating is involved, whilst also advancing an understanding of the new forms of hidden labour that households incur before they can engage with the AI. Crucially, our research informs the ongoing HCI concern over how humans understand AI, raising the question of who is responsible to assess the appropriateness of AI when the effects of human-AI performance remain opaque. Our findings contribute a new theoretical perspective into the intricate relationship between individuals and AI in the home and raise several new design implications for SHHT.

Design of a 1-phase Axial Flux Type Generator with a Variation of Winding on a Star-Shaped Stator

A single-phase axial flux generator with a star-shaped stator is required to explore the optimal use of rainwater energy as a renewable energy source. Untuk eksperimen, jumlah coils dan diameternya diubah. 25 coils dan 50 coils with 0.4 mm diameter and 25 coils and 40 coils with 0.5 mm diameter were designed. Generator performance will be tested under load and no load conditions at 600 rpm to 2000 rpm. The test results indicate that, for a single-phase axial generator running at 2.4W output conditions, increasing the number of windings impacts the voltage induced by the generator and that the number of stator windings influences the quantity of induced voltage, current, and power. Test findings with various winding variations, employing a 4.8V 0.5A or 2.4W light bulb load, demonstrate that an increase in winding count leads to an increase in electrical output

Electric Motor Maintenance

Electric motors play a vital role in industrial operations, and their protection from overloading is crucial for ensuring longevity, safety, and efficiency. This article explores the significance of motor overload protection devices in safeguarding motors from damage caused by overloading, which can lead to overheating and potential failure. It discusses the functioning of various protection devices, such as thermal, magnetic, and electronic overload relays, and emphasizes the importance of selecting the right device based on motor type, size, application, and environmental conditions. The article also highlights the role of regular maintenance and adherence to safety standards in ensuring the reliable and effective functioning of these protection systems. By implementing appropriate protection measures, companies can optimize energy use, increase operational efficiency, and reduce damage and production disruptions, thus contributing to the sustainability and safety of electric motors in industrial settings vulnerable to overloading. Highlight : Overload relays protect the motor, motor branch circuit, and motor branch circuit components from excessive heat from the overload condition Overload relays are part of the motor starter and protect the motor by monitoring the current flowing in the circuit The selection of an overload relay is important because repeated tripping and reset can reduce the mechanical life of the relay and may cause damage to the motor Keywords: electric motors, overload protection devices, thermal overload relays, magnetic overload relays, electronic overload relays, motor type.

The many channels of firm’s adjustment to energy shocks: evidence from France

Abstract Based on firm-level data in the French manufacturing sector, we find that firms adapt quickly, strongly and through multiple channels to energy shocks, even though electricity and gas bills represent a small share of their total costs. Over the period 1996–2019, faced with an idiosyncratic energy price increase, firms reduce their energy demand, improve their energy efficiency, increase intermediate inputs imports and optimize energy use across plants. Firms are also able to pass-through the cost shock fully into their export prices. Their production, exports and employment fall. A consequence of these multiple adjustment mechanisms is that the fall in profits is either non-significant, small or specific to only the most energy-intensive firms. We also find that the impact of electricity shocks has weakened over time, suggesting that only firms able to adapt their production process to energy cost shocks have survived. Importantly, when faced with large electricity and gas price increases, firms are less able to reduce their consumption. These results shed light on the mechanisms of resilience of the European manufacturing sector in the context of the present energy crisis.

