Power Consumption Levels Research Articles

Highlighting the Probabilistic Behavior of Occupants’ Preferences in Energy Consumption by Integrating a Thermal Comfort Controller in a Tropical Climate

The thermal comfort of an individual is known as the mental satisfaction they possess in a medium. This depends on several ambient factors such as air temperature, mean radiant temperature, relative humidity, air velocity, and personal factors such as cloth and metabolic activity. In buildings, occupants interact with different systems and equipment such as air conditioning, ventilation, lighting, and other appliances to influence these factors or demonstrate adaptive tendencies with the systems to reach comfort. Within the last two decades, preference-based occupant-centered control systems have been incorporated into buildings, generally validated with comfort indexes. A frequently found challenge is the formulation of the method used to create a system that considers the stochastic characteristics of the occupant’s portrait. Here, a method that links the advantages of both probabilistic and schedule-based methods and satisfactorily integrates it with comfort indexes through a controller is proposed. It is intended to compare the controller’s effect on thermal comfort through comfort indexes and energy consumption when implementing different occupant models applied in Panama. Sensibility analysis, gray-box building modeling, and thermal indexes were used in the controller’s design. Results showed that the best controller is the probability-based model providing low power consumption and PMV levels.

“Green technologies” elements by the individual residential buildings internal electrical networks operation

The article discusses the research results on the internal electrical networks operation of two independent individual residential buildings with a 0.38 kV voltage three-phase electrical input energy. It is established that these electrical networks operation is characterized by a significant currents and voltages unbalance level, which leads to a quality standards violation and electrical energy additional losses increase. When using the computer program “ModulHous”, which algorithm is based on the developed calculation methods, the currents and voltages unbalance calculation was performed. With the use of the MATLAB graphics editor technology, the obtained changes visualization in the currents and voltages unbalance has been implemented. The obtained diagrams analysis made it possible to establish the unbalance power consumption level and its impact on the internal power supply efficiency of the individual residential buildings. A special balancing device is proposed that automatically minimizes unbalancing flows in the internal individual dwellings electrical network. The developed software made it possible to establish the using balancing devices efficiency to improve the quality and reduce electrical energy additional losses in the considered electric networks of individual residential buildings.

Determination of Dynamic Characteristics for Predicting Electrical Load Curves of Mining Enterprises

The calculation of electrical loads is the first and most significant stage in the design of the power supply system. It is essential to make the right choice when choosing the power electrical equipment: transformers, power lines, and switching devices. Underestimation or overestimation of the calculated values can lead to large losses and an increase in capital costs. Therefore, the reliability of the results plays a key role. The use of energy-saving technologies and energy-efficient electrical equipment leads to a change in the nature and level of power consumption, which must be taken into account when determining the electrical loads. The existing methods leave out dynamic characteristics of electrical load curves, so the calculated values are overestimated by up to 40%. This study shows a load calculation method with the normalized correlation functions and its parameters at the level of the individual and group electricity consumers. As a result, the difference between the calculated and experimental values does not exceed 5%.

Modeling and Simulation of Household Appliances Power Consumption

The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson’s X2 test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats.

Flexible operation of a mixed fluid cascade LNG plant for electrical power management

The paper discusses operation and control of a process for the liquefaction of natural gas in which the refrigeration compressors are driven by electric motors. The aim is to enable the plant to accommodate contingencies in the availability of electrical power and to continue running when there is a shortage of electrical power, avoiding the significant economic impact of a shutdown. The article provides a detailed first principles analysis of the relationships between the electrical power consumption of the process, the production rate of the liquefied natural gas, its exit temperature, and its purity. By doing this, it is possible to ascertain settings for operating the process at various levels of power consumption. The results show that the process can operate with reductions of electrical power of 30% or more. Hence, power shortages could be managed by operating the process flexibly to make best use of the available remaining power, rather than by shutting down. The paper also discusses how such a system could be implemented industrially and identifies aspects that require further study.

