Power Estimation Model Research Articles

A photovoltaic power estimation model based on the improved local cloud occlusion index algorithm considering sun block luminance

A photovoltaic power estimation model based on the improved local cloud occlusion index algorithm considering sun block luminance

Power estimation models of a 7-axis robotic arm with simulated manufacturing applications

Power estimation models of a 7-axis robotic arm with simulated manufacturing applications

Retraction Note: Adaptive wavelet ELM-fuzzy inference system-based soft computing model for power estimation in sustainable CMOS VLSI circuits

Retraction Note: Adaptive wavelet ELM-fuzzy inference system-based soft computing model for power estimation in sustainable CMOS VLSI circuits

Neural network energy management strategy for plug-in hybrid electric combine harvesters based on quasi-periodic samples

Energy management strategies are crucial for Plug-in Hybrid Electric Combine Harvester (PHECH). However, many existing approaches rely on rigid, pre-setting rules that struggle to adjust to the PHECH operational conditions. This paper first introduces a power estimation model tailored to the quasi-periodic process of harvester activity. Then, Dynamic Programming (DP) is applied to derive optimal samples of engine power ratio across various scenarios. Building on the samples, a Neural Network (NN) is developed to enhance the strategy's economic and real-time performance. Simulation tests evaluate the proposed algorithm's efficacy and its energy conservation potential. The findings suggest that, compared to fuel-driven harvesters, the NN strategy achieves similar energy cost savings to the DP approach, exceeding 11%, which is better than the Charge Depleting and Charge Sustaining (CDCS) strategy's 7.22% and the MPC-ECMS strategy's 7.87%. Moreover, the NN strategy reduces the time expense to roughly one-fifth of that required by the DP approach.

Development of Thrust, Torque, and Power Estimation Model, and Prediction Performance of Earth Pressure Balance Tunnel Boring Machine in Mixed-Face Strata

Development of Thrust, Torque, and Power Estimation Model, and Prediction Performance of Earth Pressure Balance Tunnel Boring Machine in Mixed-Face Strata

BPNS: A Fast and Accurate Power Estimation Model for Embedded Devices

The surge in the intelligent information age has led to a growing need for enhanced computing power in smart mobile devices. However, the accompanying increase in computing power brings about a significant challenge — high power consumption. This poses a notable hurdle for mobile devices that prioritize sustainability performance. Effectively addressing power consumption estimation is crucial for overcoming this sustainability issue. In this paper, we propose using a branching parallel neural networks (BPNS) that consists of FCNN-LSTM and a attention mechanism to estimate the power consumption of a device. In addition, a data stabilization module is incorporated into the framework to improve the power estimation accuracy. We implement and assess the performance of BPNS for online estimation across diverse smart mobile devices, conducting a comparative analysis with commonly used neural networks. Notably, our approach achieves a deviation of 0.28W, 0.65W and 0.76W for online estimation on three smart mobile devices, and outperforms other methods in terms of estimation accuracy and computational cost.

An automated energy management framework for smart homes

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption.

Photovoltaic output power estimation model based on multi-meteorological variable dimension reduction and improved variational mode decomposition algorithm

ABSTRACT The instability and intermittence of photovoltaic (PV) power generation challenge the large-scale integration of PV power generation systems into the power grid and the development of energy storage systems. To accurately predict the output power of PV power generation, a power prediction model based on the optimized variational modal decomposition (OVMD)-adaptive t-distribution sparrow search algorithm (tSSA)-least squares support vector machine (LSSVM) algorithm was built. The performance of the model was then verified by three-year meteorological data and real-time PV output power data of two different climatic regions in China. The mean absolute percentage error (MAPE) and root mean square error (RMSE) of this model were less than 3.1% and 0.40, respectively, and the determination coefficient (R2) of this model was greater than 95%. Moreover, the prediction accuracy was greatly improved compared to models based on the support vector machine (SVM), LSSVM, variational modal decomposition (VMD)-LSSVM, and VMD-SSA-LSSVM algorithms. Finally, by performing dimension reduction of meteorological variables related to PV output power, the performance of the proposed OVMD-tSSA-LSSVM model was further improved.

Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction

AbstractAccurate estimation of wind power is essential for predicting and maintaining the power balance in the power system. This paper proposes a novel approach to enhance the accuracy of wind power estimation through a hybrid model integrating neural networks and error discrimination‐correction techniques. In order to improve the accuracy of estimation, a bidirectional gating recurrent unit is developed, forming an initial wind power estimation curve through training. Additionally, a sequential model‐based algorithmic configuration optimizes bidirectional gating recurrent unit's network hyperparameters. To tackle estimation errors, a multi‐layer perceptron combined with sequential model‐based algorithmic configuration is employed to create a classification model that automatically discerns the quality of estimates. Subsequently, an innovative correction model, based on grey relevancy degree and relevancy errors, is devised to rectify erroneous estimates. The final estimates result from a summation of the initial estimates and the values derived from error corrections. By analysing the real data from a wind farm in northwest China, a simulation test validates the proposed hybrid model. Experimental results demonstrate a substantial improvement in modelling accuracy when compared to the initial model.

