Mean Absolute Percentage Deviation Research Articles

An Electromechanical Impedance-Based Imaging Algorithm for Damage Identification of Chemical Milling Stiffened Panel

The multiple intersecting stiffeners on the chemical milling stiffened panel (CMSP) limit the application of active health monitoring methods on it. An imaging algorithm based on electromechanical impedance (EMI) and probability-weighting is proposed to achieve quantitative evaluation and localization of the damage on CMSP. The proposed algorithm compensates for the difference in sensor performance with coefficients and there is no need to determine the key parameters of the algorithm through prior experiments. In the paper, the applicability of ultrasonic guided wave (GW) and EMI on CMSP was first studied through the finite element method. Based on EMI and the mean absolute percentage deviation (MAPD), the selected damage indicator (DI), a probability-weighted damage imaging algorithm are proposed and experimentally verified. The results indicate that due to the reflection and attenuation effects of stiffeners on GW, the signal characteristics of damage scattering waves are contaminated, making it difficult to quantitatively characterize the damage from GW signals through DIs. MAPD is positively correlated with the damage degree and has consistency in characterizing the signal of different PZTs under the same working condition. The feasibility and accuracy of the proposed algorithm are verified through experiments which show a strong engineering application capability.

Performance evaluation of advanced machine learning methodologies in simulating hydrogen chloride (HCl) absorption by deep eutectic solvents

This work aims to construct the physical-based machine learning (ML) approach for estimating the hydrogen chloride (HCl) solubility in eleven different deep eutectic solvents (DESs). All DES-HCl equilibrium records versus temperatures and pressures are gathered from the literature to ensure ML universality. Then, a well-known feature selection technique was applied to identify the physically meaningful descriptors for the hydrogen bond acceptors (HBA) and hydrogen bond donors (HBD). Afterward, temperature, pressure, selected meaningful physical descriptors, and HBA/HBD molar concentrations are utilized to design ML tools to compute HCl solubility in DESs. Diverse MLs, including extreme learning machines, convolutional neural networks, long short-term memory, cascade neural networks, and extreme gradient boosting (XGBoost), are tested, and their accuracy is compared to select the best candidate. The XGBoost model predictions for HCl solubility in DESs are in acceptable agreement with the literature records. This model precisely simulates 417 DES-HCl equilibrium records in the temperature range of 293–348 K and pressures of up to 127 kPa with a correlation coefficient of 0.99621, mean absolute percentage deviation of 3.04, mean absolute error of 0.448, and root mean square error of 0.729. Results of the designed XGBoost model prove that acetamide-ethylene glycol (1:1), which can averagely dissolve up to 32.47 wt% (weight percent) of HCl, is the best DES for the HCl capture and recovery. After approving the XGBoost model reliability with different visual and numerical inspections, the Leverage method also justified its validity domain by more than 97.6%.

Damage localization on reinforced concrete slab structure using electromechanical impedance technique and probability-weighted imaging algorithm

Damage localization plays a key role in structural damage identification and health monitoring. This paper proposed a new damage localization approach using electromechanical impedance/admittance (EMI/EMA) technique integrated with probability-weighted imaging algorithm based on Gaussian distribution. The proposed approach comprises five steps namely measurement of the EMA signature from an array of piezoelectric ceramic lead zirconate titanate (PZT) transducers, computation of statistic damage indicator, establishment of regression model for the indicator, damage visualization via Gaussian distribution and final damage localization. Experimental validation of the approach was conducted on a reinforced concrete (RC) slab with artificial crack damages detected by using an array of surface-bonded PZT patches. Comparative studies on localization accuracy were performed by utilizing root mean square deviation (RMSD), baseline-changeable RMSD namely RMSDk, mean absolute percentage deviation (MAPD) and correlation coefficient (CC). Experimental results demonstrated that the proposed approach could achieve precise localization of concrete crack with error as small as 0 mm, and that using RMSDk index exhibited the highest localization accuracy in average. The proposed approach promoted the capability of the EMI technique in characterization of concrete structural damages.

