Mean Bias Error Research Articles

Handling uncertainty issue in software defect prediction utilizing a hybrid of ANFIS and turbulent flow of water optimization algorithm

During the development cycle of software projects, numerous defects and challenges have been identified, leading to prolonged project durations and escalated costs. As a result, both product delivery and defect tracking have become increasingly complex, expensive, and time-consuming. Recognizing the challenge of identifying every software defect, it is crucial to foresee potential consequences and strive for the production of high-quality products. The goal of software defect prediction (SDP) is to identify problematic locations within software code. This study presents the first experimental investigation utilizing the turbulent flow of water optimization (TFWO) in conjunction with the adaptive neuro-fuzzy inference system (ANFIS) to enhance SDP. The TFWO_ANFIS model is designed to address the uncertainties present in software features and predict defects with feasible accuracy. Data are divided randomly at the beginning of the model into training and testing sets to avoid the local optima and over-fitting issues. By applying the TFWO approach, it adjusts the ANFIS parameters during the SDP process. The proposed model, TFWO_ANFIS, outperforms other optimization algorithms commonly used in SDP, such as particle swarm optimization (PSO), gray wolf optimization (GWO), differential evolution (DE), ant colony optimization (ACO), standard ANFIS, and genetic algorithm (GA). This superiority is demonstrated through various evaluation metrics for four datasets, including standard deviation (SD) scores (0.3307, 0.2885, 0.3205, and 0.2929), mean square error (MSE) scores (0.1091, 0.0770, 0.1026, and 0.0850), root-mean-square error (RMSE) scores (0.3303, 0.2776, 0.3203, and 0.2926), mean bias error (MBE) scores (0.1281, 0.0860, 0.0931, and 0.2310), and accuracy scores (87.3%, 90.2%, 85.8%, and 89.2%), respectively, for the datasets KC2, PC3, KC1, and PC4. These datasets with different instances and features are obtained from an open platform called OPENML. Additionally, multiple evaluation metrics such as precision, sensitivity, confusion matrices, and specificity are employed to assess the model’s performance.

Ametropia detection using a novel, compact wavefront autorefractor.

Despite the well-known reproducibility issues of subjective refraction, most studies evaluating autorefractors compared differences between the device and subjective refraction. This work evaluated the performance of a novel handheld Hartmann-Shack-based autorefractor using an alternative protocol, which considered the inherent variability of subjective refraction. Participants underwent an initial measurement with a desktop autorefractor, two subjective refractions (SR1 and SR2) and a final measurement with the QuickSee Free (QSFree) portable autorefractor. Autorefractor performance was evaluated by comparing the differences between the QSFree and each of the subjective refractions with the difference between the subjective refractions (SR1 vs. SR2) using Bland-Altman analysis and percentage of agreement. A total of 75 subjects (53 ± 14 years) were enrolled in the study. The average difference in the absolute spherical equivalent (M) between the QSFree and the SR1 and SR2 was ±0.24 and ±0.02 D, respectively, that is, very similar or smaller than the SR1 versus SR2 difference (±0.26 D). Average differences in astigmatic components were found to be negligible. The results demonstrate that differences between QSFree and both subjective refractions in J0 and J45 were within ±0.50 D for at least 96% of the measurements. The limits of agreement (LOAs) of the differences between QSFree and SR1, as well as QSFree and SR2, were higher than those observed between SR1 and SR2 for M, J0 and J45 . A protocol was designed and validated for the evaluation of a refractive device to account for the variability of subjective refraction. This protocol was used to evaluate a novel portable autorefractor and observed a smaller difference between the device and subjective refractions than the difference between the two subjective refraction measurements in terms of mean bias error, although the standard deviation was higher.

Prediction of Circumsolar Irradiance and Its Impact on CSP Systems under Clear Skies

In this work, a model to estimate circumsolar normal irradiance (CSNI) for several half-opening angles under clear skies was developed. This approach used a look-up table to determine the model parameters and estimate CSNI for half-opening angles between 0.5° and 5°. To develop and validate the proposed model, data from five locations worldwide were used. It was found that the proposed model performs better at the locations under study than the models available in the literature, with relative mean bias error ranging from −13.94% to 0.70%. The impact of CSNI for these different half-opening angles on concentrating solar power (CSP) systems was also studied. It was found that neglecting CSNI could lead to up to a 7% difference between the direct normal irradiance (DNI) measured by a field pyrheliometer and the DNI that is captured by CSP systems. Additionally, a case study for parabolic trough concentrators was performed as a way to estimate the impact of higher circumsolar ratios (CSR) on the decrease of the intercept factor for these systems. It was also concluded that if parabolic trough designers aim to reduce the impact of CSNI variation on the intercept factor, then parabolic troughs with higher rim angles are preferred.

