Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental cons...Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental consequences,particularly in mountain regions.Here we explore spatio-temporal patterns of rime formation as a proxy for the propensity of individual sites to form rime ice.We present the recent time trends in rime ice occurrence and thickness measured by 23 professional meteorological stations in the Czech Republic in 2002–2023.In an exploratory data analysis,we found high year-to-year variability in rime occurrence and thickness at all sites.According to the annual mean number of hours with rime detected,the stations situated at the highest altitudes are significantly different(higher)from the rest of the sites.The highest rime hour and thickness records by far were observed at the LYSA station in the Beskydy(Beskid)Mts situated at the exposed mountaintop and highly elevated above the surrounding terrain.For advanced statistical modelling of rime thickness,we used two generalised additive models that account for long-term trends(potentially nonlinear),seasonal and daily variability.In an expanded model we further considered the effect of the North Atlantic Oscillation(NAO)index.All the parameters included in the models proved to be statistically significant,although the strength of their effect differed.Factors affecting the rime formation(meteorology and terrain)are strongly site-specific and identification of the significance of individual influencing factors remains a challenging task for our future research.Here,we explore a rare long-term rime record with detailed temporal resolution from multiple uniformly measured sites,which significantly enhances our understanding of rime formation.Additionally,the rime record is from a temperate zone,where rime forms only during a small part of the year.展开更多
Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is pr...Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.展开更多
Parameter extraction of photovoltaic(PV)models is crucial for the planning,optimization,and control of PV systems.Although some methods using meta-heuristic algorithms have been proposed to determine these parameters,...Parameter extraction of photovoltaic(PV)models is crucial for the planning,optimization,and control of PV systems.Although some methods using meta-heuristic algorithms have been proposed to determine these parameters,the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases.The unstable results will affect the reliable operation and maintenance strategies of PV systems.In response to this challenge,an improved rime optimization algorithm with enhanced exploration and exploitation,termed TERIME,is proposed for robust and accurate parameter identification for various PV models.Specifically,the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity.Meanwhile,a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth.The TERIME algorithm is applied to estimate the optimal parameters of the single diode model,double diode model,and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France,Photo Watt-PWP 201 and S75.According to the statistical analysis in 100 runs,the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions.All of our source codes are publicly available at https://github.com/dirge1/TERIME.展开更多
基金financially supported by the Technological Agency of the Czech Republic (TAČR), Joint Grant No SS 02030031 ARAMISby the long-term strategic development financing of the Institute of Computer Science of the Czech Academy of Sciences (RVO 67985807)
文摘Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental consequences,particularly in mountain regions.Here we explore spatio-temporal patterns of rime formation as a proxy for the propensity of individual sites to form rime ice.We present the recent time trends in rime ice occurrence and thickness measured by 23 professional meteorological stations in the Czech Republic in 2002–2023.In an exploratory data analysis,we found high year-to-year variability in rime occurrence and thickness at all sites.According to the annual mean number of hours with rime detected,the stations situated at the highest altitudes are significantly different(higher)from the rest of the sites.The highest rime hour and thickness records by far were observed at the LYSA station in the Beskydy(Beskid)Mts situated at the exposed mountaintop and highly elevated above the surrounding terrain.For advanced statistical modelling of rime thickness,we used two generalised additive models that account for long-term trends(potentially nonlinear),seasonal and daily variability.In an expanded model we further considered the effect of the North Atlantic Oscillation(NAO)index.All the parameters included in the models proved to be statistically significant,although the strength of their effect differed.Factors affecting the rime formation(meteorology and terrain)are strongly site-specific and identification of the significance of individual influencing factors remains a challenging task for our future research.Here,we explore a rare long-term rime record with detailed temporal resolution from multiple uniformly measured sites,which significantly enhances our understanding of rime formation.Additionally,the rime record is from a temperate zone,where rime forms only during a small part of the year.
基金the 2023 Liaoning Institute of Science and Technology Doctoral Program Launch Fund(2307B29),covering aspects such as data collection and publication of the paper。
文摘Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.
基金supported by the National Natural Science Foundation of China[grant number 51775020]the Science Challenge Project[grant number.TZ2018007]+2 种基金the National Natural Science Foundation of China[grant number 62073009]the Postdoctoral Fellowship Program of CPSF[grant number GZC20233365]the Fundamental Research Funds for Central Universities[grant number JKF-20240559].
文摘Parameter extraction of photovoltaic(PV)models is crucial for the planning,optimization,and control of PV systems.Although some methods using meta-heuristic algorithms have been proposed to determine these parameters,the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases.The unstable results will affect the reliable operation and maintenance strategies of PV systems.In response to this challenge,an improved rime optimization algorithm with enhanced exploration and exploitation,termed TERIME,is proposed for robust and accurate parameter identification for various PV models.Specifically,the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity.Meanwhile,a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth.The TERIME algorithm is applied to estimate the optimal parameters of the single diode model,double diode model,and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France,Photo Watt-PWP 201 and S75.According to the statistical analysis in 100 runs,the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions.All of our source codes are publicly available at https://github.com/dirge1/TERIME.