Research on medium- and long-term electricity demand forecasting under climate change

Global warming and frequent extreme heat wave events have led to changes in people’s energy consumption habits, which in turn has a significant impact on electricity consumption. Accurate forecasts of electricity consumption can provide scientific support for the capability improvement of power supply service. To meet the needs of long-term power system planning of Beijing, a variety of empirical models to estimate the temperature–power consumption response function in Beijing are adopted to predict the future power consumption under different shared socioeconomic pathways (SSPs). The main results are summarized as follows: (1) With different models for measurement, the relationship between the temperature and power consumption in Beijing present a U-shaped curve. Taking the linear model as an example, a 1 degree-day increase in CDD will lead to a 0.053 million kWh growth in Beijing’s power consumption, while a 1 degree-day increase in HDD will lead to a rise of 0.09 million kWh in Beijing’s electricity consumption. (2) Beijing’s power consumption level in the future will show a rising trend with fluctuation. In 2060, the electricity consumption in Beijing will reach 219 to 290.4 billion kWh.

Hybrid Model Predictive Control of Floating Offshore Wind Turbines With Artificial Muscle Actuated Mooring Lines

Abstract Floating offshore wind turbines (FOWTs) are subject to undesirable platform motion and a significant increases in fatigue loads compared to their onshore counterparts. We have recently proposed using the fishing line artificial muscle (FLAM) actuators to realize active mooring line force control (AMLFC) for platform stabilization and thus load reduction, which features a compact design and no need for turbine redesign. However, as for the thermally activated FLAM actuators, a major control challenge lies in the asymmetric dynamics for the heating and the cooling half cycle of operation. In this paper, for a tension-leg platform (TLP) based FOWT with FLAM actuator based AMLFC, a hybrid dynamic model is obtained with platform pitch and roll degrees-of-freedom included. Then a hybrid model predictive control (HMPC) strategy is proposed for platform motion stabilization, with preview information on incoming wind and wave. A move blocking scheme is used to achieve reasonable computational efficiency. Fatigue, aerodynamics, structures, and turbulence (FAST) based simulation study is performed using the National Renewable Energy Laboratory (NREL) 5 MW wind turbine model. Under different combinations of wind speed, wave height and wind directions, simulation results show that the proposed control strategy can significantly reduce the platform roll and tower-base side-to-side bending moment, with a mild level of actuator power consumption.

Investigation of Power Efficiency Changes in DWDM Systems Replacing Erbium-Doped Amplifiers By Semiconductor Optical Amplifiers

Abstract To evaluate potential utilization of semiconductor optical amplifiers (SOAs) as a wideband amplification alternative to erbium doped fibre amplifiers (EDFAs) in dense wavelength division multiplexed (DWDM) coherent systems, the authors discuss changes in power consumption levels required for a single bit transmission. The research evaluates the power efficiency parameter for WDM transmission systems using both amplification schemes – EDFAs that utilise standard C-band and SOAs assuming 75 nm amplification spectral band. The power efficiency levels have been estimated for five transmission spans with maximal distance of 640 km. The standard 50 GHz channel spacing has been chosen for both system configurations to allocate 100 Gbps dual-polarization quadrature phase shift keying (DP-QPSK) optical signals. The simulation schemes are described along with the critical parameters, derived from the recent relevant studies that should be taken into account considering usage of SOAs as in-line amplifiers.

Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study.

BackgroundThe use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied.ObjectiveThe aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms—logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms—when applied to clinical laboratory datasets.MethodsWe applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5.ResultsThe experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency.ConclusionsThe XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications.