Evaluation of three radiation schemes of the WRF-Solar model for global surface solar radiation forecast: A case study in Egypt

This study aims to assess the performance of three different radiation schemes in Weather Research and Forecasting (WRF) mesoscale model against ECMWF fifth generation (ERA5) Reanalysis data over Egypt. Dudhia, rrtmg, and Goddard schemes were used to simulate Global Horizontal Irradiance (SR) during 2017. Sixteen stations were selected to represent the different climate conditions in Egypt. The observed SR data is only available at five stations and used to evaluate the accuracy of ERA5 and WRF output. SR data at the other eleven stations were extracted from ERA5 to evaluate the corresponding values from WRF simulation. Coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), monthly standard deviation (STDV) and mean absolute percentage error (MAPE%) are the implemented evaluation statistical parameters in the study. The results showed that ERA5 and WRF Dudhia scheme have reliable results as compared to the observations at the five stations. Also, the WRF Dudhia scheme simulates the SR at the other eleven stations better than rrtmg, and Goddard schemes as compared to ERA5 dataset. Where, the three WRF radiation schemes overestimate the SR along the eleven stations and underestimate at Alexandria station by −0.15 KWh/m2, which may due to the weather conditions over these sites. But the Dudhia scheme over the eleven stations has the lowest MBE (<0.34 KWh/m2), RMSE (<0.58 KWh/m2), and MAPE (<−2%), with R2 more than 0.985 at most stations.

Predicting monthly near-surface soil temperature from air temperature and the leaf area index

Soil temperature at the land surface (Tsoil) is a key variable for modeling processes that occur belowground. Although Tsoil is correlated to the near-surface air temperature (Tair), these values are often not equal. Evidence confirms that Tsoil – Tair (ΔT) varies by ecosystem in both positive and negative directions. The goal of this study was to calibrate a semi-empirical model to predict Tsoil from Tair and its interaction with the leaf area index (LAI) at a monthly timescale. The latter variable is introduced to quantify the reduction in solar radiation reaching the land surface using Beer-Lambert's Law with an extinction coefficient k = 0.55. Bootstrap distributions for the three unknown parameters in the model were optimized using monthly Tair and Tsoil measurements from 169 eddy covariance towers in the FLUXNET2015 dataset accompanied by LAI observations at 0.5-km2 resolution from the MODIS satellite. The optimized model had a mean bias error (MBE) of -0.08 °C and a root mean square error (RMSE) of 2.26 °C. When Tsoil was assumed equal to Tair (ΔT = 0), these metrics worsened to -1.51 °C and 4.09 °C, respectively. Data from the US National Ecological Network (MBE/RMSE = -0.51°/2.81 °C) and published global predictions of ΔT obtained with a machine learning algorithm (MBE/RMSE = 0.73°/1.76 °C) validated the optimized model at the global scale. The data confirmed that ΔT tends to be positive when Tair 〈 0 °C, and transitions to negative values when Tair 〉 0 °C and LAI is greater than 1.89. In the proposed model, LAI values close to 1.89 indicate a 37 % reduction in solar radiation (e − 1 in Beer-Lambert's Law). Overall, this work confirms that Tsoil ≠ Tair in most ecosystems and that ΔT is predominantly explained by LAI in non-freezing conditions. Re-calibration is recommended before applying the model in barren ecosystems, or at spatiotemporal scales different from those in this research.

Comparison of the sensitivities and accuracies of optoelectronic transducers for solar irradiance measurement

This paper compares the sensitivities and accuracies of four different optoelectronic transducers in measuring solar irradiance for the fabrication of portable and low-cost pyranometers. Trans-impedance conditioning circuits were designed for two phototransistors (BP103 and SFH3310) and two photodiodes (BPW21 and BPW34). The Arduino Mega 2560 was used as an interface between the analogue signal produced by the conditioning circuits and the digital output ports. The transducers with a standard pyranometer RSRA_05V were arranged on a vero board and exposed to the sun. Statistical analysis of the experimental results produced the least root mean square error value of 6.58794 Wm−2 for phototransistor BP103 during the dry season followed by 13.35216 Wm−2 for phototransistor SFH3310 during the dry season. The correlation coefficients of BPW21, BPW34, BP103, and SFH3310 with the standard pyranometer are 0.9489, 0.9916, 0.9976, and 0.9905, respectively. The experimental results obtained from the phototransistors BP103 and SFH3310 strongly correlated with those of the standard pyranometer coupled with lower root mean square error and mean bias error values than those of the photodiodes. Thus, the phototransistors BP103 and SFH3310 are more accurate and effective for measuring solar irradiance. This study contributes to the development of low-cost and accurate solar radiation meters.

