Optimize Power Consumption Research Articles

Efficient multi-agent reinforcement learning HVAC power consumption optimization

This work proposed a novel reinforcement learning (RL) approach deep deterministic policy gradient with multi-stabilization network (MADDPG-MSN). It enhanced the sample efficiency of RL in heating, ventilation, and air conditioning (HVAC) power consumption optimization. Employing the multi-stabilization network trick, MADDPG-MSN efficiently learned to balance temperature control and power consumption with a limited number of interactions, expanding the potential of RL in the power consumption optimization of real-world HVAC systems. Evaluated by the simulated data center scenario, it reduced 28% power usage without compromising temperature control capability compared with the traditional model-predictive controller. In the real-world air conditioner testing, it demonstrated a similar control performance of indoor temperature to the built-in controller while consuming 32% less power.

Monitoring Perishable Commodities Using Cellular IoT: An Intelligent Real-Time Conditions Tracker Design

Perishable product losses can occur throughout postharvest handling. Proper monitoring of key environmental conditions during this period is essential for predicting quality losses throughout their shelf life. This paper presents the design and testing of a portable and compact datalogger for the real-time monitoring of environmental conditions throughout the food supply chain. The device developed incorporates high-precision sensors to measure temperature, relative humidity, CO2 concentration, luminosity and vibrations, as well as wireless communication capabilities for data transmission, simplifying real-time monitoring over existing multi-component systems while keeping costs affordable. Strategies to optimize power consumption allow a month of battery life, being able to cover entire periods of transport and storage, according to the results of the autonomy test performed on the device. The datalogger uses NB-IoT and relies on other wireless communication protocols if not available to send sensor data to a cloud platform. Comparative testing with commercial dataloggers has been carried out to verify correct device measurements, and field testing has validated successful real-time data transmission along an entire refrigerated transport route. The functionality and autonomy of the proposed device meet the needs of live remote monitoring to help reduce food losses.

Single Ended Read Decoupled High Stable 9T CNTFET SRAM for Low Power Applications

ABSTRACTIn wireless sensor networks, conserving power is vital for prolonging battery life. This research introduces a groundbreaking solution: a 9T carbon nanotube‐field effect transistor (CNTFET) based SRAM cell (9T SRAM) designed to optimize power consumption and stability. Through meticulous analysis, the performance of this 9T SRAM cell is quantified. Power consumption metrics reveal impressive figures: the write, hold, read, and dynamic power are measured at 0.21 nW, 0.32 nW, 15.28 μW, and 8.09 μW, respectively. Furthermore, the Write SNM (WSNM), Hold SNM (HSNM), and Read SNM (RSNM) are found to be 380.11, 390.22, and 390.31 mV, respectively, indicating robust stability. The proposed bit cell has a write and read delay of 95.1 and 39.6 pS, respectively. Incorporating stacked transistors diminishes power consumption, while the decoupled read technique boosts the stability of the proposed bit cell. By comparing these results with existing SRAM cells, the superiority of the proposed 9T SRAM cell in terms of power efficiency becomes evident. Notably, it outperforms earlier models, making it an ideal candidate for integration into wireless sensor networks. These findings are supported by simulations conducted using HSPICE, alongside a 32 nm CNTFET model sourced from Stanford University.

Advanced Hybrid Optimization Algorithms for Energy Efficient Cloud Resource Allocation

AbstractCloud computing is highly sought after for its dynamic resource allocation capabilities and pay‐per‐use model. However, previous research has identified several challenges, such as lower coverage, high integration rates, longer computation times, and complex operators, all of which are associated with NP‐hard problems. These issues negatively impact the efficiency of resource allocation and scheduling, leading to slower processes, inefficiencies in multi‐objective optimization, lower throughput, and higher power consumption. To address these challenges, we propose a unique Hybridized Optimization Algorithm that integrates Crow Swarm Optimization (CSO), Cuckoo Search Optimization (CSO), and Cat Hunting Optimization (CHO). Initially, Particle Swarm Optimization (PSO) and the Crow Search Algorithm (CSA) handle the exploitation and exploration phases to balance task loads. Subsequently, Cuckoo Search Optimization (CSO) enhances resource utilization and addresses NP‐hard issues, while Cat Hunting Optimization (CHO) refines the search from global to local optimal spaces to achieve the best values. The results demonstrate that the proposed hybrid technique effectively reduces user request waiting times, lowers energy consumption, and decreases execution times on cloud servers compared to baseline approaches, thereby significantly improving overall system performance.