Dynamic Multi-Sleeping Control with Diverse Quality-of-Service Requirements in Sixth-Generation Networks Using Federated Learning

The intensive deployment of sixth-generation (6G) base stations is expected to greatly enhance network service capabilities, offering significantly higher throughput and lower latency compared to previous generations. However, this advancement is accompanied by a notable increase in the number of network elements, leading to increased power consumption. This not only worsens carbon emissions but also significantly raises operational costs for network operators. To address the challenges arising from this surge in network energy consumption, there is a growing focus on innovative energy-saving technologies designed for 6G networks. These technologies involve strategies for dynamically adjusting the operational status of base stations, such as activating sleep modes during periods of low demand, to optimize energy use while maintaining network performance and efficiency. Furthermore, integrating artificial intelligence into the network’s operational framework is being explored to establish a more energy-efficient, sustainable, and cost-effective 6G network. In this paper, we propose a small base station sleeping control scheme in heterogeneous dense small cell networks based on federated reinforcement learning, which enables the small base stations to dynamically enter appropriate sleep modes, to reduce power consumption while ensuring users’ quality-of-service (QoS) requirements. In our scheme, double deep Q-learning is used to solve the complex non-convex base station sleeping control problem. To tackle the dynamic changes in QoS requirements caused by user mobility, small base stations share local models with the macro base station, which acts as the central control unit, via the X2 interface. The macro base station aggregates local models into a global model and then distributes the global model to each base station for the next round of training. By alternately performing model training, aggregation, and updating, each base station in the network can dynamically adapt to changes in QoS requirements brought about by user mobility. Simulations show that compared with methods based on distributed deep Q-learning, our proposed scheme effectively reduces the performance fluctuations caused by user handover and achieves lower network energy consumption while guaranteeing users’ QoS requirements.

Advancements in Energy Efficiency Technologies for Thermal Systems: A Comprehensive Review

This comprehensive literature review investigates the diverse landscape of energy efficiency technologies in thermal systems, offering an extensive overview of current research and development in this crucial domain. Thermal systems, vital in industries like heating, ventilation, air conditioning, and industrial processes, play a pivotal role. With escalating global energy demands and growing environmental concerns, optimizing energy use in thermal systems is imperative. The paper explores a wide range of energy efficiency technologies, from traditional methods to cutting-edge innovations, examining the implementation of advanced materials, smart sensors, and control strategies to enhance thermal system performance. Additionally, it scrutinizes the integration of renewable energy sources and waste heat recovery mechanisms, addressing the dual challenge of energy conservation and sustainability. The literature review critically evaluates the economic feasibility and practical applicability of these technologies, highlighting barriers and opportunities for widespread adoption. Synthesizing information from various sources provides a holistic understanding of existing knowledge gaps and future directions in energyefficient thermal systems. This research paper serves as a valuable resource for researchers, practitioners, and policymakers seeking insights into state-of-the-art technologies and strategies to optimize energy utilization in thermal processes. Keywords: Energy efficiency, thermal systems, design optimization, fuels and combustion, boilers and turbines, cogeneration, circulating cooling water systems and heat exchangers

A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0

AbstractIndustry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings.

A generative approach to explore residential urban form potential based on environmental performance for energy efficiency

The United Nations predicts that by 2050, 70% of people will live in cities, which will bring significant environmental challenges, including climate change and resource depletion. In India, the residential sector account for 30% of total energy consumption. Therefore, it is crucial to optimize energy use in residential structures to address urban population growth, density, and energy efficiency. This study explores the use of genetic algorithms for multi-objective optimization in the design of energy efficient residential urban forms linking urban population growth and environmental concerns. Urban form significantly influences energy consumption within neighborhoods. However, introducing genetic algorithms into urban planning, a field not typically associated with such computational methods, presents considerable challenges. This research addresses complexities such as simulating and optimizing energy use by focusing on the control of solar radiation and the enhancement of natural daylight within residential neighborhoods. It further seeks to enhance the quality of life in open spaces by improving the sky view factor and the sense of spaciousness, adding another layer of complexity to the optimization. In response to these complexities, a framework is developed using the Biomorpher plugin, integrating genetic algorithms in Grasshopper. This research, carried out in Ahmedabad, India, progresses in two primary phases. The experiment progresses in two phases: first, developing prototypes considering factors like Floor Space Index and building types; and second, refining these through multiple design iterations based on environmental criteria. The study generates a diverse range of optimized scenarios, such as low-rise, mid-rise, and high-rise densities, to understand the relationship between residential form and energy consumption. The research identifies energy-efficient configurations through analysis, offering adaptable solutions tailored to specific environmental and spatial contexts.Additionally, it offers a replicable methodology for urban designers and policymakers.