Point Pedotransfer Functions (PPTF) for Estimating Soil Water Retention Curve by Multiple Linear Regression Methods

The aim of this study was to derive and find the best Point Pedotransfer Functions (PPTF) for estimating SWRC for Iraqi soils using the multiple regression methods. Soil samples were collected from 30 different sites of Iraqi soil at two depths: 0-0.3 m and 0.3-0.6 m, in order to obtain the required database for the derivation of PTF, which includes the soil particle size distribution, carbonate minerals, bulk density, particles density, organic matter, saturated hydraulic conductivity, capillary height and the boundaries of available water. Measured characteristics were considered as explanatory variables (EV). Volumetric water content was measured at suctions 0, -5, -10, -33, -500, -100, -500, -1000, and -1500 kPa and considered as response variables (RV). PPTF was derived using SAS statistical software based on multiple linear regression. Three methods for fitting the PPTF to EV were Maximum R2 Improvement (MR), Backward Elimination (BW) and Forward selection (FW). The prediction accuracy was evaluated through Mean Bias Error (MBE), Mean Absolute Error (MAE), root mean squared error (RMSE) and Coefficient Of Determination (R2). The results showed that all EVs were included in the PPFT derivation by MR method, while in the BW and FW methods the significant level of entery (SLENTRY) of EV was 0.1. It was also found that variables w1, capillary height, available water, bulk density, and porosity were included in the derivation of most of the models using the BW and FW method. Besides no statistically significant differences were found among the values of MBE, MAE, RMSE, and R2 of the PPTF although the number of selected EV are different.

Estimation of hourly solar irradiation on tilted surfaces

Tilted photovoltaic panel (PVP) are extensively utilized in solar power installations. To model and design them, you must understand the solar irradiation on a tilted surface over time scales of an hour, day, week, month, or other period. However, most meteorological stations only record global horizontal irradiance (GHI). In this research, a method is proposed for translating hourly worldwide horizontal irradiation from the NASA POWER database into hourly global sun irradiation on a tilted surface utilizing regression analysis methods and numerical modeling methods based on an isotropic model of solar irradiation. In addition, this work proposes a method for establishing the regression reliance of the diffuse transmittance index on the clearness index for geographic areas where this relationship has not yet been empirically proven. To assess effectiveness, the results of the proposed technique and other authors' methods are compared with monthly average NASA POWER climatological data using evaluation methods such as mean bias error (MBE), mean absolute bias error (MABE), and root-mean-square error (RMSE). The study's findings can be used to construct, optimize, or anticipate the functioning of solar power facilities at any angle of tilt and in any geographic area.

Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI.

Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice. To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water-fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined. We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques (r = 0.70-0.86, p < 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength. We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water-fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.

Thermochemical valorization of greenhouse cucumber, tomato and pepper as biofuel

Agricultural residues adequate managing contributes to reduce CO2 emissions and easy the circular economy. In this paper, cucumber, tomato and pepper greenhouse crop residues were characterized both chemically and energetically to potential of waste as a biofuel. When compared to other herbaceous and woody biomass, the data show higher moisture, ash and chlorine contents. The gross calorific values of these residues are in the same range as the herbaceous biomass (14.92 MJ/kg), with net values of 13.71, 14.58 and 15.73 MJ/kg for cucumber, tomato and pepper, respectively. The study also included other not common elements, finding the highest values for calcium (13–25 mg/kg) and potassium (23–30 mg/kg). To date, empirical correlations have been defined to predict the CV as a function of total C content (expressed as % in dry weight) for these residues individually. The empirical correlations obtained had maximum absolute errors (MAE) of 0.11, 0.32 and 0.58 % for cucumber, tomato and pepper whereas the marginal mean bias errors (MBE) were 0.016, 0.002 and 0.004 %, respectively, which confirms the value of using this method to determinate the gross calorific value of these residues. The correlations shown provide a useful tool for developing energy production processes based on crop residues, that would appear to be an interesting source of renewable energy to produce heat.