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

Network-on-Chip (NoC) Applications for IoT-Enabled Chip Systems: Latest Designs and Modern Applications

Network-on-Chip (NoC) technology has emerged as a critical innovation in the field of integrated circuit design, addressing the growing demands for efficient, scalable, and high-performance on-chip communication. This review provides a comprehensive examination of NoC, highlighting its architecture, key features, and current applications. NoC leverages a network-based approach, utilizing routers and links to interconnect various components on a chip, thereby overcoming the limitations of traditional System-on-chip (SoC) architectures that rely on shared bus systems. The review underscores NoC’s superior communication efficiency, scalability, and reduced latency, which are essential for modern high-performance computing, multi-core processors, and advanced embedded systems. It also explores NoC’s ability to optimize power consumption through dynamic voltage scaling and power gating, making it a favorable choice for energy-efficient designs. Despite its complexity, NoC’s modularity and adaptability make it suitable for a wide range of applications, from artificial intelligence accelerators to high-performance data centers. In conclusion, NoC stands as a transformative technology that enhances the performance and scalability of integrated circuits, paving the way for more sophisticated and powerful electronic systems. As the demand for higher computational capabilities and efficiency continues to rise, NoC is poised to play an increasingly vital role in the advancement of electronic design and architecture.

Experimental comparison of First-law analysis of peak hour operation of GSHP system under optimized conditions

The present study presents a novel procedure to study energy – efficient performance of a Ground − Source Heat Pump (GSHP) system during peak hour space cooling and heating operations by integrating an experimental study with First law analysis and optimization techniques. The main objective of the study is to identify how closely the results of First law analysis agree with the actual experimental results when operated under optimized conditions. The experimental data obtained during peak hour operation of a 17.58 kW GSHP system for each three days of operation in summer and winter seasons are considered for the analysis. The Coefficient of Performance (COP) and Power Consumption (W) of the GSHP system are computed using an energy balance based on First law using the experimentally measured heat rejection by the Borehole Heat Exchangers (BHEs) and the cooling effect. For optimization using the Taguchi method, six distinct measured parameters, each at three different levels, are selected as variables. Analysis of Variance (ANOVA) was carried out on COP and W to ascertain the parameters that exert the most significant influence on the performance of the GSHP system. During peak hour operation, the temperature difference between the inlet and outlet water in the BHE was found to be 10–11 °C in cooling mode and 3 °C to 4 °C in heating mode operations. Temperatures of water in the BHEs and air at the heat pump, which contribute to the heat interaction, are found to be the most influential parameters for cooling and heating mode operations. The COP for cooling mode computed by the optimization technique, 3.59, closely agrees with the experimental COP of 3.73. Similarly, the optimized COP for heating mode, 3.84 is found to be closer to the experimental COP of 4.32. The optimized power consumption deviates by 14.21 % and 11.94 % from the experimental results for cooling and heating modes, respectively.

A coupled plasma-thermal model for hollow cathode power decomposition and parametric analysis

Abstract A 2D axisymmetric transient coupled plasma-thermal model is developed to simulate the plasma behavior during the self-sustained discharge of hollow cathodes, which presents a complete hollow cathode structure and energy transfer processes in multiphysics fields. The model has been validated by quantitative agreement between the simulation results and experimental data on the plasma and emitter temperature at the NSTAR cathode. The effects of thermal protection design, operating conditions, and geometric design on the cathode performance are analysed through electric and thermal power decomposition. The parametric analysis shows that the optimal thermal protection design is to use a 1/3 thickness cathode tube with 4 layers of radiation shielding close to the tube, which reduces 43.7% conductive and 61.1% radiative heat dissipation, respectively. Increasing the inlet flow rate counter-intuitively reduces the emitter temperature due to the potential reversal in the diffusion electric field dominated region, revealing that the flow rate can be traded for the dual optimisation of lifetime and power consumption. Under high current conditions, the IAT effect dominates the plume resistance to increase the discharge voltage after an inflexion point, which is the main factor limiting the cathode performance. A large internal radius gives a uniform low emission and helps to prolong life, while the orifice length should be avoided to be longer than 4 times the orifice radius due to the significantly enhanced Joule heating in the narrow orifice. The orifice radius determines the power deposition due to electron and ion bombardment through potential penetration. For high-current discharger cathodes dominated by electron bombardment, large or through-orifice designs are preferred, while for low-current neutralizer cathodes dominated by electron bombardment, small-orifice designs are recommended.