Thermal−hydraulic characteristics of R32 and R410A condensation in plate heat exchangers with 1 mm chevron depth

Compact brazed plate heat exchangers (BPHEs) with low chevron depths are widely used in new heat pumps and air-conditioners for reducing inventory and improving efficiency. This study analyses the complete condensation of R410A and R32 inside BPHEs with a chevron depth of 1 mm. The effects of the mass flux and chevron patterns (V-shaped and W-shaped) on the heat transfer coefficient (HTC) and frictional pressure drop are investigated. The HTC of R32 is 30–50 % higher than that of R410A, which has a 10–20 % higher friction factor at high mass fluxes and vice versa at low mass fluxes. The HTC of R410A monotonically increases with the mass flux, whereas that of R32 does not. The W-shaped pattern results in 30 % and 15 % higher pressure drops compared with the V-shaped pattern for R410A and R32, respectively. The friction factor of R32 decreases more sharply than that of R410A as the mass flux increases, particularly at low mass fluxes. The correlations developed by Longo et al. and Zhang et al. show the best agreement with the experimental data for the HTCs of R32 and R410A, respectively, with mean absolute percentage deviations (MAPDs) of 15 %. The friction factor correlation of Zhang et al. accurately predicts the pressure drop of R32 for the W-shaped and V-shaped patterns. In addition, it accurately predicts the pressure drop of R410A for the W-shaped pattern, with an MAPD of less than 10 %, but over-estimates it by 37 % for the V-shaped pattern.

Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners

Terrestrial LiDAR scanning (TLS) has the potential to revolutionize forestry by enabling the precise estimation of aboveground biomass, vital for forest carbon management. This study addresses the lack of comprehensive benchmarking for leaf-filtering algorithms used in TLS data processing and evaluates four widely recognized geometry-based leaf-filtering algorithms (LeWoS, TLSeparation, CANUPO, and a novel random forest model) across openly accessible TLS datasets from diverse global locations. Multiple evaluation dimensions are considered, including pointwise classification accuracy, volume comparisons using a quantitative structure model applied to wood points, computational efficiency, and visual validation. The random forest model outperformed the other algorithms in pointwise classification accuracy (overall accuracy = 0.95 ± 0.04), volume comparison (R-squared = 0.96, slope value of 0.98 compared to destructive volume), and resilience to reduced point cloud density. In contrast, TLSeparation exhibits the lowest pointwise classification accuracy (overall accuracy = 0.81 ± 0.10), while LeWoS struggles with volume comparisons (mean absolute percentage deviation ranging from 32.14 ± 29.45% to 49.14 ± 25.06%) and point cloud density variations. All algorithms show decreased performance as data density decreases. LeWoS is the fastest in terms of processing time. This study provides valuable insights for researchers to choose appropriate leaf-filtering algorithms based on their research objectives and forest conditions. It also hints at future possibilities for improved algorithm design, potentially combining radiometry and geometry to enhance forest parameter estimation accuracy.

Monitoring of crack repair in concrete using spherical smart aggregates based on electromechanical impedance (EMI) technique

Cracks will inevitably occur in concrete structures or members during the construction process and service life due to aging, environmental factors, external loads, etc. To improve the strength and stability of the cracked concrete structures, many methods have been proposed to repair the cracks. However, the monitoring of the repairing process and repair quality has not been fully studied. The previous studies have proved that the spherical smart aggregates (SSAs) based on the electromechanical impedance (EMI) technique have outperformed the traditional smart aggregates (SAs) based on the EMI technique in structural health monitoring of civil structures, however, SSAs have not been applied to the monitoring of the concrete crack repair. In this work, the monitoring of the concrete crack repair using the SSAs based on the EMI technique was explored. A total of eight concrete specimens were prepared, and cracks in the concrete specimens were simulated by manually cutting under laboratory conditions. According to the principle of grouting method, two repair agents including cement paste and cement mortar were used to repair the cracks. The impedance signals of 28 d were measured, and three quantitative indicators, namely root mean square deviation, mean absolute percentage deviation, and correlation coefficient deviation were used to evaluate the quality of the concrete repair effect. The results indicate that the SSAs show excellent sensitivity and stability over the traditional SAs. In addition, the normalized values of the quantitative indicators were analyzed to distinguish the types of repair agents. A mathematical expression of exponential function was also proposed by fitting the experimental data to quantitatively evaluate and predict the repair effect of concrete cracks. Further, the influences of temperature, humidity, crack depth and width on the experimental results were analyzed and discussed. The numerical simulation was also presented to validate the reasonability of the experimental results.

