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Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R2, and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust. [ABSTRACT FROM AUTHOR]

This study analyzes the impact of weather factors on the resilience of airport flight operations, focusing on flight performance, economic outcomes, and transportation capacity. A Kolmogorov–Arnold Network (KAN) model was employed to identify key weather variables and establish the relationship between these factors and airport operational resilience. Xi'an Xianyang International Airport was used as a case study, with the weights of various routes determined using grey relational analysis, considering average daily flight volume, flight distance, and airport flow stability indicators. Flight operation records and weather data were utilized to assess the influence of critical weather factors on key operational resilience metrics. The findings reveal that routes in economically developed areas exert a more pronounced effect on flow stability. Temperature and wind speed emerged as the most influential factors, with importance values of 0.35 and 0.32, respectively, about flight operations and economic performance. Furthermore, changes in wind direction and wind speed had the greatest impact on transportation capacity, with importance values of 0.7 and 0.65, respectively. These results highlight the need for special attention to weather factors such as temperature and wind speed during flight scheduling and risk assessment to ensure operational safety, efficiency, and economic viability. [ABSTRACT FROM AUTHOR]

This article examines the impact of meteorological conditions represented by ambient temperature, ambient humidity, wind speed, and daily precipitation sum on the reliability of low-voltage cable lines. Cable line reliability is crucial to the stability and safety of power systems. Failure of cable lines can lead to power outages. This can cause serious economic and social consequences, as well as threaten human safety, especially in the public sector and critical infrastructure. In addition, any interruption of cable lines generates costs related to repairs, operational losses, and possible contractual penalties. This is why it is so important to investigate the causes of power equipment failures. Many power system failures are caused by weather factors. The main purpose of this article is to quantify the actual impact of weather conditions on the performance and reliability of power equipment in distribution networks. Reliability indicators (failure rate, failure duration, restoration rate, and failure coefficient) for low-voltage cable lines were calculated as a function of weather conditions. Empirical values of the indicators were determined based on many years of observations of power lines operating in the Polish power system. An analysis of the conformity of their empirical distribution with the assumed theoretical model was also conducted. By quantifying the impact of specific weather factors on the operation of power equipment, it becomes possible to identify the ranges in which failures are most likely. [ABSTRACT FROM AUTHOR]

The article presents a study on the influence of weather factors (ambient temperature) on the operational reliability of overhead low-voltage power lines with bare conductors. A method for determining the average failure intensity, average failure duration, average renewal intensity, and failure rate of overhead low-voltage power lines with bare conductors as a function of ambient temperature is presented. Based on many years of observations of power lines operated in electric power distribution networks in Poland, the empirical values of the above-mentioned reliability indicators were determined. An analysis of empirical distribution compliance with the assumed theoretical model was also carried out. The reliability studies conducted showed that the highest failure intensity of the considered power lines occurred at temperatures commonly found in Poland. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven framework aimed at investigating how weather factors affect the spatio-temporal patterns of air traffic flow in the terminal maneuvering area (TMA) is presented. The framework mainly consists of three core modules, namely, trajectory structure characterization, flow pattern recognition, and association rule mining. To fully characterize trajectory structure, abnormal trajectories and typical operations are sequentially extracted based on a deep autoencoder network with two specially designed loss functions. Then, using these extracted elements as basic components to further construct and cluster per-hour-level descriptions of airspace structure, the spatio-temporal patterns of air traffic flow can be recognized. Finally, the association rule mining technique is applied to find sets of weather factors that often appear together with each flow pattern. Experimental analysis is demonstrated on two months of arrival flight trajectories at Hong Kong International Airport (HKIA). The results clearly show that the proposed framework effectively captures spatial anomalies, fine-grained trajectory structures, and representative flow patterns. More importantly, it also reveals that those flow patterns with non-conforming behaviors result from complex interactions of various weather factors. The findings provide valuable insights into the causal relationships between weather factors and changes in flow patterns, greatly enhancing the situational awareness of TMA. [ABSTRACT FROM AUTHOR]

There has been an insurgence of allergic respiratory diseases such as asthma and rhinitis in industrialized countries in the last few decades as a result of the interaction between air pollutants and pollen, which has become a global and dramatic health problem. Air pollutants such as nitrogen dioxide, sulfur dioxide, ozone, and carbon dioxide affect the physical, chemical and biological properties of pollen such as the pollen content, production, and allergenicity, exacerbating symptoms in vulnerable subjects. When investigating these interactions and their effects, the environmental impact of climate change, weather variables and urbanization should be taken into account as well as the pollen species, type of pollutant, conditions of exposure, and individual susceptibility. Up to 25% of asthma adult cases are work-related, because several categories of workers in different sectors are exposed to aeroallergens and outdoor air pollutants. Thus, in this study, we evaluated the significant impacts of occupational allergies on worker's health and quality of life. In summary, to assess the effect of interactions between air pollutants and pollen on public and occupational health, all the factors that play a role in this context will be investigated, including environmental factors, individual susceptibility in relation to pollen species, type of pollutants, and conditions of exposure. [ABSTRACT FROM AUTHOR]