Comparative Assessment of Physical and Machine Learning Models for Wind Power Estimation: A Case Study for Hungary

Comparative Assessment of Physical and Machine Learning Models for Wind Power Estimation: A Case Study for Hungary

Multilayer LSTM Model for Wind Power Estimation in the Scada System

Wind energy is clean energy that does not pollute the environment. However, the complex and variable operating environment of a wind turbine often makes it difficult to predict the instantaneous active power generated. In this study, a wind turbine active power estimation system based on a short-term memory network (LSTM) using time series analysis is proposed. The data obtained from the wind turbine SCADA system is used as input variables. In the proposed method, a multilayer LSTM architecture is designed to train the model. The first LSTM network consists of 64 units, and the second one consists of 32 units. This is followed by a dense layer consisting of 16 neurons. In the last layer, the architecture is finalized by using a linear activation function for the prediction process. The proposed deep learning (DL)-based LSTM prediction model takes into account environmental factors such as wind speed and wind direction for active power forecasting. The results show that the LSTM-based time series analysis method is capable of effectively capturing time series features among the data. Thus, the proposed architecture can realize high-accuracy active power forecasting.

Development of a ship performance model for power estimation of inland waterway vessels

A ship performance model is an important factor in energy-efficient navigation. It formulates a speed–power relationship that can be used to adjust the engine loads for dynamic energy optimisation. However, currently available models have been developed for sea-going vessels, where the environmental conditions are significantly different from those experienced on inland waterways. Inland waterway shipping has great potential to become a mode of transport that can both improve safety and reduce emissions. Therefore, this paper presents the development of an energy performance model specifically for inland waterway vessels (IWVs). The holistic ship energy system model is based on empirical methods, from resistance to engine performance prediction, established in a modular code architecture. The resistance and propulsion prediction in confined waterways are captured by a newly developed method, considering a superposing of shallow water and bank effect. Verification against model tests and high-fidelity simulations indicate that the selected empirical methods achieved good accuracy for predicting ship performance. The resistance prediction error was 5.2% for single vessels and 8% for pusher-barge convoys based on empirical methods. The results of a case study investigating the performance of a self-propelled vessel under dynamic waterway data, indicate that the developed model could be used for onboard power monitoring and energy optimisation during operation.

Study on the wireless sensor networks routing for Low-Power FPGA hardware in field applications

Very intelligent compact sensors with low-power and inexpensive solutions have been created because of the industrial revolution and subsequent advancements in electrical technology and wireless communications. Modern innovative enhancements have been made for the utilization of small, low-price, and low-power wireless communication devices with fast processing capabilities. The sensor node communication and routing play a significant role in the configuration of a specific WSN environment. There are three types of WSN routing: proactive, reactive, and hybrid. DSDV and OLSR are two proactive routing technologies. AODV and DSR are reactive routing technologies. DSDV Routing requires a progress update of its routing tables even when the network is not in use, which uses battery power and generates negligible data. As a result, this protocol is inappropriate for networks that are both huge in scope and very dynamic. OLSR maintains track of all potential solutions in a routing table. The overhead from control messages rises as there are more mobile hosts. It requires more processing power than other protocols while looking for alternate routes. The DSR protocol relies on the minimal hop count parameter to pick the way without considering other parameters that impact routing algorithm performance, such as energy consumption, residual energy, and connection stabilities. The FPGA realization provides an ascendable computing environment for WSN planning in a specific region. The research articles emphasize the hardware chip simulation and analysis of these routing protocols which can be further applied for field applications and configuration The network performance is evaluated using several performance metrics such as throughput, control overhead, delay, packet delivery ratio, and power consumption. This study offers a key method for creating FPGA-based power estimation and verification models for a WSN planning environment.

PM2VE: Power Metering Model for Virtualization Environments in Cloud Data Centers

Virtualization technologies provide solutions for cloud computing. Virtual resource scheduling is a crucial task in data centers, and the power consumption of virtual resources is a critical foundation of virtualization scheduling. Containers are the smallest unit of virtual resource scheduling and migration. Although many practical models for estimating the power consumption of virtual machines (VMs) have been proposed, few power estimation models of containers have been put forth. In this paper, we propose a fast-training piecewise regression model based on a decision tree for VM power metering and estimate the power of containers configured on the VM by treating the container as a group of processes on the VM. We select appropriate features from the collected metrics of VMs/containers to help our model fit the nonlinear relationship between power and features well. Besides, we optimize the leaf nodes of the regression tree, realizing the effective power metering of virtualization environments. We evaluate the proposed model on 13 tasks in PARSEC and compare it with several commonly used models in data centers. The experimental results prove the effectiveness of the proposed model, and the estimated power of containers is in line with expectations.