A new Frontier in electric load forecasting: The LSV/MOPA model optimized by modified orca predation algorithm

Electric load forecasting is a vital task for energy management and policy-making. However, it is also a challenging problem due to the complex and dynamic nature of electric load data. In this paper, a novel technique, called LSV/MOPA, has been proposed for electric load forecasting. The technique is a hybrid model that combines the advantages of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), two powerful artificial intelligence algorithms. The hybrid model is further optimized by a newly Modified Orca Predation Algorithm (MOPA), which enhances the forecasting accuracy and efficiency. The LSV/MOPA model has been applied to historical electric load data from South Korea, covering four regions and 20 years. The LSV/MOPA model has been compared with other state-of-the-art forecasting techniques, including SVR/FFA, LSTM/BO, LSTM-SVR, and CNN-LSTM. The results show that the LSV/MOPA model with minimum average mean absolute percentage deviation error, including 365 in northern region, 12.8 in southern region, 8.6 in central region, and 30.8 in eastern region, provides the best fitting and outperforms the other techniques in terms of the Mean Absolute Percentage Deviation (MAPD) index, achieving lower values for all regions and years. The LSV/MOPA model also exhibits faster convergence and better generalization than the other techniques. This study demonstrates the effectiveness and superiority of the LSV/MOPA model for electric load forecasting and suggests its potential applications in other sectors where accurate forecasting is crucial.

Evaluation of ground conductor corrosion based on EMI technique with asymmetric sensor layout

ABSTRACT The ground conductor is prone to corrosion, which affects its ability to diverge current and the normal operation of the power system. The traditional detection method can’t detect its corrosion degree intuitively. This paper proposes a novel method based on Electromechanical impedance (EMI) technique to evaluate the corrosion degree of the grounding conductor. Firstly, based on the analysis of corrosion of metals. The change of ground conductor radius Δr is used as the corrosion parameter. A linear model of peak frequency Δf m and corrosion parameter Δr in asymmetric layout is established by analysing EMI and conductor mechanical admittance. Secondly, the EMI coupling models with different sensor layouts were built on the finite element software. It is verified that the asymmetric sensor layout effectively improves the sensitivity to corrosion. The linear model greatly improves the assessment accuracy compared with the Mean Absolute Percentage Deviation (MAPD) and Root Mean Square Deviation (RMSD) damage index. Thirdly, the proposed method is verified in a real grounding conductor corrosion experiment. The method based on the EMI technique with asymmetric sensor layout can evaluate the corrosion of the grounding conductor well.

Evaluating the capabilities of China's new satellite HJ-2 for monitoring chlorophyll a concentration in eutrophic lakes

HJ-2 (Huanjing-2) was launched in September 2020. It has a charge-coupled device (CCD) that offers a spatial resolution of 16 m and a revisiting time of two days, making it an excellent option for environmental and disaster monitoring. However, more evaluation is needed for the ability of CCD to monitor water quality. As chlorophyll a concentration (CChla) is the main indicator for water quality evaluation, in this study, the performance of the CCD sensor to monitor CChla has been verified, with a particular focus on typical eutrophic lakes. The essential findings can be summarized as follows: (1) the CCD sensor has a signal-to-noise ratio (SNR) greater than 400 in all bands except the green band, indicating that it meets the requirements for water quality monitoring; (2) the semi-empirical estimation model of CChla derived from HJ-2 CCD has a mean absolute deviation (MAD) of 8.18 μg/L, root mean square deviation (RMSD) of 10.66 μg/L and mean absolute percentage deviation (MAPD) of 20.93 %; (3) the higher temporal and spatial resolution of the CCD data enables effective monitoring of water quality parameters characterized by high spatiotemporal variations; (4) HJ-2 CCD exhibits strong consistency with Sentinel-2 MSI and Sentinel-3 OLCI across various bands, with most bands showing correlation coefficients exceeding 0.8 and a MAPD below 30 %. This study reveals the significant potential and practical value of HJ-2 CCD data for monitoring and assessing water quality, providing important information for the design and development of next-generation satellites.

Comprehensive kinetic modeling of the Fischer Tropsch synthesis over the Ru–Co@C(Z-d)@void@CeO2 catalyst based on a molecular dynamics evaluated mechanism

