Energy Estimation Error Research Articles

Variational denoising for variational quantum eigensolver

The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems that are currently intractable on classical computers. VQE trains parameterized quantum circuits using a classical optimizer to approximate the eigenvalues and eigenstates of a given Hamiltonian. However, VQE faces challenges in task-specific design and machine-specific architecture, particularly when running on noisy quantum devices. This can have a negative impact on its trainability, accuracy, and efficiency, resulting in noisy quantum data. We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE by learning from noisy VQE outputs. Our approach can significantly decrease energy estimation errors and increase fidelities with ground states compared to noisy input data for the H2, LiH, and BeH2 molecular Hamiltonians and the transverse field Ising model. Surprisingly, it only requires noisy data for training. Variational denoising can be integrated into quantum hardware, increasing its versatility as an end-to-end quantum processing for quantum data. Published by the American Physical Society 2024

An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures

The LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This paper introduces an artificial intelligence-based electrical–thermal coupling battery model, presents an application-oriented procedure to estimate SOC and RAE for a reliable and effective battery management system, and puts forward a model-based strategy to control the battery thermal state in low temperature. Firstly, an LFP battery electrical model based on artificial intelligence is proposed to estimate the terminal voltage, and a thermal resistance model with an EKF estimation algorithm is established to assess the temperature distribution in the battery pack. Then, the electrical and thermal models are coupled, a closed-loop EKF algorithm is employed to estimate the battery SOC, and a fusion method is discussed. The coupled model is simulated under a given protocol and RAE can be obtained. Finally, based on the electrical–thermal coupling model and RAE calculation algorithm, a preheating method and constant power condition-based RAE estimation are discussed, and the thermal management strategy of the battery system under low temperature is formed. Results show that the estimation error of SOC can be controlled within 2% and RAE can be controlled within 4%, respectively. The preheating strategy at low temperature and low SOC can significantly improve the energy output of the battery pack system.

Dynamic Connectivity Analysis Using Adaptive Window Size

In this paper, we propose a new method to study and evaluate the time-varying brain network dynamics. The proposed RICI-imCPCC method (relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient) is based on an adaptive window size and the imaginary part of the complex Pearson correlation coefficient. It reduces the weaknesses of the existing method of constant sliding window analysis with narrow and wide windows. These are the low temporal precision and low reliability for short connectivity periods for wide windows, and high susceptibility to noise for narrow windows, all resulting in low estimation accuracy. The proposed method overcomes these shortcomings by dynamically adjusting the window width using the RICI rule, which is based on the statistical properties of the area around the observed sample. In this paper, we compare the RICI-imCPCC with the existing constant sliding window analysis method and describe its advantages. First, the mathematical principles are established. Then, the comparison between the existing and the proposed method using synthetic and real electroencephalography (EEG) data is presented. The results show that the proposed RICI-imCPCC method has improved temporal resolution and estimation accuracy compared to the existing method and is less affected by the noise. The estimation error energy calculated for the RICI-imCPCC method on synthetic signals was lower by a factor of compared to the error of the constant sliding window analysis using narrow window size imCPCC, by a factor of compared to using wide window size imCPCC, by a factor of compared to using narrow window size wPLI, and by a factor of compared to using wide window size wPLI. Analysis of the real signals shows the ability of the proposed method to detect a P300 response and to detect a decrease in dynamic connectivity due to desynchronization and blockage of mu-rhythms.

Heterogeneity Analysis of Operating Mode Distribution for Modeling Energy Consumption of Light-Duty Vehicles