Optimal economic operation of smart home devices based on home microgrids and time-of-use tariffs

Abstract In this paper, a two-layer model is proposed for smart devices in a home energy management system in order to optimize residential energy use under time-of-use tariffs and home microgrids. Home smart devices are modelled based on their power consumption attributes and building thermal resistance. An HVAC-transferable device cooperative operation strategy is introduced to construct the economic optimization of the upper-level HVAC operation and the lower-level transferable device operation. This strategy reduces home power costs while maintaining user comfort. By iteratively solving the two-layer model, the optimal power consumption scheme for home smart devices is determined.

English

English

A 10-bit 70-MS/s SAR ADC with Reference Buffer in 40nm CMOS

Abstract A 10-bit 70-MS/s successive approximation register (SAR) analog-to-digital converter (ADC) with reference buffer is presented in this paper. Segmented capacitive DAC with redundant 2bits is designed to relax the design of reference buffer. A hybrid switching sequence is applied to the ADC to optimize power consumption. The Measurement results Show that the core SAR ADC achieves 48.52 dB SNDR and 56.67 dB SFDR at 70 MS/s sampling rate and Nyquist input frequency, consuming 2.50mW power consumption, achieving a FoM of 163.6 fJ/step. The ADC occupies an active area of 0.017 mm2 in 40nm CMOS technology.

(Invited) Advances in Wide Bandgap Thermal Resonant Infrared Detectors

Exploring the surface of distant planets, notably Venus, requires robust technologies capable of withstanding extreme environmental conditions while providing valuable scientific insights. This paper delves into the advancements made in designing an instrument tailored for capturing infrared (IR) images in the harsh Venusian environment, where temperatures soar to 500°C. The presented IR detector harnesses the power of a sparse array of resonant-based micromechanical devices, strategically addressing the limitations of existing technologies that can only withstand extreme conditions for limited durations.At the core of this technology lies the utilization of high-temperature-tolerant InAlN/GaN as the primary resonating element. GaN's resilience and high temperature tolerance make it a crucial player in challenging space environments. Its wide bandgap and chemical stability are advantageous for enduring harsh conditions during space missions. In tandem with GaN's intrinsic properties, high-temperature-tolerant meta-absorbers strategically positioned on the surface of each pixel play a pivotal role in the functionality of the IR detector. These meta-absorbers exhibit a remarkable capacity to absorb incoming infrared (IR) radiation, promptly transforming it into heat. The resonant frequency of these devices exhibits a linear shift with temperature variations, and a high-temperature-tolerant read-out electronic adeptly senses this frequency shift, enabling the seamless transfer of imaging data. Extraction of IR source power occurs through the resonance frequency shift in the exposed GaN resonator, compared to a reference resonator. The reference resonator, lacking the IR-absorbing layer, establishes a baseline for minimal resonant shifts.The sparse array configuration strategically reduces imaging elements to optimize power consumption and cost-effectiveness. Inspired by techniques employed in medical imaging instruments, this approach allows the construction of images from a low-power 2D array, striking a balance between imaging capabilities and processing efficiency. This sparse array configuration serves a dual purpose, streamlining signal routing for integrated read-out electronics and simplifying the overall integration and packaging of the IR imaging instrument. Monolithic integration of the read-out electronic with the sensor platform on the same chip overcomes challenges related to electrical and mechanical stability at high temperatures.The resonant effect delivers a remarkable enhancement in the signal-to-noise ratio, surpassing existing thermal detector technologies by a factor of 70×. Furthermore, the strategic implementation of sparse arraying results in approximately a 4× reduction in power consumption, presenting an efficient and resilient solution for imaging in extreme environments. Diverging from traditional IR detectors limited in performance at high temperatures, these detectors rely on the temperature coefficient of elasticity (TCE), offering a wide dynamic range of operational temperatures.Finally, this paper includes recent results measured at 500°C, demonstrating the instrument's capability to operate effectively in Venus's challenging conditions. These advancements pave the way for future planetary exploration missions, promising a deeper understanding of Venus's geological and atmospheric processes.

Design of 1.1T/300mm Bitter-type magnet for gyro-traveling-wave tube with power consumption optimization theory.

To improve the portability of magnets in gyrotron devices, we designed a compact Bitter-type magnet with power consumption optimization theory. This magnet operates at room temperature in a small volume. The theory revises existing electromagnetic theory for non-uniform structural Bitter-type magnets and achieves the lowest energy consumption through iterative optimization. To extend the magnetic field homogeneity region, the ferromagnetic material armature is applied to the Bitter-type system without additional power consumption. Unlike previous manual designs, the proposed Bitter-type magnets can obtain optimal parameters with a significant reduction in computing time. Through the introduction of correction factors, we improve accuracy through multiple verifications of simulations and experiments. On this basis, a room-temperature Bitter-type magnet system for Ka-band fundamental mode gyrotron amplifiers is designed. Its maximum magnetic field strength is 1.1T, and the length of the homogeneity region is 300mm. Through optimization, its energy consumption is only 27.5 kW.