Mathematical models for predicting the higher heating value and ultimate analysis of biomass

The higher heating value (HHV) and elemental composition are important properties of biomass and they are necessary to perform computer simulations of the biomass thermal conversion processes. Both properties require equipment to measure that can be expensive, while the proximate analysis required equipment that is typically found in a laboratory. This work presents a general correlation, based on proximate analysis of biomass, to calculate elemental composition and two new correlations, based on proximate and ultimate analyses of biomass and char used for prediction of the higher heating value. The correlations based on ultimate analysis and proximate analysis show an average absolute error (AAE) of 5.59% and 6.81%, respectively, an average bias error (ABE) of 0.17% and 1.52%, a mean bias error (MBE) of − 0.09 and − 0.06, and root mean square error (RMSE) of 1.52 and 1.98 concerning the value of HHV. To calculate the elemental composition from proximate analysis, the correlations were proposed of C, H and O. The AAE of these correlations were 3.86%, 7.49%, and 5.68%, the ABE were − 1.10%, − 0.22%, − 2.23%, the MBE were 0.58, 0.06, and 1.14 while the RMSE were 2.65, 0.6, 3.45 with respect to measured values of C, H, O, respectively. The AAE values of the correlation proposed in this study using ultimate analysis are lower than those found for the other studied correlations.

A New CCN Number Concentration Prediction Method Based on Multiple Linear Regression and Non‐Negative Matrix Factorization: 1. Development, Validation, and Comparison Using the Measurement Data Over the Korean Peninsula

AbstractTo reduce uncertainty in climate change prediction, a large amount of cloud condensation nuclei number concentration (NCCN) data must be obtained. This study aimed to develop a new NCCN prediction method, hereafter MLRNMF method, that applies multiple linear regression (MLR) and non‐negative matrix factorization (NMF) to aerosol number size distribution data measured in Seoul and over the Yellow Sea. To verify the reliability of the MLRNMF method, a data set separated from the training data set was used, and sufficient time differences of several years were given between the two data sets to make them as independent as possible from each other. The predicted NCCN was in acceptable agreement with the measured NCCN. The coefficient of determination (R2) values between measured and predicted NCCN for the Yellow Sea and Seoul were 0.81 and 0.71, respectively. Mean fractional bias (MFB) and mean fractional error (MFE) also met the performance goals (&lt;±30% and &lt;+50%, respectively). The MLRNMF method had similar accuracy to the backward integration method but showed strength in robustness and the capability to take into account external mixing. The NCCN prediction methods trained using data of a specific season tended to underestimate/overestimate somewhat, but MFB and MFE for all four seasons met the performance goals except for MFB in using June–August (JJA) data, implying that the MLRNMF method trained using only the data of a specific season can predict NCCN for all four seasons to some extent. It is expected that abundant NCCN data can be obtained through the MLRNMF method in future studies.

Performance of potential evapotranspiration models across different climatic stations in New South Wales, Australia

Study regionNew South Wales, southeast Australia Study focusEstimating potential evapotranspiration (ETp) rates, detecting its temporal trends and analysing its interannual oscillation are critical for long-term assessment of water availability and regional drought. This study aimed to evaluate the comprehensive performance of 12 simplified models in characterising ETp against the benchmark Penman model across different climate sites in southeast Australia. This study used Taylor skill score (S), normalised root mean square error (nRMSE) and relative mean bias error (rMBE) to estimate models’ capability in estimating ETp rates. Then, this study adopted Mann-Kendall test and continuous wavelet transform (CWT) to test temporal trends and periodicity of ETp estimated by all models. New hydrology insights for the regionJensen-Haise model was capable to produce fair (nRMSE ≤ 30%) estimates of daily ETp across all stations. Models except Mak1 were generally able to produce reasonable estimates of ETp at larger time scale. In addition, we found that the 12 alternative ETp models generally agreed with the Penman model on the primary (9.6–12.4-year) and quasi (2.6–3.9-year) periods of ETp, but they did not show matchable ability in detecting ETp temporal trends. The comprehensive investigation on models’ performance will shed light on models’ selection in estimation of drought and hydrological cycle.