The use of vehicle operating mode (OpMode) distribution is widely accepted for estimating energy consumption and emissions in the Motor Vehicle Emission Simulator (MOVES) model. However, the heterogeneity of driving behavior may lead to errors when using the default OpMode distribution. To improve the accuracy of energy consumption estimations, it is necessary to recognize the heterogeneity in OpMode distribution among different driving behaviors. With this aim, this paper designs a speed-specific indicator of energy efficiency reflecting driving behavior based on the speed-specific vehicle-specific power (VSP) distribution. The paper uses field data from 26,082 drivers recorded second by second during workdays. It also discusses the intra-heterogeneity and inter-heterogeneity of driving behavior based on unsupervised algorithm clustering. The findings of this paper are as follows. (1) The speed-specific VSP distribution clearly reflects the differences in energy efficiency of individuals’ driving behavior. (2) The energy efficiency indicator reflects the multidimensional inter-heterogeneity and intra-heterogeneity of driving behavior. (3) Drivers’ varied driving behavior causes heterogeneity in energy efficiency at different speeds, possibly causing an error of 6.34% in the emissions estimations. (4) Drivers of electric vehicles (EVs) and hybrid electric vehicles (HEVs) show more aggressive driving behaviors than drivers of conventional vehicles (CVs), which may cause an energy estimation error of over 6% for EVs and HEVs. Thus, the OpMode distribution of EVs, HEVs, and CVs should be modeled separately for on-road energy estimations.

Modelling and quantitation of embodied, operational and mobile energies of buildings: a holistic review from 2012 to 2021

PurposeThis paper proposes an intact framework for building life cycle energy estimation (LCEE), which includes three major energy sources: embodied, operational and mobile.Design/methodology/approachA systematic review is conducted to summarize the selected 109 studies published during 2012–2021 related to quantifying building energy consumption and its major estimation methodologies, tools and key influence parameters of three energy sources.FindingsResults show that the method limitations and the variety of potential parameters lead to significant energy estimation errors. An in-depth qualitative discussion is conducted to identify research knowledge gaps and future directions.Originality/valueWith societies and economies developing rapidly across the world, a large amount of energy is consumed at an alarming rate. Unfortunately, its huge environmental impacts have forced many countries to take energy issues as urgent social problems to be solved. Even though the construction industry, as the one of most important carbon contributors, has been constantly and academically active, researchers still have not arrived at a clear consensus for system boundaries of life cycle energy. Besides, there is a significant difference between the actual and estimated values in countless current and advanced energy estimation approaches in the literature.

Fast energy estimation framework for long-running applications

The computation power in data center facilities is increasing significantly. This brings with it an increase of power consumption in data centers. Techniques such as power budgeting or resource management are used in data centers to increase energy efficiency. These techniques require to know beforehand the energy consumption throughout a full profiling of the applications. This is not feasible in scenarios with long-running applications that have long execution times. To tackle this problem we present a fast energy estimation framework for long-running applications. The framework is able to estimate the dynamic CPU and memory energy of the application without the need to perform a complete execution. For that purpose, we leverage the concept of application signature. The application signature is a reduced version, in terms of execution time, of the original application. Our fast energy estimation framework is validated with a set of long-running applications and obtains RMS values of 11.4% and 12.8% for the CPU and memory energy estimation errors, respectively. We define the concept of Compression Ratio as an indicator of the acceleration of the energy estimation process. Our framework is able to obtain Compression Ratio values in the range of 10.1 to 191.2.

Robust H2/H∞ Fuzzy Filtering for Nonlinear Stochastic Systems with Infinite Markov Jump

Robust fuzzy filterings for a class of nonlinear stochastic systems with infinite Markov jump are explored based on T-S fuzzy model approach in this paper. First of all, the exponentially mean square stable filter with a prescribed Hoo performance is designed via linear matrix inequalities (LMIs). Secondly, H2/H∞ fuzzy filtering is solved in terms of LMIs by minimizing the estimation error energy under the worst case fuzzy disturbance. The corresponding fuzzy filter parameters can be received by convex optimization algorithms. And lastly, two illustrative examples are performed to manifest the validity of the proposed methods.

Real-time Adaptive State of Energy Estimation of Lithium-ion Batteries Applied in Electric Vehicles

State of energy estimation of lithium-ion batteries applied in electric vehicles is required for users to predict the battery recharge time. The paper developed a new mathematical model for estimating state of energy in real-time. The recursive least squares method with an optimal forgetting factor was used to identify model parameters, and the adaptive extended Kalman filter was used to estimate the state of energy. Experimental results indicated that the developed method can realize accurate model parameter estimation with modeling error less than 2 mV. The state of energy estimation error was less than 2%. The developed method can still estimate accurate state of energy even if an erroneous initial state of energy value was available.