This paper presents the models developed for the short-term forecasting of energy production by photovoltaic panels. An analysis of a set of weather factors influencing daily energy production is presented. Determining the correlation between the produced direct current (DC) energy and the individual weather parameters allowed the selection of the potentially best explanatory factors, which served as input data for the neural networks. The forecasting models were based on MLP and Elman-type networks. An appropriate selection of structures and learning parameters was carried out, as well as the process of learning the models. The models were built based on different time periods: year-round, semi-annual, and seasonal. The models were developed separately for monocrystalline and amorphous photovoltaic modules. The study compared the models with the predicted and measured insolation energy. In addition, complex forecasting models were developed for the photovoltaic system, which could forecast DC and AC energy simultaneously. The complex models were developed according to the rules of global and local modeling. The forecast errors of the developed models were included. The smallest values of the DC energy forecast errors were achieved for the models designed for summer forecasts. The percentage forecast error was 1.95% using directly measured solar irradiance and 5. 57% using predicted solar irradiance. The complex model for summer forecasted the AC energy with an error of 1.86%. [ABSTRACT FROM AUTHOR]

Late blight, caused by Phytophthora infestans, is a major disease of the potato crop with a strong negative impact on tuber yield and tuber quality. The control of late blight in conventional potato production systems is often through weekly application of prophylactic fungicides, moving away from a sustainable production system. In support of integrated pest management practices, machine learning algorithms were proposed as tools to forecast aerobiological risk level (ARL) of Phytophthora infestans (>10 sporangia/m3) as inoculum to new infections. For this, meteorological and aerobiological data were monitored during five potato crop seasons in Galicia (northwest Spain). Mild temperatures (T) and high relative humidity (RH) were predominant during the foliar development (FD), coinciding with higher presence of sporangia in this phenological stage. The infection pressure (IP), wind, escape or leaf wetness (LW) of the same day also were significantly correlated with sporangia according to Spearman's correlation test. ML algorithms such as random forest (RF) and C5.0 decision tree (C5.0) were successfully used to predict daily sporangia levels, with an accuracy of the models of 87% and 85%, respectively. Currently, existing late blight forecasting systems assume a constant presence of critical inoculum. Therefore, ML algorithms offer the possibility of predicting critical levels of Phytophthora infestans concentration. The inclusion of this type of information in forecasting systems would increase the exactitude in the estimation of the sporangia of this potato pathogen. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite traditional reports on the ability of female dromedaries to modulate pregnancy length in response to environmental conditions, no empirical study has been developed. According to the present results, female dromedaries would adjust the onset of parturition to give birth around darker and, therefore, safer nights since social communication and interaction between congeners are increased under dim light conditions, mostly mediated by hormonal signals. Furthermore, the time of delivery, a highly energy-demanding process, may also be modulated by mean wind speed and its transient increases since these weather variables affect the thermal comfort and, thus, the heat transfer between an animal and its environment and the individual energy budget. Gravid females may be more energetically compromised when the offspring is a male; hence, the onset of parturition for the newborns of the male sex will be more probable to occur on slightly brighter nights and when mean wind speed is lower when compared to their female counterparts. Such specific phenology would favor a proper multisensory interaction between the mother and the more immature male young at neonatal stages so that thermoregulatory demands are minimized for these reproductive tasks, and then the fitness of parents and the survival of offspring are improved. Given energy costs for gestating and caring for male offspring are higher than those of female newborns, external environmental conditions might be regarded as likely to affect the timing of delivery processes differentially depending on the sex of the newborn calf to be delivered. The aim of the present paper is to evaluate the association between environmental stressors such as the moon phase and weather-related factors and the onset of labor in female dromedaries. A binary logistic regression model was developed to find the most parsimonious set of variables that are most effective in predicting the probability for a gravid female dromedary to give birth to a male or a female calf, assuming that higher gestational costs and longer labor times are ascribed to the production of a male offspring. Although the differences in the quantitative distribution of spontaneous onset of labor across lunar phases and the mean climate per onset event along the whole study period were deemed nonsignificant (p > 0.05), a non-negligible prediction effect of a new moon, mean wind speed and maximum wind gust was present. At slightly brighter nights and lower mean wind speeds, a calf is more likely to be male. This microevolutionary response to the external environment may have been driven by physiological and behavioral adaptation of metabolic economy and social ecology to give birth to cooperative groups with the best possible reduction of thermoregulatory demands. Model performance indexes then highlighted the heterothermic character of camels to greatly minimize the impact of the external environment. The overall results will also enrich the general knowledge of the interplay between homeostasis and arid and semi-arid environments. [ABSTRACT FROM AUTHOR]

