Accurate Temperature Estimation Research Articles

A dynamic thermal model for a photovoltaic module under varying atmospheric conditions

Operating temperature strongly affects the performance of the photovoltaic module. Thus, the accurate estimation of the module temperature plays an important role in the assessment of photovoltaic system generation. In this article, it was shown that the existing empirical models are characterized by a limited accuracy in determining the module temperature under varying atmospheric conditions, and the estimation error exceeded 25 °C. This feature of empirical models results from the neglect of the thermal inertia of the module. To solve this problem, a dynamic thermal model for the photovoltaic module was proposed. The proposed model is based on the Finite Difference Method and only uses data on the ambient temperature, solar irradiation, and wind speed. The model coefficients were optimized using the Particle Swarm Optimization method. Developed model was benchmarked against field measurements at the Silesian University of Technology, Poland. The performance of the proposed model was also compared with the performance of the dynamic thermal model proposed by Barry et al., using measurements from systems located in the Allgäu region, Germany. The results demonstrated the effectiveness of the proposed dynamic thermal model for the correct calculation of the photovoltaic module temperature under varying weather conditions. The temperature estimation error did not exceed 9 °C.

Modeling and prediction of lithium-ion battery thermal runaway via multiphysics-informed neural network

In this study, a multiphysics-informed neural network (MPINN) is proposed for the estimation and prediction of thermal runaway (TR) in lithium-ion batteries (LIBs). MPINNs are encoded with the governing laws of physics, including the energy balance equation and Arrhenius law, ensuring accurate estimation of time and space-dependent temperature and dimensionless concentration in comparison to a purely data-driven approach. Specifically, the network is trained using data from a high-fidelity model of an LIB, which describes TR by addressing several coupled partial differential equations. Quantitative analysis reveals that the mean absolute error (MAE) and root mean squared error (RMSE) of the MPINN for TR estimation are less than an artificial neural network (ANN) by 0.71 and 1.57, respectively, when using fully labeled data for training. It outperforms the ANN in terms of MAE and RMSE by 90.56 and 118.64, when only a small portion of labeled data (semi-supervision) are used for TR prediction. Importantly, it predicts TR without any labeled data when the decomposition of reactive species is modeled in the positive electrode. The MPINN exhibits promising results in surrogate modeling, implying it can be successfully implemented in practical scenarios and stimulate further research related to TR modeling using physics-informed deep learning.

Blind PV temperature model calibration

The determination of module temperature in a photovoltaic (PV) system is a crucial factor in PV modelling and the assessment of system health status. However, the scarcity of on-site temperature measurements poses a challenge, and existing PV temperature models encounter difficulties in accurately estimating temperatures in systems characterized by unique structural or locational attributes. This paper introduces a novel approach that enables the calibration of PV temperature models without relying on direct temperature measurements. Referred to as blind calibration, this method eliminates the requirement for temperature measurements, thus offering a promising solution to the aforementioned challenges. The method is validated using three datasets, demonstrating accurate PV temperature (TPV) estimation with mean absolute errors below 2 °C. The findings highlight the suitability of the proposed approach for various PV system types, while acknowledging limitations regarding certain system configurations.

Artificial neural network approach for monthly air temperature estimations and maps

Accurate air temperature estimation plays a crucial role in weather planning, and hydrological modeling techniques including water supply, drought analysis, and demand problems of a country. In this study, monthly maximum air temperature (Tmax), minimum air temperature (Tmin), and mean air temperature (T) values for Türkiye were modeled using the Feed-forward neural network (FNN) and Elman neural network (ENN) approaches. In the developed models, monthly Tmax, Tmin, and T values at 81 measurement stations over the period of 1927 through 2021 recorded by the Turkish State Meteorological Service (known as MGM in Türkiye) were used. While the periodicity (month of the year) and geographical variables (latitude, longitude, and altitude) were used as inputs in the developed models, the Tmax, Tmin, and T variables were defined as the output. First of all, long-term monthly air temperature maps were created with the help of the ArcGIS software program using the observed values. Then, maps of long-term air temperatures were created with the ArcGIS software program using the predicted values of the developed artificial neural network models. To prove the accuracy of the models, the predicted long-term air temperature maps were compared with those actual maps. The obtained results showed that the long-term monthly air temperature maps of the studied regions could be created with a small and acceptable error as a function of geographical information and periodicity component. By creating artificial intelligence maps in this way, it will be possible to accurately predict the long-term monthly air temperatures as a function of latitude, longitude, altitude, and the number of months at any point where no measurement is made.