A novel MTCMOS based 8T2M NVSRAM design for low power applications with high temperature endurance

This research investigates, for the first time, a novel eight-transistor-two-memristor (8T2M) nonvolatile static random access memory (NVSRAM) with 7-nm technology. The key innovation in this design lies in the incorporation of multiple-threshold complementary metal oxide semiconductor (MTCMOS) technology with power gating technique, which enables efficient power management and enhanced performance with low leakage current. The implementation of multiple threshold voltage levels allows for dynamic control of transistor behavior, optimizing power consumption and read/write speeds. As compared to a traditional six-transistor (6T) static random access memory (SRAM) cell, it has been ascertained that there is a 33% enhancement in the read margin and an 18% improvement in the write margin. Moreover, the delay for read, write ‘0’ and write ‘1’ is also minimized by 63.89%, 37.99% and 42.77%. Furthermore, the power attenuation is also reduced for read and write by 63.02% and 81.6%, respectively with respect to a conventional SRAM.

Optimizing Power Consumption in Machining Nickel-Based Superalloys: Strategies for Energy Efficiency

<div>In the face of the world’s population growth and ensuing demands, the industrial sector assumes a crucial role in the management of limited energy supplies. Superalloys based on nickel, which are well-known for their remarkable mechanical qualities and resilience to corrosion, are now essential in vital applications like rocket engines, gas turbines, and aviation. However, these metals’ toughness presents a number of difficulties during machining operations, especially with regard to power consumption. This abstract explores the variables that affect power consumption during the machining of superalloys based on nickel in great detail and suggests ways to improve energy efficiency in this area. The effects of important variables on power consumption are extensively investigated, including cutting speed, feed rate, depth of cut, tool geometry, and cooling/lubrication techniques. A careful balance between these factors is necessary to maximize machining efficiency and reduce power usage. Furthermore, this study reviews the effect of different heat source applications on power consumption and the resultant quality of machined nickel-based superalloys. Additionally, the critical role of cooling and lubrication in mitigating the adverse effects of high temperatures generated during machining is thoroughly examined. Innovative cooling strategies, including cryogenic or high-pressure coolant systems, are explored as potential avenues to enhance heat dissipation and minimize power requirements. In essence, this abstract not only sheds light on the challenges inherent in machining nickel-based superalloys but also offers actionable insights into how energy efficiency can be maximized through strategic parameter optimization and the adoption of innovative cooling techniques. By addressing these aspects, manufacturers can effectively navigate the complexities of machining superalloys while minimizing their environmental footprint and operational costs.</div>

Intelligent switching gain based sliding mode control for optimization of power consumption in cooperative manipulators

AbstractCooperative manipulators, vital for intricate tasks, are gaining widespread attention across industries. Recognizing their impact on power consumption, costs, and task outcomes, this paper emphasizes the critical study of control methods, actuator power consumption, and manipulator accuracy. Addressing these challenges, we propose a neural network‐sliding mode controller (NN‐SMC) to optimize actuator power consumption and minimize errors in cooperative manipulators. Operating in trajectory and point‐to‐point modes, the NN‐SMC dynamically generates real‐time switching mode controller (SMC) gains (L and K) for precise control. Stability is ensured by maintaining gains within permissible ranges. In point‐to‐point mode, the NN orchestrates an optimal path generation, along with tailored gains. To evaluate performance accurately, a novel control performance index is introduced. Experimental results on 3‐DOF cooperative manipulators demonstrate a remarkable 28% increase in the control performance index for the trajectory mode and a substantial reduction in computational complexity for both modes. This work not only addresses inherent challenges in cooperative manipulators but also signifies a methodological advancement through the integration of neural network‐based control, promising enhanced efficiency and stability.