An Algorithmic Framework for Optimizing Submerged Arc Additive Manufacturing Process Parameters for User-Specified Bead Width

Submerged Arc Additive Manufacturing (SAAM) is a process known for its high material deposition characteristics, and since it is based on Submerged Arc Welding (SAW), it is expected to obtain thicker material depositions. In this work, an algorithmic framework has been developed which helps product designers or the user by taking their expectations of the bead width according to their requirement, and the algorithm suggests the optimal input process parameters which would help obtain an optimal bead width and optimal use of energy, minimizing the need for post-processing. The framework incorporates penetration depth, a critical factor for proper interlayer fusion. The framework was compared with a Genetic Algorithm (GA) output with similar GA parameters for a single run to evaluate its effectiveness. The proposed framework achieves a 93.7% accuracy in bead width prediction and a 27.5% reduction in energy input per unit length compared to the GA. This user-friendly tool empowers novice SAAM users and manufacturers to optimize deposition and energy efficiency.

Intelligent Control of Electric Vehicle Drives using Swarm Robotics

This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.

ASPECTS OF ENERGY EFFICIENCY MANAGEMENT FOR RATIONAL ENERGY RESOURCE UTILIZATION

This study delves into the multifaceted landscape of energy efficiency management with the objective of rationalizing the utilization of energy resources. The article considers a methodical approach to the study of the level of energy intensity of economic activity, covering levels of management, from the macro level to the level of an individual enterprise. In general, it is advisable to supplement the proposed approach with existing methods that extend the content to the results of energy efficiency research. Using the Life Cycle Assessment (LCA) method and other methodologies, we explore various dimensions of energy resource utilization and the scale of energy efficiency management across different stages of the lifecycle. Additionally, the study introduces levels of energy efficiency management developed by the authors, providing a structured framework for optimizing energy use. Through rigorous analysis, we evaluate the environmental impacts, energy consumption patterns, and efficiency levels associated with diverse energy management strategies. Our findings illuminate key areas for improvement and optimization in energy utilization practices, offering insight beneficial for policymakers, industry stakeholders, and environmental advocates alike. By leveraging the comprehensive framework of LCA alongside the developed levels of energy efficiency management, this research contributes to a nuanced understanding of energy efficiency management, thereby facilitating informed decision-making towards sustainable energy utilization pathways. Examining the example of building the life cycle of gas production and highlighting the main stages of its transformation from a raw resource to a finished product for consumption allows for the consideration of the entire chain of creation of the added value of this energy resource and enables control of the level of its influence on the results of the activities of economic units involved in this chain, as well as the consequences of their impact on the environment. This allows us to conclude that the approach discussed in the article can be used both for researching the energy efficiency of individual enterprises, as well as their associations, industries, and the economy in general.

Advancements in energy-efficient welding production techniques: Innovative models and methods for combined workpiece fabrication

This article highlights notable advances in energy-efficient welding manufacturing techniques, with particular emphasis on new models and methodologies for combined welding machines. High energy density welding processes such as laser beam and electron beam welding are being investigated for their potential to optimize energy use in various industrial applications. Furthermore, the examination extends to energy- efficient high-density welding procedures, notably encompassing electric arc, gas welding, and autogenous welding, while also taking into account factors tied to corrosion processing. Technological advances in machining, cutting techniques, and specialized connectors are also explored in detail, and their contribution to reducing energy consumption and improving overall equipment efficiency is examined. This thorough investigation would not be complete without considering the implications for the energy sector, where the use of energy-efficient welding techniques is critical in reducing costs and refining manufacturing processes.

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

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

Enhancing electric vehicle efficiency through model predictive control of power electronics

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

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

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