HRLT: a high-resolution (1 d, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China

Abstract. Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km that covers a long-term period (1961 to 2019). It has been named the HRLT, and the dataset is publicly available at https://doi.org/10.1594/PANGAEA.941329 (Qin and Zhang, 2022). In this study, the daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning methods, the generalized additive model, and thin plate splines. It was based on the 0.5∘ × 0.5∘ gridded dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using observation data from meteorological stations across China. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (Cor), coefficient of determination after adjustment (R2), and Nash–Sutcliffe modeling efficiency (NSE) were 1.07 ∘C, 1.62 ∘C, 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R2, and NSE were 1.08 ∘C, 1.53 ∘C, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R2, and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of three other existing datasets, and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution or over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy.

Dynamic high temperature operating life test methodology for long-term switching reliability of GaN power devices

Dynamic High Temperature Operating Life (DHTOL) test is becoming a mandatory test for GaN power devices as it explores the device reliability under application-close conditions. However, the number of applications and circuitries where GaN power devices are intended to be used poses a serious challenge in terms of validity of the application-like test setup used to evaluate the device degradation. In this paper, an accurate model supported by real DHTOL tests was developed precisely including test board and device properties with parasitic elements providing an advanced environment to support the evaluation of the GaN switching behavior and its stability. In order to evaluate and validate the current and voltage peaks, which define the switching stress conditions, we present the methodology to extract device, package, and board parasitics of the complete test setup. Furthermore, an accurate junction temperature (TJ) estimation and prediction technique is also presented being essential to judge and discriminate the natural self-heating effects from the one induced by the RDSON degradation during long-term repetitive hard-switching events.

Black hole and host galaxy growth in an isolated z ∼ 6 QSO observed with ALMA

The outstanding mass growth of supermassive black holes (SMBHs) at the epoch of reionisation and its relation to the concurrent growth of their host galaxies poses challenges to theoretical models aimed at explaining how these systems formed on short timescales (< 1 Gyr). To trace the average evolutionary paths of quasi-stellar objects (QSOs) and their host galaxies in the plane of BH mass to host mass (Mdyn), we compare the star formation rate (SFR), derived from the accurate estimate of the dust temperature and the dust mass (Tdust, Mdust) based on infrared and sub-millimeter (sub-mm) spectral energy distribution (SED), with the BH accretion rate, derived from Lbol based on X-ray and optical and ultraviolet SED. To this aim, we analysed a deep ALMA observation of the sub-mm continuum, [CII], and H2O of the z ∼ 6 QSO J2310+1855 with a resolution of 900 pc, which enabled a detailed study of dust properties and cold gas kinematics. We performed an accurate SED analysis obtaining a dust temperature of Tdust = 71 ± 4 K, dust mass Mdust = (4.4 ± 0.7)×108 M⊙, and total far-infrared luminosity of LTIR = 2.5−0.5+0.6 × 1013 L⊙. The implied active galactic nuclei (AGN) – corrected SFR = 1240−260+310 M⊙ yr−1 is a factor of 2 lower than previously reported for this QSO. We measured a gas-to-dust ratio of GDR = 101 ± 20. The dust continuum and [CII] surface brightness profiles are spatially extended out to r ∼ 6.7 kpc and r ∼ 5 kpc, respectively, with half-light radii of 0.9 and 1.1 kpc for the dust and gas, respectively. The derived gas surface density, Σgas, and star formation rate density, ΣSFR, place the J2310+1855 host galaxy above the Kennicutt-Schmidt relation. We derived a best estimate of the dynamical mass Mdyn = 5.2 × 1010 M⊙ within r = 1.7 kpc based on a dynamical model of the system with a rotating disk inclined at i = 25 deg. The Toomre parameter profile across the disk is Qgas ∼ 3 and implies that the disk is unstable. We found that SFR/Mdyn > ṀBH/MBH, suggesting that AGN feedback might be efficiently acting to slow down the SMBH accretion, while stellar mass assembly is still vigorously taking place in the host galaxy. In addition, we were also able to detect high-velocity emission on the red and blue sides of the [CII] emission line that is not consistent with disk rotation and traces a gaseous outflow. We derived an outflow mass Mout = 3.5 × 108 M⊙, and a mass outflow rate in the range Ṁout = 1800 − 4500 M⊙ yr−1. The implied Ėout ∼ 0.0005 − 0.001 Lbol is in agreement with the values observed for ionised winds. For the first time, we mapped a spatially resolved water vapour disk through the H2O v = 0 3(2, 2) − 3(1, 3) emission line detected at νobs = 274.074 GHz, whose kinematic properties and size are broadly consistent with those of the [CII] disk. The luminosity ratio LH2O/LTIR = 1.4 × 10−5 is consistent with line excitation by dust-reprocessed star formation in the interstellar medium of the host galaxy.