An error correction framework for sequences resulting from known state-transition models in Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM), the set of techniques used for disaggregating total electricity consumption in a building into its constituent electrical loads, has recently received renewed interest in the research community, partly due to the roll-out of smart metering technology worldwide. Event-based NILM approaches (i.e., those that are based on first segmenting the power time-series and associating each segment with the operation of electrical appliances) are a commonly implemented solution but are prone to the propagation of errors through the data processing pipeline. Thus, during energy estimation (the final step in the process), many corrections need to be made to account for errors incurred during segmentation, feature extraction and classification (the other steps typically present in event-based approaches). A robust framework for energy estimation should use the labels from classification to (1) model the different state transitions that can occur in an appliance; (2) account for any misclassifications by correcting event labels that violate the extracted model; and (3) accurately estimate the energy consumed by that appliance over a period of time. In this paper, we address the second problem by proposing an error-correcting algorithm which looks at sequences generated by Finite State Machines (FSMs) and corrects for errors in the sequence; errors are defined as state transitions that violate the said FSM. We evaluate our framework on simulated data and find that it improves energy estimation errors. We further test it on data from 43 appliances collected from 19 houses and find that the framework significantly improves errors in energy estimates when compared to the case with no correction in 19 appliances, leaves 17 appliances unchanged, and has a slightly negative impact on 6 appliances.

Probability density function selection based on the characteristics of wind speed data

The probabilistic approach has an important place in the wind energy research field as it provides cheap and fast initial information for experts with the help of simulations and estimations. Wind energy experts have been using the Weibull distribution for wind speed data for many years. Nevertheless, there exist cases, where the Weibull distribution is inappropriate with data presenting bimodal or multimodal behaviour which are unfit in high, null and low winds that can cause serious energy estimation errors. This paper presents a procedure for dealing with wind speed data taking into account non-Weibull distributions or data treatment when needed. The procedure detects deviations from the unimodal (Weibull) distribution and proposes other possible distributions to be used. The deviations of the used distributions regarding real data are addressed with the Root Mean Square Error (RMSE) and the annual energy production (AEP).

An energy-based sparse representation of ultrasonic guided-waves for online damage detection of pipelines under varying environmental and operational conditions

This work addresses the main challenges in real-world application of guided-waves for damage detection of pipelines, namely their complex nature and sensitivity to environmental and operational conditions (EOCs). Different propagation characteristics of the wave modes, their distinctive sensitivities to different types and ranges of EOCs, and to different damage scenarios, make the interpretation of diffuse-field guided-wave signals a challenging task. This paper proposes an unsupervised feature-extraction method for online damage detection of pipelines under varying EOCs. The objective is to simplify diffuse-field guided-wave signals to a sparse subset of the arrivals that contains the majority of the energy carried by the signal. We show that such a subset is less affected by EOCs compared to the complete time-traces of the signals. Moreover, it is shown that the effects of damage on the energy of this subset suppress those of EOCs. A set of signals from the undamaged state of a pipe are used as reference records. The reference dataset is used to extract the aforementioned sparse representation. During the monitoring stage, the sparse subset, representing the undamaged pipe, will not accurately reconstruct the energy of a signal from a damaged pipe. In other words, such a sparse representation of guided-waves is sensitive to occurrence of damage. Therefore, the energy estimation errors are used as damage-sensitive features for damage detection purposes. A diverse set of experimental analyses are conducted to verify the hypotheses of the proposed feature-extraction approach, and to validate the detection performance of the damage-sensitive features. The empirical validation of the proposed method includes (1) detecting a structural abnormality in an aluminum pipe, under varying temperature at different ranges, (2) detecting multiple small damages of different types, at different locations, in a steel pipe, under varying temperature, (3) detecting a structural abnormality in an operating hot-water piping system, under multiple varying EOCs, such as temperature, water flow rate, and inner pressure; and (4) detecting a structural abnormality as the ratio of the damaged pipe's signals in the reference dataset increases.

Development and validation of a photographic food atlas for portion size assessment in the southern plains of Nepal.