A Grey-box model approach using noon report data for trim optimization

Trim optimization improves the energy efficiency of ships, thus reducing operational costs and emissions; however, trim tables are only available for a limited number of ships. There is thus a desire to develop additional, more accurate trim tables without the need for expensive model testing. The objective of this research was to develop a method to decrease fuel consumption by trim optimization, by a dynamic shaft power estimation model based on available operational data. A method that uses noon report data and a grey-box modelling approach is proposed. The grey box model consists of a multi-layer feedforward neural network to estimate the required shaft power, using operational parameters and an initial estimate of the required shaft power. A case study is presented for a modern chemical tanker and sea trials have been conducted to validate the results. The method provides correct trim advice for full load conditions; however, the magnitude of the effect is smaller compared to sea trial results. The model is able to estimate the required power with an average accuracy of over 6% for a random subset of the noon report data. Due to challenges inherent to noon reports as a data source, the actual effect of trim and speed have a bigger magnitude than the extracted trend.

Adaptive power load restoration considering flexible control of air conditioners for resilience enhancement

Frequent extreme hot weather brings a surge of air conditioner load and further increases the probability of outages. Fitting to different power grid operating conditions and outage scenarios, an adaptive load restoration strategy is proposed, which can take full use of flexible control of air conditioners and reliable power sources from the power grid and distributed generations or microgrids. Considering the influence of outage duration and environmental temperature, an air conditioner aggregated power estimation model is established to evaluate the cold load pickup amount. Based on the allowable maximum load pickup amount, a load pickup amount reduction method by flexible control of air conditioners considering the impact degree on the customer is proposed to guarantee secure load pickup operation. Simulation results of the IEEE 14-bus system and a practical power grid demonstrate that the proposed method can restore the power supply of customers rapidly and securely to enhance power grid resilience under extreme hot weather.

On Side-Channel Analysis of Memristive Cryptographic Circuits

Memristive technologies offer fascinating opportunities for unconventional computing architectures and emerging applications. While memristive devices have received substantial attention as sources of entropy for security applications, security vulnerabilities of memristive technologies for implementing cryptographic circuits have been largely neglected so far. In this article, we provide the first in-depth analysis of power side-channel analysis against memristive cryptographic implementations based on both: physical experiments and simulations. We show that power consumption models developed for CMOS are not fully adequate for memristive circuits. In particular, the memory effect makes even input-independent initialization cycles vulnerable to attacks that would be fundamentally impossible in CMOS technologies. We propose a memristive-oriented Power Estimation Model (mPEM) integrated into the Stochastic Approach (StA) framework and demonstrate its effectiveness against larger-scale circuits. Finally, we demonstrate that attack countermeasures that were effective for CMOS fail for fundamental reasons in the memristive case.

A novel energy-efficient actuator integrated with compact variable gravity compensation module

• Modularized CVGCm by 3D shape design and improved its performance. • Novel energy-efficient actuator that can compensate for the gravitational torque. • Power estimation model of the VGCA based on the Lagrangian method is presented. • Reduced rate of current and power in static and dynamic motion is 63.1 % and 64.0 %, respectively. This paper proposes a new type of energy-efficient actuator called variable gravity compensation module integrated actuator (VGCA). VGCA improves the energy efficiency by compensating the gravitational torque at the target joint. Additionally, the variability of compensation torque can further improve the energy efficiency by dealing with the variation of payload, which is required in many robotic applications. As a core part of the VGCA, a compact variable gravity compensation module, called CVGCm , is a cylindrical compact modular unit based on the cam and variable pivot of the lever mechanism. By the theoretically designed cam and lever profiles, the CVGCm can generate a non-linear compensation torque and energy-free variability. First, the functional principles of CVGCm and VGCA are explained. Next, implementation detail and manufacturing of VGCA are introduced. Subsequently, the power estimation model of the actuator is explained based on the Lagrangian method. The experimental results showed that the CVGCm achieved a rapid change of the compensation torque in the compact module. Furthermore, compared to the actuator without CVGCm, VGCA showed a 63.1% reduction of current in static motion and a 64.0% reduction of power in dynamic motion.

Power and Energy Consumption Models for Embedded Applications

This paper describes a study on the power and energy consumption estimation models that have been defined to facilitate the development of ultra-low power embedded applications. During the study, various measurements have been carried out on the instruction and application level to challenge the models against empirical data. The study has been performed on the multicore heterogeneous hardware platform developed for ultra-low power Digital Signal Processors (DSP) applications. The final goal was to develop a tool that can provide insight into power dissipation during the execution of embedded applications, so that one can refactor the source code in an energy-efficient manner, or ideally to develop an energy-aware C compiler. The side effect of the research presents interesting insight into how the custom hardware architecture influences power dissipation. The selected platform has been chosen simply because it represents R&D state of the art ultra-low power hardware used in hearing aids. The presented solution has been developed and tested in an Eclipse environment using Java programming language.

A One-Stage Ensemble Framework Based on Convolutional Autoencoder for Remaining Useful Life Estimation.

As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In legacy studies, a machine conducting a single continuous operation has been mainly observed for power estimation. However, the production machine of a modularized line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of this kind of production line, it is important to interpret mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline—from data collection (from different sources) to preprocessing, data conversion, synchronization, and deep learning classification—to estimate the total power use of the future process plan, is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building an power estimation model without manual data preprocessing. The proposed system is applied to a modular factory, connected with machine controllers, using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline, with the result of the power profile being synchronized with the robot program.