Fischer Tropsch synthesis (FTS) is a promising catalytic solution to achieve ultra-clean fuel in which catalyst has a significant role. Even though several kinetic models have been rendered for it, representing a new one based on a meticulous mechanism is noteworthy, particularly when employing a novel catalyst. Hence, in this work, molecular dynamic (MD) simulation is utilized to identify the appropriate pathway from the viewpoint of minimum energy of the elementary reactions. In this regard, we consider a majority of plausible FTS elementary reactions over Ru–CO@C(Z-d)@void@CeO2 (cobalt carbon-MOF based plus ruthenium and ceria with porous structure) catalyst, and by employing MD computations forward and reverse activation energy barrier values for each reaction are evaluated. Then, based on the minimum energy pathway (MEP), the appropriate mechanism is derived, which is inferred from the H-assisted CO dissociation pathway for the beginning step. Subsequently, a kinetic model is developed based on this mechanism, and the model parameters are correlated and optimized employing the genetic and Levenberg-Marquardt algorithms, respectively. In addition, we use various statistical methods to assess the significance of the model. For instance, the Mean Absolute Percentage Deviation (MAPD) of the correlated values from the experimental value is 4.46%. Additionally, the relative residual percentage (RR%) plots illustrate that 99% of the correlated data have less than a 10% deviation.

Towards resilient operation of photovoltaic-thermal collector with incorporated organic phase change material: Numerical and experimental investigation

The resilient operation of the novel free-standing photovoltaic-thermal (PVT) collector with an integrated phase change material (PCM) in variable weather conditions was examined by developing the computational fluid dynamics (CFD) 3D model to perform numerical analysis of heat transfer. The developed transient multi-parameter numerical model was directly compared with experimental measurements for the case of variable cloudiness, unstable ambient temperature, and fluctuating wind. Due to volatile weather conditions, the numerical analysis included variable time-dependent boundary conditions implemented using user-defined functions (UDF). The investigation considered a novel multi-parameter numerical approach, together with novel experimental data for unconventional organic PCM. Specifically, the thermal characteristics of unconventional organic PCM, i.e., pork fat, were experimentally determined with the differential scanning calorimetry (DSC) method and embedded in the numerical model. It was found that melting of the crystal phase of pork fat PCM starts at 8.3 °C and ends at 45.2 °C with a latent heat of melting being 45.4 Jg−1. The numerical model was successfully validated with experimental data. The mean absolute percentage deviation of the operating temperature numerical forecast compared with the experiment was from about 2.9% to 4.9%, depending on the considered domain. The findings of this investigation can be used to improve the existing designs of PVT-PCM collectors. Furthermore, a validated numerical model can be used to investigate the impact of various operating parameters on the system performance to enhance the resilience aspect in the early development phase of novel designs.

Satellite-estimated air-sea CO2 fluxes in the Bohai Sea, Yellow Sea, and East China Sea: Patterns and variations during 2003–2019

The Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) together form one of the largest marginal sea systems in the world, including enclosed and semi-enclosed ocean margins and a wide continental shelf influenced by the Changjiang River and the strong western boundary current (Kuroshio). Based on in situ seawater pCO2 data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO2 was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML-ECS). MeSAA-ML-ECS introduced semi-analytical parameters, including the temperature-dependent seawater pCO2 (pCO2,therm) and upwelling index (UISST), to characterise the combined effect of atmospheric CO2 forcing, thermodynamic effects, and multiple mixing processes on seawater pCO2. The best-selected machine learning algorithm is XGBoost. The satellite-derived pCO2 achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE = 20 μatm) and mean absolute percentage deviation (APD = 4.12 %) for independent in situ validation dataset. During 2003–2019, the annual average CO2 sinks in the BS, YS, ECS, and entire study area were 0.16 ± 0.26, 3.85 ± 0.68, 14.80 ± 3.09, and 18.81 ± 3.81 Tg C/yr, respectively. Under continuously increasing atmospheric CO2 concentration, the BS changed from a weak source to a weak sink, the YS experienced interannual fluctuations but did not show significant trend, while the ECS acted as a strong sink with CO2 absorption increased from ∼10 Tg C in 2003 to ∼19 Tg C in 2019. In total, CO2 uptake in the entire study area increased by 85 % in 17 years. For the first time, we present the most refined variation in the satellite-derived pCO2 and air-sea CO2 flux dataset. These complete ocean carbon sink statistics and new insights will benefit further research on carbon fixation and its potential capacity.

Forecasting Model Accuracy Assessment in a Bottled Water Supply Chain

This study presents an accuracy assessment of various classical time series forecasting methods to determine the most accurate forecasting method for predicting demand of the 50cl product of a bottled water supply chain. The classical time series forecasting methods compared are the moving average, weighted moving average, exponential smoothing, adjusted exponential smoothing, linear trend line, Holt’s model, and Winter’s model. The Mean Absolute Percent Deviation (MAPD) value was determined for the various forecasting methods to find the forecasting method with the least MAPD and hence the highest forecasting accuracy. The tracking signal measure was also used to determine the forecasting models which were biased. The results showed that though the weighted moving average method began to underforecast demand in the final period, it had the lowest MAPD value of 2.25%, making it the forecasting method with the highest accuracy for predicting the 50cl bottled water demand. On the other hand, though the exponential smoothing forecasting method had the highest MAPD value of 3.43% and least accuracy for predicting the bottled water demand, its forecasts were consistently within the tracking signal control limits. This study will aid supply chain analysts in implementing accuracy assessment of classical time series forecasting models.