EVALUATION OF FINANCIAL AND TECHNICAL INDICATORS OF EFFICIENCY OF MICROGRID WORK IN DYNAMIC MODES

The analysis showed the significant spread and successful operation of modern local Microgrid systems, which are considered as a group of interconnected loads and dispersed energy resources within clearly defined territorial boundaries and act as a single managed object for a higher level network and can connect or disconnect from this network to be able to work in both connected and island mode. An assessment of the financial and technical performance of Microgrid in dynamic modes, in particular, the development of Microgrid to ensure optimal generation and consumption regimes in terms of their performance in local markets. The Microgrid has been identified as being able to improve the stability, reliability, quality, and safety of conventional distribution systems, making it a more reliable and useful technique for generating electricity and reducing the use of non-renewable energy sources.
 It is shown that the advantages of dynamic charging are most fully manifested at the local level, and modern interaction of participants in the market of ancillary services involves increasing the role of aggregates of aggregate and aggregates of dispersed consumption. -models with a combination of physical, communication, information, and business levels. The tariff for dynamic pricing should be considered as one of the most efficient and economical programs, in which the price of electricity changes over a period. At the same time, charging must be based on dynamic models, which should include the fundamentally dynamic nature of regulating the capacity of the system and stimulating the relevant desired actions by the consumer. The application of tariff calculation in dynamic tariffing is proposed not by time interval, but by state; summing up the real balance of energy components (instantaneous and integral values); formation for further control of optimality of levels of generation and consumption of the electric power, in particular, in the form of the reference tariff and reference profiles of generation and consumption of the electric power.
 The influence of dynamic pricing on Microgrid functions is determined taking into account the factors that affect electricity demand and depend on the modes of operation of the generator and load, in particular, with the allocation of the "reference tariff". It is substantiated that efficient dynamic pricing is possible with the use of Smart meters with minimum requirements that allow you to reliably monitor the cost of primary fuel for generation and electricity consumption in specific time intervals. The developed algorithm for calculating the price of primary fuel depending on the uneven consumption of active power over a period allows you to use dynamic charging when changing modes of operation of Microgrid generators, while providing an adequate price for consumers and producers of primary fuel and electricity supplied and consumed. Using the Frieze power modification, the developed algorithm provides for the calculation of the optimal value of active power, which corresponds to a uniform power consumption and is characterized by minimal use of primary fuel.
 The need to combine technical and economic (financial, price) indicators in business models and technical means at the Microgrid level is substantiated, which will significantly improve the process of managing electricity demand in the local electricity market. The proposed algorithm allows studying the impact of a rapid change in the level of generator power and power consumption on changes in cost indicators of the system, the introduction of demand management mechanisms and measures to improve energy efficiency.

An Open-Source Low-Cost Mobile Robot System With an RGB-D Camera and Efficient Real-Time Navigation Algorithm

Currently, mobile robots are developing rapidly and are finding numerous applications in the industry. However, several problems remain related to their practical use, such as the need for expensive hardware and high power consumption levels. In this study, we build a low-cost indoor mobile robot platform that does not include a LiDAR or a GPU. Then, we design an autonomous navigation architecture that guarantees real-time performance on our platform with an RGB-D camera and a low-end off-the-shelf single board computer. The overall system includes SLAM, global path planning, ground segmentation, and motion planning. The proposed ground segmentation approach extracts a traversability map from raw depth images for the safe driving of low-body mobile robots. We apply both rule-based and learning-based navigation policies using the traversability map. Running sensor data processing and other autonomous driving components simultaneously, our navigation policies perform rapidly at a refresh rate of 18 Hz for control command, whereas other systems have slower refresh rates. Our methods show better performances than current state-of-the-art navigation approaches within limited computation resources as shown in 3D simulation tests. In addition, we demonstrate the applicability of our mobile robot system through successful autonomous driving in an indoor environment.

Self-controlling photonic-on-chip networks with deep reinforcement learning

We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.

An efficient networking solution for extending and controlling wireless sensor networks using low-energy technologies.

Wireless sensor networks connect a set of highly flexible wireless devices with small weight and size. They are used to monitor and control the environment by organizing the acquired data at a central device. Constructing fully connected networks using low power consumption sensors, devices, and protocols is one of the main challenges facing wireless sensor networks, especially in places where it is difficult to establish wireless networks in a normal way, such as military areas, archaeological sites, agricultural districts, construction sites, and so on. This paper proposes an approach for constructing and extending Bi-Directional mesh networks using low power consumption technologies inside various indoors and outdoors architectures called “an adaptable Spider-Mesh topology”. The use of ESP-NOW protocol as a communication technology added an advantage of longer communication distance versus a slight increase of consumed power. It provides 15 times longer distance compared to BLE protocol while consuming only twice as much power. Therefore, according to our theoretical and experimental comparisons, the proposed approach could provide higher network coverage while maintaining an acceptable level of power consumption.