Daily satellite-based sunshine duration estimates over Brazil: validation and intercomparison

Abstract. The broad geographical coverage and high temporal and spatial resolution of geostationary satellite data provide an excellent opportunity to collect information on variables whose spatial distribution and temporal variability are not adequately represented by in situ networks. This study focuses on assessing the effectiveness of two geostationary satellite-based sunshine duration (SDU) datasets over Brazil, given the relevance of SDU to various fields, such as agriculture and the energy sector, to ensure reliable SDU data over the country. The analyzed datasets are the operational products provided by the Satellite Application Facility on Climate Monitoring (CMSAF) that uses data achieved with the Meteorological Satellite (Meteosat) series and by the Satellite and Meteorological Sensors Division of the National Institute for Space Research (DISSM–INPE) that employs Geostationary Operational Environmental Satellite (GOES) data. The analyzed period ranges from September 2013 to December 2017. The mean bias error (MBE), mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and scatterplots between satellite products and in situ daily SDU measurements provided by the National Institute of Meteorology (INMET) were used to access the performance of the products. They were calculated on a monthly basis and grouped into climate regions. The statistical parameters exhibited a uniform spatial distribution, indicating homogeneity within a given region. Except for the tropical northeast oriental (TNO) region, there were no significant seasonal dependencies observed. The MBE values for both satellite products were generally low across most regions in Brazil, mainly between 0 and 1 h. The correlation coefficient (r) results indicated a strong agreement between the estimated values and the observed data, with an overall r value exceeding 0.8. Nevertheless, there were notable discrepancies in specific areas. The CMSAF product showed a tendency to overestimate observations in the TNO region, with the MBE consistently exceeding 1 h for all months, while the DISSM product exhibited a negative gradient of the MBE values in the west–east direction in the northern portion of Brazil. The scatterplots for the TNO region revealed that the underestimation pattern observed in the DISSM product was influenced by the sky condition, with more accurate estimations observed under cloudy skies. Additional analysis suggested that the biases observed might be attributed to the misrepresentation of clear-sky reflectance. In the case of the CMSAF product, the overestimation tendency observed in the TNO region appeared to be a result of systematic underestimation of the effective cloud albedo. The findings indicated that both satellite-based SDU products generally exhibited good agreement with the ground observations across Brazil, although their performance varied across different regions and seasons. The analyzed operational satellite products present a reliable source of data to several applications, which is an asset due to its high spatial resolution and low time latency.

Evaluation of Selected Empirical Models for Asphalt Pavement Temperature Prediction in a Tropical Climate: The Case of Ghana

Asphalt pavement temperature has several applications, including pavement structural design and evaluation, asphalt mixture design, asphalt binder grade determination and material aging characterization. However, available asphalt pavement temperature prediction models were mostly developed for temperate climatic conditions. Before such models are adopted for use in a tropical climate to facilitate advanced pavement engineering, their applicability must be verified. This study evaluated five empirical asphalt pavement temperature prediction models: the Lukanen (BELLS 3), Park, Diefenderfer, and Taamneh models, all developed in the United States, and the Asefzadeh model, formulated in Canada, to ascertain their prediction accuracy in a tropical climate, using the West African country Ghana as a case study. The results of such a model evaluation study will justify the adoption of existing models for local application or the development of new ones suitable for tropical climates. In this study, in situ asphalt pavement temperature data were measured at two sites for eight months: Kumasi and Tamale in the Forest and Savannah climatic zones, respectively. The measured pavement temperature data were compared with predicted pavement temperatures using the two independent-samples t-test, the coefficient of determination, the line of equality, and three error statistics (mean bias error, mean percentage error and root mean square error). It was found that the Park model provided a more accurate pavement temperature prediction in both climatic zones. The other models either over-predicted or under-predicted asphalt pavement temperature with significant error margins. However, there is a need to improve the prediction accuracy of the Park model as considerable over-prediction occurred at a temperature of at least 47 °C or a local model developed.

Variable refrigerant flow heat pump model with estimated parameters and emulated controller based on manufacturer data

A new physics-based and modular variable refrigerant flow (VRF) heat pump model aimed toward multi-year simulations is presented. The model allows the simulation of any number of indoor units (IU), outdoor units (OU) and compressors. A parameter-estimation procedure and a control strategy both using available manufacturer data is proposed. The model is validated against data collected from a VRF system that services the first floor of the former ASHRAE Headquarters Building in Atlanta (USA), comprised of 22 IU, 2 OU, and 8 compressors. Results show that the model accurately predicts the total energy consumption over a two-month cooling period, with a relative error, normalized mean bias error, and coefficient of variation of the root mean square error of 1%, 1.6%, and 16.7%, respectively.