To develop and validate a photographic food atlas of common foods for dietary assessment in southern Nepal. We created a life-sized photographic atlas of forty locally prepared foods. Between March and June 2014, data collectors weighed portion sizes that respondents consumed during one mealtime and then a different data collector revisited the household the next day to record respondents' estimations of their previous day's intakes using the atlas. Validity was assessed by percentage error, Cohen's weighted kappa (κ w) and Bland-Altman limits of agreement. Dhanusha and Mahottari districts in southern Nepal. A random sample of ninety-five adults in forty-eight rural households with a pregnant woman. Overall, respondents underestimated their intakes (mean error =-4·5 %). Rice and dal (spiced lentil soup) intakes were underestimated (-14·1 % and -34·5 %, respectively), but vegetable curry intake was overestimated (+20·8 %). Rice and vegetable curry portion size images were significantly reliably selected (Cohen's κ w (se): rice=0·391 (0·105); vegetable curry=0·430 (0·139)), whereas dal images were not. Energy intake over one mealtime was under-reported by an average of 569 kJ (136 kcal; 4·5 % error) using recall compared with the weighing method. The photographic atlas is a useful tool for field estimation of dietary intake. Average errors were low, and there was 'modest' agreement between weighed and recalled portion size image selection of rice and vegetable curry food items. Error in energy estimation was low but with wide limits of agreement, suggesting that there is scope for future work to reduce error further.

Reconstruction of Fine-Scale Auroral Dynamics

We present a feasibility study for a high frame rate, short baseline auroral tomographic imaging system useful for estimating parametric variations in the precipitating electron number flux spectrum of dynamic auroral events. Of particular interest are auroral substorms, characterized by spatial variations of order 100 m and temporal variations of order 10 ms. These scales are thought to be produced by dispersive Alfv\'en waves in the near-Earth magnetosphere. The auroral tomography system characterized in this paper reconstructs the auroral volume emission rate to estimate the characteristic energy and location in the direction perpendicular to the geomagnetic field of peak electron precipitation flux using a distributed network of precisely synchronized ground-based cameras. As the observing baseline decreases, the tomographic inverse problem becomes highly ill-conditioned; as the sampling rate increases, the signal-to-noise ratio degrades and synchronization requirements become increasingly critical. Our approach to these challenges uses a physics-based auroral model to regularize the poorly-observed vertical dimension. Specifically, the vertical dimension is expanded in a low-dimensional basis consisting of eigenprofiles computed over the range of expected energies in the precipitating electron flux, while the horizontal dimension retains a standard orthogonal pixel basis. Simulation results show typical characteristic energy estimation error less than 30% for a 3 km baseline achievable within the confines of the Poker Flat Research Range, using GPS-synchronized Electron Multiplying CCD cameras with broad-band BG3 optical filters that pass prompt auroral emissions.

Estimation of supercapacitor energy using a linear capacitance for applications in wireless sensor networks

Supercapacitor-based energy storage systems have been developed for many applications. For wireless sensor network applications, supercapacitor energy is a critical parameter for system design and power management. However, estimation of supercapacitor energy using the rated capacitance may result in a significant error. To reduce the error, this paper proposes to use a linear capacitance for supercapacitor energy estimation based on the supercapacitor physics. A linear capacitance composed of a constant capacitance and a voltage-dependent capacitance is determined by conducting a constant power charge or discharge test. Evaluation results for a variety of supercapacitor samples with different rated capacitance and voltage demonstrate that using the proposed linear capacitance is more accurate than using the rated capacitance to estimate supercapacitor energy. Moreover, the results show that using the linear capacitance determined through a high power charge experiment can minimize the energy estimation error. This observation can serve as a guideline to design the experiment in a way that the determined linear capacitance results in the minimum supercapacitor energy estimation error.