Low Optical Writing Energy Multibit Optoelectronic Memory Based on SnS2 /h-BN/Graphene Heterostructure.

With the rapid development of artificial intelligence and neural network computing, the requirement for information storage in computing is gradually increasing. Floating gate memories based on 2D materials has outstanding characteristics such as non-volatility, optical writing, and optical storage, suitable for application in photonic in-memory computing chips. Notably, the optoelectronic memory requires less optical writing energy, which means lower power consumption and greater storage levels. Here, the authors report an optoelectronic memory based on SnS2 /h-BN/graphene heterostructure with an extremely low photo-generated hole tunneling barrier of 0.23eV. This non-volatile multibit floating gate memory shows a high switching ratio of 106 and a large memory window range of 64.8V in the gate range ±40V.And the memory device can achieve multilevel storage states of 50 under a low power light pulses of 0.32nW and small light pulse width of 50ms. Thanks to the Fowler-Nordheim tunneling of the photo-generated holes, the optical writing energy of the optoelectronic memory has been successfully reduced by one to three orders of magnitude compared to existing 2D materials-based systems.

Algorithm for implementing energy-efficient traction substations on the subway

The work is devoted to the modernization of traction substations on the subway. The paper deals with the problem of using energy-saving technologies in the subway. The practical value of the work results lies in solving an urgent problem aimed at reducing the level of power consumption in the metro system. The paper considers the possibility of modernizing traction substations by using multiphase solutions that improve the stabilization properties of the rectified voltage and reduce the loss of electricity by energy output to storage devices installed on the vehicle. Circuitry solutions have been developed for transformer-rectifier units (TRU) with 24-fold rectified voltage ripple, which make it possible to reduce active power losses in transformer equipment and valve structures and to increase the stabilizing properties of the rectified voltage.

Intelligent Miniature Circuit Breaker

The traditional electrical distribution panel (or breaker panel) is a system that divides the main electrical power feed and distributes them to subsidiary circuits whiles providing a protective mechanism via the use of miniature circuit breakers, residual current devices, etc. The conventional panel distributes electrical power alright but the system does not make provision for any form of real time monitoring and feedback of power consumption levels in the home. This paper presents a design of a miniature circuit breaker distribution panel integrated with other electronic devices which helps achieve real time monitoring of power consumption and also automatically trips the circuit if there is a fault and reconnects the circuit if the fault is cleared to ensure little to no interruption in electricity to appliances.

Designing Ultra-low-power Cardiac Pacemaker with Quantum Cellular Automation Technology

The heartbeat is triggered by a sinoatrial node in the heart. If the sinoatrial node is disrupted for any reason or if there is a problem with the heart's electrical signal path, the heart rate will decrease or become impaired; in which case the cardiac pacemaker could control the heart function. The pacemaker is an electrical stimulator that causes the heart to expand and contract and triggers pulses to the heart when needed or permanently. Since the pacemaker is placed inside the patient's body, it should be designed based on the minimum power consumption. Besides, frequency adjustment in this device is necessary to regulate heart rate in a variety of arrhythmias. In this paper, logic cells of quantum cellular automata are utilized to design a pulse generator circuit in a heart oscillator, where power consumption and dimensions are minimal. An important feature of the proposed circuit is the ability to adjust the output pulse frequency. The efficiency of this circuit has been evaluated using QCAdesigner simulator and desirable results have been obtained in terms of power consumption level. The simulation results also show very low power consumption for the designed circuit.

A 2.63 μW ECG Processor With Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device.