Wind Power Station Efficiency Increase Methods Based on Wind Turbine Condition Process Mathematical Modelling when Distributing Electric Power Between the Consumers in Arctic and Far North Regions

The relevance of wind power plant efficient control method based on wind turbine condition process mathematical modeling is proved for electric power distribution between consumers, intended to minimize decision-making time on technological process redistribution of electric power production for another consumer by means of actual information on equipment failure time (wind turbine, electric power stocking system), that provides optimum power production and consumption planning in northern regions. The review of the basic mathematical modeling methods of electric power redistribution between consumers that showed there are certain problem statements with insufficient descriptions of mathematical models or the absence of ready and effective methods to solve them, in particular, wind turbine condition process mathematical modeling for electric power distribution to consumers in northern regions. As a result of the analysis of wind power plant efficient control method on the basis of wind turbine condition process mathematical modeling to distribute electric power to consumers in northern regions game theory and pulse system functioning mathematical methods application based on difference equations is expedient. The decision-making strategies stipulated by 'nature' (working capacity or wind turbine failure), the scheduler (repair or wind turbine redistribution) and a condition of phase co-ordinate (repair, idle, redistribution or operation wind turbine modes) are defined. On the basis of the accepted strategy and use of step function Heaviside F(t) for fixing of technological process time, the description of the wind turbine condition equation was made providing operative failure time information access on because due to idle time, redistribution or repair that provides optimum electric power production planning for a consumer.

A centralized fleet management system for electrified transportation

Abstract Hybrid Electric Vehicles are a promising alternative to Conventional and Electric Vehicles, as they offer better fuel economy, lower emissions, and long drive range without requiring extensive charging infrastructure. Limited driving range and battery degradation in electric vehicles are the main challenges in electrified fleets working in large metropolitan. For these challenges, the hybrid electric vehicle fleet management system is an effective solution to optimize power consumption and mitigate electric vehicle and conventional vehicle challenges. This paper proposes a hybrid electric vehicle fleet management system for public transportation, to analyze and investigate the performance and energy consumption rate of conventional, electric, and hybrid electric vehicles on different routes using representative driving cycles. The hybrid electric vehicle fleet management system enhances operational efficiency for the fleet management system and reduces power consumption in transportation. The proposed work comprises a hybrid electric vehicle model that is based on object-oriented programming. The proposed work offers the ability to add the input of the initial state of charge, power split ratio, and route data for each bus, and the hybrid electric vehicle model computes the state of charge and fuel consumption rate along the road. Finally, The proposed work shows how a hybrid electric vehicle fleet management system has a significant potential to be a solution for public transportation in Egyptian urban areas at this time compared with conventional vehicle and electric vehicle fleet management systems.

Surrogate-assisted constrained hybrid particle swarm optimization algorithm for propane pre-cooled mixed refrigerant LNG process optimization

The propane pre-cooled mixed refrigerant (C3MR) process is one of the most widely used and efficient natural gas liquefaction processes. However, optimization of this process involves various design and operational constraints, complex thermodynamics, and hence nonconvex in nature. Therefore, it's computationally expensive, often involving trial and error approaches. Existing optimization algorithms often possess flaws such as insufficient accuracy and premature convergence, leading to suboptimal solutions that may violate essential process constraints. This article proposes a novel method to optimize the C3MR process that handles the computational complexity, meets the constraints, and achieves feasible solutions quickly. The proposed method includes modified feasibility-based constraint handling and radial basis function network-assisted surrogate modeling and optimization, where the power consumption is optimized using a hybrid of the particle swarm optimization (PSO) and social learning PSO algorithm. The proposed algorithm achieves the optimum power consumption (121109.31 kW), which is 21.5 % less than the base case (154200 kW). The optimization results are compared with similar optimization algorithms, where the proposed algorithm outperformed the other algorithm regarding optimal solution, convergence, and speed. The results from this study are compared to previous studies from the literature, which validate the accuracy and applicability of the proposed method.

Self-detection of thermal ambient parameters for air-conditioned space by learning operation data and self-prediction of indoor temperature and power consumption

For efficient energy use of air conditioning unit (AC), there is a need to find an optimal control method for operation. To deduce an optimal control algorithm, indoor temperature and power consumption need to be predicted according to given operating conditions. Among the key factors, the thermal ambient parameters such as thermal resistances of the operation sites are most difficult to identify. This study proposed a method for an AC to deduce these parameters for itself by learning operation data obtained from the embedded sensors and autonomously predict indoor temperature and power consumption by solving the transient energy conservation equation incorporating refrigeration cycle simulation. A novel aspect of this study is that the AC can autonomously perceive thermal ambient parameters of its surroundings and utilize these parameters to predict indoor temperature variations and power consumption. These data could be fed back to the control software of the AC in real time. To validate the proposed method, experiments were conducted in model house test facility using a real air-conditioner, and the results were compared with the simulation results. It was confirmed that the time to reach the air temperature setpoint was within 3 min and the power consumption could be predicted within 9 %.