Identification of Tension Force in Cable Structures Using Vibration-Based and Impedance-Based Methods in Parallel

For cable structures, the tension force is one of the main factors showing the structure’s health. If the tension force falls below a safe level during construction or operation, it can lead to partial or complete the structural failure, posing a risk to the people’s safety. In this study, a parallel structural health monitoring approach of the vibration-based and impedance-based methods is proposed to identify the tension force in cable structures. Firstly, a cable structure including the anchorage is simulated using a finite element model to obtain the vibration and impedance responses. The numerical results are verified with the experimental ones of the previous studies. Then, the parallel approach combining the above two methods is presented to determine the tension force. For the vibration-based method, the tension force is estimated by the natural frequencies. For the impedance-based method, the tension force is estimated by the mean absolute percentage deviation (MAPD) index and the artificial neural network (ANN). Finally, the tension force estimation results are compared and assessed. By using the parallel approach, the reliability and accuracy of the tension force identification results are guaranteed.

A precise estimation for vibrational energies of diatomic molecules using the improved Rosen–Morse potential

In the context of the generalized fractional derivative, novel solutions to the D-dimensional Schrödinger equation are investigated via the improved Rosen-Morse potential (IRMP). By applying the Pekeris-type approximation to the centrifugal term, the generalized fractional Nikiforov-Uvarov method has been used to derive the analytical formulations of the energy eigenvalues and wave functions in terms of the fractional parameters in D-dimensions. The resulting solutions are employed for a variety of diatomic molecules (DMs), which have numerous uses in many fields of physics. With the use of molecular parameters, the IRMP is utilized to reproduce potential energy curves for numerous DMs. The pure vibrational energy spectra for several DMs are determined using both the fractional and the ordinary forms to demonstrate the effectiveness of the method utilized in this work. As compared to earlier investigations, it has been found that our estimated vibrational energies correspond with the observed Rydberg-Klein-Rees (RKR) data much more closely. Moreover, it is observed that the vibrational energy spectra of different DMs computed in the existence of fractional parameters are superior to those computed in the ordinary case for fitting the observed RKR data. Thus, it may be inferred that fractional order significantly affects the vibrational energy levels of DMs. Both the mean absolute percentage deviation (MAPD) and average absolute deviation (AAD) are evaluated as the goodness of fit indicators. According to the estimated AAD and MAPD outcomes, the IRMP is an appropriate model for simulating the RKR data for all of the DMs under investigation.

Electromechanical Impedance Instrumented Wearable Piezoelectric Ring-Type Sensor for Bolted Connection Monitoring

Due to the loss of preload, structural failure caused by bolt loosening is common in engineering structures. Effective monitoring of bolted connections is of great importance in providing early warning to the structural maintenance department. In this study, a novel and reusable electromechanical impedance (EMI) instrumented wearable piezoelectric ring (WPR) was proposed to detect bolted connection loosening. In the experimental investigation, the EMI spectra of three identical WPRs during the bolt-loosening process were acquired and analyzed. It was concluded that bolt looseness changes can be qualitatively and quantitatively characterized by conductance spectra. Specifically, the resonance frequency and amplitude at certain frequencies were reduced correspondingly as bolts loosen. This was further validated by finite element model simulation results. Moreover, two statistical metrics including mean absolute percentage deviation (MAPD) and root mean square deviation (RMSD) were adopted to evaluate different degrees of bolted connections quantitatively. Both metrics showed an increasing tendency during the bolt loosening process in three different frequency bands. It was found that both metrics demonstrated a significant linear correlation with bolt torque variations. Both of them serve as reliable indicators to detect loose bolts in the bolted connection. The present work verifies the feasibility and applicability of using EMI-instrumented WPR to monitor bolted connections.

Revised spectral optimization approach to remove surface-reflected radiance for the estimation of remote-sensing reflectance from the above-water method.