An ultra-low power ECG processor ASIC (application specific integrated circuit) with R-wave detection and data compression is presented, which is designed for the long-term implantable cardiac monitoring (ICM) device for arrhythmia diagnosis. An adaptive derivative-based detection algorithm with low computation overhead for potential arrhythmia recording is proposed to detect arrhythmia with the occasional abnormal heart beats. In order to save as much as possible cardiac information with the limited memory size available in the ICM device, a hierarchical data buffer structure is proposed which saves 3 types of data, including the raw ECG data segments of 2 seconds, compressed ECG data segments of 45 seconds, and R-peak values and interval lengths of >2000 beat cycles. A modified swinging-door-trending (SDT) method is proposed for the ECG data compression. The ASIC has been implemented based on fully-customized near-threshold standard cells using the thick-gate transistors in 65-nm CMOS technology for low dynamic power consumption and leakage. The ASIC core occupies a die area of 1.77 mm2. The measured total power is 2.63 μW, which is among the ECG processors with the lowest core power consumption. It exhibits a relatively high positive precision rate (P+) of 99.3% with a sensitivity of 98.2%, in contrast to the similar designs in literature with the same core power consumption level. Also, an ECG data compression ratio (CR) of up to 17.0 has been achieved, with a good trade-off between the compression efficiency and loss.

Research of the optimal method of control of the first rise pumps electric drives operated in conditions of negative temperatures

OBJECT. The work is aimed at solving an important problem of increasing the economic efficiency, the level of resource saving and accident-free operation of the first rise section of the centralized water supply systems of small and medium-sized settlements. At the same time, the task is to minimize the cost of electricity associated with the excess performance of pumps, while ensuring the protection of the pipeline from freezing in conditions of negative ambient temperatures. METHODS. To solve this problem, an optimal method for controlling electric drives of first rise pumps at the request of the end user of water was developed, which differs from the existing ones by analyzing the state of the thermal parameters of the pipeline. The method is based on the calculation of control actions based on the water level in the storage reservoir or the water pressure in the pipeline UL and the water temperature at the end of the pipeline UT, followed by the calculation of the optimal pump supply setpoint, which is then maintained by the frequency control method. The Matlab® Simulink simulation model was used to determine the optimal laws for calculating control actions and evaluate the effectiveness of the proposed solutions. RESULTS. Based on the simulation results, it was found that it is advisable to use the PID law for calculating the control effect for the UL level, the PD law for the UT temperature, and the PI law for the flow rate. The parameters of the technological process and the level of power consumption in the control process were also evaluated. CONCLUSIONS. Studies of the proposed concept of managing the first rise section have shown high technical and economic efficiency, in particular, the savings in electricity consumption are estimated at about 22 % while ensuring the protection of the pipeline from freezing.

Research of the optimal method of control of the first rise pumps electric drives operated in conditions of negative temperatures

OBJECT. The work is aimed at solving an important problem of increasing the economic efficiency, the level of resource saving and accident-free operation of the first rise section of the centralized water supply systems of small and medium-sized settlements. At the same time, the task is to minimize the cost of electricity associated with the excess performance of pumps, while ensuring the protection of the pipeline from freezing in conditions of negative ambient temperatures. METHODS. To solve this problem, an optimal method for controlling electric drives of first rise pumps at the request of the end user of water was developed, which differs from the existing ones by analyzing the state of the thermal parameters of the pipeline. The method is based on the calculation of control actions based on the water level in the storage reservoir or the water pressure in the pipeline UL and the water temperature at the end of the pipeline UT, followed by the calculation of the optimal pump supply setpoint, which is then maintained by the frequency control method. The Matlab® Simulink simulation model was used to determine the optimal laws for calculating control actions and evaluate the effectiveness of the proposed solutions. RESULTS. Based on the simulation results, it was found that it is advisable to use the PID law for calculating the control effect for the UL level, the PD law for the UT temperature, and the PI law for the flow rate. The parameters of the technological process and the level of power consumption in the control process were also evaluated. CONCLUSIONS. Studies of the proposed concept of managing the first rise section have shown high technical and economic efficiency, in particular, the savings in electricity consumption are estimated at about 22 % while ensuring the protection of the pipeline from freezing.