Junction Temperature Estimation via Plug-in System for the Design Validation of IGBT Industrial Power Converters

In the design process of power electronics converters, the power semiconductors thermal modeling is one of the most critical tasks. An accurate estimation of the junction temperature allows the validation of the converter design and also the calibration of the thermal models during prototyping. Therefore, this article presents a straightforward method to estimate the junction temperature directly on the target converter using a plug-in measurement system. The estimation is based on the IGBT <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> -state voltage measurement under high current with a dedicated sensing circuitry externally connected to the converter under test. This solution is specifically designed to fulfill the needs during the prototyping process and design validation since allows a fast and reliable monitoring in all working conditions of interest and the sensing circuitry does not affect the normal operation of the converter under test (CUT). The acquisition circuitry and the calibration procedure are described and tested on a 68-kVA IGBT industrial three-phase inverter. The experimental results obtained on the target inverter prove that the proposed design validation based on the implemented estimation of the junction temperature provides better results than the conventional design methods based on thermal models implemented in simulators.

A novel method for measuring sublingual temperature using conventional non-contact forehead thermometer.

Background: Sublingual temperature measurement is a quick and accurate representation of oral temperature and corresponds closely with core temperature. Sub-lingual temperature measurement using non-contact infrared thermometers has not been studied for this purpose and if accurate they would be a reliable and convenient way of recording temperature of a patient very quickly. The aim of the study was to evaluate the utility of recording sublingual temperature using an infrared non-contact thermometer and establish its accuracy by comparing the readings with tympanic thermometer recordings. Methods: This cross-sectional study was carried out in 29 patients (328 paired recordings from sublingual and tympanic sites simultaneously). Subjects were requested to keep their mouth closed for five minutes before recording the temperature. Sublingual recordings were performed for each patient at different times of the day using an infrared thermometer. The infrared thermometer was quickly brought 1cm away from the sublingual part of the tongue and the recordings were then done immediately. Readings were compared with the corresponding tympanic temperature. Results: The non-contact sublingual temperature correlated very closely with tympanic temperature (r=0.86, p<0.001). The mean difference between the infrared sublingual and tympanic temperature was 0.21°C (standard deviation [SD]:0.48°C, 95% confidence interval [CI] of 0.16-0.27). The intra-class correlation co-efficient (ICC) between core and sublingual temperatures was 0.830 (95% CI: 0.794 to 0.861) p<0.001. The sensitivity ofsublingual IR (infrared) temperature of 37.65°C was 90% and specificity was 89% for core temperature >38°C. Conclusions: This innovative modification of using the forehead infrared thermometer to measure the sublingual temperature offers an accurate, rapid and non-contact estimation of core temperature.

MulT_predict - An optimised comprehensive multicomponent geothermometer

In this study, we introduce MulT_predict as a fully integrated solute multicomponent geothermometer, combining numerical optimisation processes for sensitive parameters to back-calculate to chemical reservoir conditions. This results in a state of the art geothermometer, providing an accurate reservoir temperature estimation validated by geothermal borehole measurements on a worldwide scale. In addition, a universally valid mineral assemblage for an unknown reservoir composition is developed, focusing on worldwide applicability. Using the evolved methodology, the limits of the optimisation processes are determined by using a synthetic brine (150 °C, pH 6, aluminium concentration 0.003 mmol/l) and successively perturbing its geochemical equilibrium state. Individual back-calculation of reservoir conditions lead to valid temperature estimations of 145 °C, 3.4% lower than the initial temperature while a simultaneous and interdependent optimisation reconstructs the sensitive parameters even more precisely with a deviation of 0.056 for the initial pH value, and 0.164 µmol/l for the aluminium concentration.

Study on air temperature estimation and its influencing factors in a complex mountainous area

Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters.

Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries

The rapid development of new energy fields such as electric vehicles and smart grids has put forward higher requirements for the power management of battery integrated systems. Considering that internal temperature and parameter consistency are important factors affecting battery safety and state estimation accuracy, a lumped thermoelectric coupling model based on the multi-time scale effects of battery dynamics parameter is established in this paper. On this basis, a new multi-feature separation modeling idea is proposed and adopted to complete the development of the strong coupling adaptive asynchronous identification strategy to realize the solution of the model. Specifically, the high-frequency and low-frequency characteristics of the resistor–capacitor link under different time constants are distinguished on different time scales. Three sub-filters based on forgetting factor recursive least squares, extended Kalman filtering and joint Kalman filtering are used to realize the adaptive asynchronous synergistic estimation of battery high-frequency dynamics parameter, low-frequency dynamics parameter and internal temperature. In addition, the filters at different time scales are strongly coupled through the voltage response on the diffusion impedance, and the time scale drive under slow dynamics depends on the current distribution of the test conditions. The experimental results of two long-term cycles show that the proposed strategy exhibits excellent terminal voltage tracking effect and internal temperature estimation accuracy. Finally, the concept of parameter dispersion is proposed and discussed. Compared with the results under the traditional identification method, the proposed strategy reduces the maximum parameter dispersion by 51.9%.