The effective sea-surface skylight reflectance (ρ) is an important parameter for removing the contribution of surface-reflected radiance when measuring water-leaving radiance (Lw) using the above-water approach (AWA). Radiative simulations and field measurements show that ρ varies spectrally. To improve the determination of Lw (and then remote sensing reflectance, Rrs) from the AWA, we further developed a wavelength-dependent model for ρ to remove surface-reflected radiance, which is applied with a spectral optimization approach for the determination of Rrs. Excellent agreement was achieved between the AWA-derived and skylight-blocked approach (SBA)-obtained Rrs (coefficient of determination > 0.92, mean absolute percentage deviation < ∼ 11% for Rrs > 0.0005 sr-1), even during high wave conditions. We found that the optimization approach with the new ρ model worked very well for a wide range of water types and observation geometries. For developing remote sensing algorithms and evaluating satellite products, it would be beneficial to apply this approach to current and historical above-water in situ measurements of Rrs to improve the quality of these data. In addition, this approach could also increase the number of useable spectra where previously rendered unusable when processed with a traditional scheme.

Generalized Support Vector Regression and Symmetry Functional Regression Approaches to Model the High-Dimensional Data

The analysis of the high-dimensional dataset when the number of explanatory variables is greater than the observations using classical regression approaches is not applicable and the results may be misleading. In this research, we proposed to analyze such data by introducing modern and up-to-date techniques such as support vector regression, symmetry functional regression, ridge, and lasso regression methods. In this study, we developed the support vector regression approach called generalized support vector regression to provide more efficient shrinkage estimation and variable selection in high-dimensional datasets. The generalized support vector regression can improve the performance of the support vector regression by employing an accurate algorithm for obtaining the optimum value of the penalty parameter using a cross-validation score, which is an asymptotically unbiased feasible estimator of the risk function. In this regard, using the proposed methods to analyze two real high-dimensional datasets (yeast gene data and riboflavin data) and a simulated dataset, the most efficient model is determined based on three criteria (correlation squared, mean squared error, and mean absolute error percentage deviation) according to the type of datasets. On the basis of the above criteria, the efficiency of the proposed estimators is evaluated.

Genetic algorithm optimization for parametrization, digital twinning, and now-casting of unknown small- and medium-scale PV systems based only on on-site measured data

Accurately predicting and balancing energy generation and consumption are crucial for grid operators and asset managers in a market where renewable energy is increasing. To speed up the process, these predictions should ideally be performed based only on on-site measured data and data available within the monitoring platforms, data which are scarce for small- and medium-scale PV systems. In this study, we propose an algorithm that can now-cast the power output of a photovoltaic (PV) system with high accuracy. Additionally, it offers physical information related to the configuration of such a PV system. We adapted a genetic algorithm-based optimization approach to parametrize a digital twin of unknown PV systems, using only on-site measured PV power and irradiance in the plane of array. We compared several training datasets under various sky conditions. A mean deviation of −1.14 W/kWp and a mean absolute percentage deviation of 1.81% were obtained when we analyzed the accuracy of the PV power now-casting for the year 2020 of the 16 unknown PV systems used for this analysis. This level of accuracy is significant for ensuring the efficient now-casting and operation of PV assets.

Monitoring of soil water content using spherical smart aggregates based on electromechanical impedance (EMI) technique

Real-time monitoring of soil water content is of great significance to prevent many engineering accidents, such as landslide, water seepage of foundation dam, and reduction of foundation bearing capacity. Electromechanical impedance (EMI) technique based smart aggregates (SAs) have showed excellent monitoring capability in various engineering applications. However, there are limited reports on the application of EMI based SAs in the monitoring of the soil water content. In this paper, the EMI based spherical SAs (SSAs) were investigated in the monitoring of soil water content. Firstly, the SSAs were designed and fabricated using the monolithic concrete encapsulation method, and their stable performance in air were evaluated, and confirmed by testing and analyzing the impedance spectrum. Then, the fabricated SSAs were immersed in the water environment for 28 d to ensure the stability under the working conditions, such as the soil with high water content and the hydration process of early-age concrete. Secondly, the monitoring experiments of soil water content were carried out based on the SSAs and the traditional SAs using the EMI technique. The measured impedance signatures under different water contents in soil were quantified by three types of statistical indexes, including root mean square deviation, mean absolute percentage deviation, and correlation coefficient deviation. The experimental results show that compared with the traditional SAs, the SSAs are more sensitive and stable to monitor the soil water content. Finally, the effect of temperature on the performance of SSA based on the EMI technique were conducted experimentally. The results demonstrate that temperature has influence on the monitoring results of the SSAs based on the EMI technique.