A linux implementation of the energy-based fair queuing scheduling algorithm for battery-limited mobile systems

This paper provides a concrete implementation and comprehensive assessment of the energy-based fair queuing (EFQ) scheduling algorithm based on the Linux operating system. EFQ is an extended application of the classical fair queuing algorithm in the energy domain. It is designed to provide proportional power sharing as well as effective time-constraint compliance in energy-centric power management (PM) schemes, a type of operating system-level PM schemes that are targeted at providing a battery lifetime guarantee for energy-limited mobile systems. In this paper work, the structure of the Linux completely fair scheduler (CFS) has been effectively utilized to ease the EFQ implementation and reduce the scheduling overhead. Around 150 lines of code have been added to the Linux kernel V3.3 to achieve the EFQ-related functions and to implement the system calls that are required by the Linux user space. To assess the properties of the EFQ scheduler, a test-bench based on the POSIX threads has been developed and the benchmarks of an open-source embedded suite are referred to program the threads under test. The EFQ algorithm is assessed from two aspects, energy management and real-time scheduling. Experimental results on energy management show that EFQ is more effective than the CFS scheduler in managing energy and it can achieve a proportional share of the system power regardless of on which device the energy is spent. Experimental results on real-time scheduling demonstrate that EFQ can achieve strict time-constraint compliance and a robust response time upon the increase of energy estimation error and active tasks.

On the trade-off between energy efficiency and estimation error in compressive sensing

Compressive sensing (CS) refers to the process of reconstructing a signal that is supposed to be sparse or compressible. CS has wide applications, such as in cognitive radio networks. In this paper, we investigate effective CS schemes for the trade-off between energy efficiency and estimation error. We propose an enhancement to a Bayesian estimation approach and an enhancement to the isotonic regression approach that is based on nearly isotonic regression. We also show how to compute the routing matrix for selecting active sensor nodes. The proposed enhancements are evaluated with trace-driven simulations. Considerable gaps are observed between the original approaches and the proposed enhancements in the simulation results. The near isotonic regression method achieves the best performance among all the CS schemes examined in this paper.

Dynamic Sensor Network Configuration in InfoSymbiotic Systems Using Model Singular Vectors

Data assimilation is an important data-driven application (DDDAS) where measurements of the real system are used to con- strain simulation results. This paper describes a methodology for dynamically configuring sensor networks in data assimilation systems based on numerical models of time-evolving differential equations. The proposed methodology uses the dominant model singular vectors, which reveal the directions of maximal error growth. New sensors are dynamically placed such as to minimize an estimation error energy norm. A shallow water test problem is used to illustrate our approach.

Adaptive filter support selection for signal denoising based on the improved ICI rule

Performance and simulation-based optimization of the improved intersection of confidence intervals (ICI) rule for adaptive filter support selection are presented. The improved ICI rule (refereed to as the relative intersection of confidence intervals (RICI) rule) is combined with the local polynomial approximation (LPA) method and applied to signal denoising, with the aim to enhance the signal estimation accuracy and reduce the estimation error energy. The results achieved using the RICI rule are compared to those obtained using the classical ICI rule, showing the reduction of the root mean-square error (RMSE) of up to 10 times for various classes of analyzed signals. The proposed procedure for the selection of the RICI parameters Γ and Rc, for which the RMSE is minimum, has been shown to significantly improve the quality of denoised signals.

Robust H2/H∞ Filter Design for a Class of Nonlinear Stochastic Systems with State‐Dependent Noise

This paper investigates the problem of robust filter design for a class of nonlinear stochastic systems with state‐dependent noise. The state and measurement are corrupted by stochastic uncertain exogenous disturbance and the dynamic system is modeled by Itô‐type stochastic differential equations. For this class of nonlinear stochastic systems, the robust H∞ filter can be designed by solving linear matrix inequalities (LMIs). Moreover, a mixed H2/H∞ filtering problem is also solved by minimizing the total estimation error energy when the worst‐case disturbance is considered in the design procedure. A numerical example is provided to illustrate the effectiveness of the proposed method.

A Power-Based Time Domain Passivity Control for Haptic Interfaces

In this paper, a power based time domain passivity control is presented. Compared to the energy based time domain passivity control, integration is not needed in the calculation and the energy estimation error is avoided. The new passivity observer (PO) distributes the activation of the passivity controller (PC) along the time index and helps to smooth the output of the PC. Applications of the approach to haptic interfaces are simulated. Comparisons with the two existing energy based approaches are made. Simulation results show the computational simplicity and effectiveness of the approach.