Forced Convection in a Transpired Passage With a Porous Medium Insert

Abstract Forced convective heat transfer in a passage with a porous medium matrix subjected to uniform blowing has been fully analyzed, using a local thermal nonequilibrium approach, in application of transpiration systems on the basis of porous medium. An exact solution was found for the asymptotic case of infinitely high interstitial Biot number. In order to extend the problem to general local nonthermal equilibrium cases, an analytical solution procedure has been developed, introducing six unknown parameters to describe both fluid and solid temperature profiles, which are then determined by solving six independent differential and integral equations based on the two energy equations for individual phases and their corresponding boundary conditions. The important performance index for the problem, namely, the overall cooling effectiveness is found quite sensitive not only to the blowing ratio, but also to the interstitial Biot number and fluid to solid effective thermal conductivity ratio. It rises with the blowing ratio and effective thermal conductivity ratio, while it drops as increasing the interstitial Biot number toward the local thermal equilibrium. The present analysis reveals that the local nonthermal equilibrium analysis is definitely needed for accurate estimation of the transpiration-cooled wall temperature.

Machine Learning-Assisted Phase Transition Temperatures from Generalized Replica Exchange Simulations of Dry Martini Lipid Bilayers.

Accurate estimation of phase transition temperatures has been a longstanding challenge for molecular simulations. Recently, the generalized Replica Exchange technique for estimating phase transition temperatures has allowed for improved sampling of the phase transition; however, it requires a significant number of simultaneous replicas both inside and outside of the transition region leading to costly computational expense. In this work, the recently developed machine learning-assisted lipid phase analysis technique for learning the phase of individual lipids has been combined with generalized Replica Exchange Molecular Dynamics to reduce the overall computational expense of evaluating transition temperatures. This technique is then applied to eight different Dry Martini lipids to demonstrate its ability to describe transition temperatures as a function of chain length and tail saturation.

Applicability of the auricular temperature for the assessment of body temperature in healthy large and small domestic species, in a normal metabolic state and in controlled environmental conditions

In clinical practice, an important aspect of animal health status evaluation is the body temperature recording obtained using different methods. Within them, the non-contact and non-invasive infrared thermometer can provide an accurate estimation of body temperature improving the quality of care and medical decision. To evaluate the use of the monitoring of auricular temperature to improve the quality of care and medical decision. To evaluate the use of the monitoring of auricular temperature as indices of body temperature, as well as rectal temperature in eleven clinically healthy mixed bred cats, eleven Rottweiler dogs, and eleven Italian saddle horses temperature values were obtained by means of an infrared thermometer in the left and right ears and by means of a digital thermometer in the rectum. Two-way analysis of variance (ANOVA) indicated a statistically higher rectal temperature value than the temperature recorded in the left and right ears (P<0.001). In all species, no differences between the right and left ear were recorded (p>0.05). Cats and dogs showed a statistically higher auricular (p<0.001) and rectal (p<0.01) temperature compared to horses. No differences were observed between cats and dogs (p>0.05). Auricular and rectal temperature agreement was shown by the Bland-Altman test. Between the two methods, the average difference was 1.6 °C for cats, 1.4 °C for dogs, and 3.3 °C for horses. In dogs, rectal and auricular temperature showed a positive correlation (r2=0.78). In conclusion, only in healthy dogs maintained in controlled conditions the monitoring of auricular temperature reflects the clinical practice gold standard of core body temperature measurement represented by rectal temperature. The lower value of about 1.5 °C must be taken into consideration when this technique is used.