Investigation of Adsorption Kinetics on the Surface of a Copper-Containing Silicon–Carbon Gas Sensor: Gas Identification

The low selectivity of materials to gases of a similar nature may limit their use as sensors. Knowledge of the adsorption kinetic characteristics of each gas on the surface of the material may enable the ability to identify them. In this work, copper-containing silicon–carbon films were formed using electrochemical deposition on the Al2O3 substrate with interdigitated Cr/Cu/Cr electrodes. These films showed good adsorption characteristics with several different gases. The adsorption kinetics of nitrogen dioxide, sulfur dioxide, and carbon monoxide on the film surface were investigated by the change in the resistivity of the material. Pseudo-first-order and pseudo-second-order kinetics, Elovich, Ritchie, and Webber intraparticle diffusion models were applied. It was found that the largest approximation factor and the lowest Root-Mean-Square Error and Mean Bias Error for all three gases were for the Elovich model. The advantages of silicon–carbon copper-containing films for gas sensor applications were shown. An algorithm for gas recognition was proposed based on the dependence of the change in the resistivity of the material under stepwise gas exposure. It was found that parameters such as the values of the extrema of the first and second derivatives of the R vs. t dependence during adsorption and the slope of R vs. t dependence in the Elovich coordinates are responsible for gas identification among several one-nature gases.

Junction temperature of CMOS electronics cooled by a Stirling cryocooler

This case study addresses the practical challenges associated with operating cryo-CMOS electronics. Previous research has been largely confined to impractical liquid cryogen cooling and the few under-reported examples of implementing electric cryocoolers. This study further clarifies the operation of cryo-CMOS devices cooled by electric cryocoolers. It develops and experimentally validates an analytical relation between the junction temperature and the parameters of a cryogenic system. To validate this relationship, we placed a printed circuit board in a thermal chamber, cooled it to 173 K using a Stirling cryocooler, and varied input clock frequencies. The measured values of the junction temperature were compared with calculations, which showed good prediction accuracy with a coefficient of variance of ±2%, a mean biased error of −2%, and a determination coefficient of 0.92. In addition, the measurements provided evidence for a multi-fold increase in the clock frequency of cryo-CMOS electronics. The results of this study help estimate accurately the steady-state junction temperature of cryo-CMOS devices in a wide range of cryogenic temperatures for different types of cryocoolers.

Validating the credibility of solar simulation tools using a real-world case study

Solar energy is being recognized around the world for generating clean power using photovoltaic (PV) panels. Inadequate planning and sizing of PV panel systems leads to increased costs, emphasising the significance of reviewing solar energy simulationtools for accuracy and credibility. Even with the current literature, it could be challenging to select the software tools that are appropriate for the research. This paper identified, reviewed and validated four system-based (System Advisor Model (SAM), PVsyst, PV*Sol and Solarius PV) and three online software tools (Helioscope, PV-Watts and PV-GIS). It provides necessary information on the software tools which compiled in a features matrix under seven categories. The validation process involves using real-world data from an 84.5KW solar plant installed on a building in Nagpur, India, to assess the accuracy and performance of the software tools. The statistical metrics used for validation are Absolute Deviation (AD), Root Mean Square Error (RMSE) accompanied by Mean Bias Error (MBE) and Nash-Sutcliffe Efficiency (NSE) for a comprehensive understanding of the accuracy and reliability. The findings show a discrepancy in the simulated output data and the recorded data of the tools with PV*Sol and Solarius PV showcasing the least error. In conclusion, the findings of this research provide valuable insights and guidance for users seeking to make informed decisions in selecting the most appropriate Solar PV simulation tool tailored to their specific needs and requirements.

A two-stage optimization method for improving the load flexibility of existing district energy systems

Decarbonization of district energy systems is essential for China to meet its carbon neutrality goal by 2060. Most existing district energy systems are missing historical load data and have incomplete information, resulting in a lack of data support for the low-carbon transition. Moreover, demand-side load flexibility has not been fully exploited in the planning stage. In this paper, we developed a two-stage computational approach to optimize district loads. We first established an integrated Python framework, incorporating the TEASER simulation tool and AixLib model library, to efficiently calculate baseline loads through the bottom-up modeling and simulation of district buildings. Then, a price-based integrated demand response strategy was introduced. A mixed-integer nonlinear programming model was formulated to optimize the energy pricing strategy with the objective of minimum load fluctuations. Finally, a case study was employed to illustrate the feasibility of the calculation method, showing a normalized mean bias error of 7.17%. The results further demonstrated that the strategy could reduce the peak electric and heat loads by 3.55% and 9.57%, and increase load rates by 3.85% and 9.48%, respectively. The strategy could assist district energy service providers to optimize equipment capacity configuration and enhance the low-carbon planning potential of energy systems from the demand-side.