Modelling Soil Temperature by Tree-Based Machine Learning Methods in Different Climatic Regions of China

Accurate estimation of soil temperature (Ts) at a national scale under different climatic conditions is important for soil–plant–atmosphere interactions. This study estimated daily Ts at the 0 cm depth for 689 meteorological stations in seven different climate zones of China for the period 1966–2015 with the M5P model tree (M5P), random forests (RF), and the extreme gradient boosting (XGBoost). The results showed that the XGBoost model (averaged coefficient of determination (R2) = 0.964 and root mean square error (RMSE) = 2.066 °C) overall performed better than the RF (averaged R2 = 0.959 and RMSE = 2.130 °C) and M5P (averaged R2 = 0.954 and RMSE = 2.280 °C) models for estimating Ts with higher computational efficiency. With the combination of mean air temperature (Tmean) and global solar radiation (Rs) as inputs, the estimating accuracy of the models was considerably high (averaged R2 = 0.96–0.97 and RMSE = 1.73–1.99 °C). On the basis of Tmean, adding Rs to the model input had a greater degree of influence on model estimating accuracy than adding other climatic factors to the input. Principal component analysis indicated that soil organic matter, soil water content, Tmean, relative humidity (RH), Rs, and wind speed (U2) are the main factors that cause errors in estimating Ts, and the total error interpretation rate was 97.9%. Overall, XGBoost would be a suitable algorithm for estimating Ts in different climate zones of China, and the combination of Tmean and Rs as model inputs would be more practical than other input combinations.

Accurate Estimation of Water Temperature from UAV-mounted Thermal Camera: the Use of Generalized Additive Models and Dynamic Programming Algorithm to Correct for Vingetting Effect and Thermal Shift

Accurate Estimation of Water Temperature from UAV-mounted Thermal Camera: the Use of Generalized Additive Models and Dynamic Programming Algorithm to Correct for Vingetting Effect and Thermal Shift

Feasibility of Random Forest and Multivariate Adaptive Regression Splines for Predicting Long-Term Mean Monthly Dew Point Temperature

The accurate estimation of dew point temperature (Tdew) is important in climatological, agricultural, and agronomical studies. In this study, the feasibility of two soft computing methods, random forest (RF) and multivariate adaptive regression splines (MARS), is evaluated for predicting the long-term mean monthly Tdew. Various weather variables including air temperature, sunshine duration, relative humidity, and incoming solar radiation from 50 weather stations in Iran as well as their geographical information (or a subset of them) are used in RF and MARS as inputs. Three statistical indicators namely, root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) are used to assess the accuracy of Tdew estimates from both models for different input configurations. The results demonstrate the capability of the RF and MARS methods for predicting the long-term mean monthly Tdew. The combined scenarios in both the RF and MARS methods are found to produce the best Tdew estimates. The best Tdew estimates were obtained by the MARS model with the RMSE, MAE, and R of respectively 0.17°C, 0.14°C, and 1.000 in the training phase; 0.15°C, 0.12°C, and 1.000 in the validation phase; and 0.18°C, 0.14°C, and 0.999 in the testing phase.

Stellar population astrophysics (SPA) with the TNG

Context. The Perseus complex in the outer disk of the Galaxy hosts a number of clusters and associations of young stars. Gaia is providing a detailed characterization of their kinematic structure and evolutionary properties. Aims. Within the SPA Large Program at the TNG, we secured HARPS-N and GIANO-B high-resolution optical and near-infrared (NIR) spectra of the young red supergiant (RSG) stars in the Perseus complex in order to obtain accurate radial velocities, stellar parameters, and detailed chemical abundances. Methods. We used spectral synthesis to best fit hundreds of atomic and molecular lines in the spectra of the observed 27 RSGs. We obtained accurate estimates of the stellar temperature, gravity, micro- and macroturbulence velocities, and chemical abundances for 25 different elements. We also measured the 12C/13C abundance ratio. Results. Our combined optical and NIR chemical study provides homogeneous half-solar iron with a low dispersion, about solar-scaled abundance ratios for the iron-peak, alpha-, and other light elements, and a low enhancement of Na, K, and neutron-capture elements. This is consistent with the thin-disk chemistry traced by older stellar populations at a similar galactocentric distance of about 10 kpc. We inferred an enhancement of N and a depletion of C and of the 12C/13C isotopic abundance ratio that are consistent with mixing processes in the stellar interiors during the RSG evolution.

Finite-size scaling at fixed renormalization-group invariant.

Finite-size scaling at fixed renormalization-group invariant is a powerful and flexible technique to analyze Monte Carlo data at a critical point. It consists in fixing a given renormalization-group invariant quantity to a given value, thereby trading its statistical fluctuations with those of a parameter driving the transition. One remarkable feature is the observed significant improvement of statistical accuracy of various quantities, as compared to a standard analysis. We review the method, discussing in detail its implementation, the error analysis, and a previously introduced covariance-based optimization. Comprehensive benchmarks on the Ising model in two and three dimensions show large gains in the statistical accuracy, which are due to cross-correlations between observables. As an application, we compute an accurate estimate of the inverse critical temperature of the improved O(2) ϕ^{4} model on a three-dimensional cubic lattice.