In order to enrich the research on the influence of weather factors on agricultural economy and provide practical decision-making references for the relevant market entities, this study took pointed pepper, loofah, Chinese chives and tomato as examples, using weekly wholesale prices and corresponding weather factors data from one of the main production areas in China based on the vector autoregressive (VAR) and the time-varying parameter vector autoregressive (TVP-VAR) models to explore the dynamic impacts of weather changes on vegetable price fluctuations. It was found that price fluctuations of specific vegetable varieties were affected by changes in specific weather factors. The shock intensity of weather factor changes in the same lag period on the current vegetable price fluctuations was time-varying, which could describe the historical dynamics of the impacts of weather changes on vegetable price fluctuations. The dissipation characteristics of the dynamic impacts of weather factors changes occurring at selected time points on vegetable price fluctuations had strong consistency, and the obtained rules could be used to analyze the life cycle and dynamic characteristics of extreme weather impacts on vegetable price fluctuations. In addition, vegetable price fluctuations were affected mostly by their own lag periods, and the shock intensity of vegetable price fluctuations with equal lag periods on their own current price fluctuations were relatively stable. The dynamic impacts of vegetable price fluctuations occurring at chosen time points on its own later fluctuations were highly consistent in the variation from the beginning of the strongest positive shock to the final dissipation. [ABSTRACT FROM AUTHOR]

Secondary infections of early blight during potato crop season are conditioned by aerial inoculum. However, although aerobiological studies have focused on understanding the key factors that influence the spore concentration in the air, less work has been carried out to predict when critical concentrations of conidia occur. Therefore, the goals of this study were to understand the key weather variables that affect the hourly and daily conidia dispersal of Alternaria solani and A. alternata in a potato field, and to use these weather factors in different machine learning (ML) algorithms to predict the daily conidia levels. This study showed that conidia per hour in a day is influenced by the weather conditions that characterize the hour, but not the hour of the day. Specifically, the relative humidity and solar radiation were the most relevant weather parameters influencing the conidia concentration in the air and both in a linear model explained 98% of the variation of this concentration per hour. Moreover, the dew point temperature three days before was the weather variable with the strongest effect on conidia per day. An improved prediction of Alternaria conidia level was achieved via ML algorithms when the conidia of previous days is considered in the analysis. Among the ML algorithms applied, the CART model with an accuracy of 86% were the best to predict daily conidia level. [ABSTRACT FROM AUTHOR]

The spongy moth (Lymantria dispar) is a forest pest that damages a variety of trees in North America and Eurasia. A spongy moth outbreak occurred in part of South Korea in 2020 and caused severe damage to domestic forests and human society. Since the occurrence of spongy moths is influenced by climatic factors, this study examines the causes of spongy moth outbreaks by analyzing the temporal and spatial differences in climatic factors, influencing spongy moth occurrence using specimens collected during field surveys. Climatic factors were identified using global occurrence coordinates to compare the weather characteristics of spongy moth occurrence in domestic regions, using the kernel density function. Spatial and temporal comparisons were performed for monthly weather factors obtained from field surveys in 2020 and 2021 in areas with high and low spongy moth larvae densities. Spongy moth outbreaks may result from particular combinations of variable seasonality in temperature and precipitation, including high temperatures during cold periods and low precipitation during developmental periods. [ABSTRACT FROM AUTHOR]

Sugarcane is an important sugar source in America and is mainly planted in the states of Florida and Louisiana. The purpose of this study was to predict the sugarcane yield in these two states from 2008 to 2016. Three statistical sugarcane yield models (i.e., the CNDVI, K–M, and SiPAR models) were applied to predict yield in America, using remote sensing and weather data. To verify the robustness of models, model parameters obtained in places (i.e., Reunion Island and Southwestern China) far away from America were used. The results showed that the SiPAR model outperformed the CDNVI and K–M models for yield prediction. Solar radiation was an important constraint factor to ensure the statistical model's robustness under different conditions. The CNDVI model had the lowest robustness because of the absence of solar radiation, although it could reflect the yield trend to some extent. The K–M model failed to predict the low sugarcane yield, owing to the lack of consideration of temperature and soil water stresses. Florida had a low sugarcane yield in the west and southwest; however, Louisiana had high sugarcane yield in the same directions. This study demonstrated the robustness of the SiPAR model and investigated the sugarcane yield status in Florida and Louisiana. It can be a reference for similar studies in the future. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to evaluate the impact of weather factors on stroke parameters. Methods: This retrospective study analyzed the records of stroke patients concerning the influence of meteorological conditions and moon phases on stroke parameters. Results: The study group consisted of 402 patients aged between 20 and 102; women constituted 49.8% of the subjects. Ischaemic stroke was diagnosed in 90.5% of patients and hemorrhagic stroke was diagnosed in 9.5% of patients. The highest number of hospitalizations due to stroke was observed in January (48 events); the lowest number was observed in July (23 events). There was no statistically significant correlation between the meteorological parameters on the day of onset and the preceding day of stroke and the neurological status (NIHSS) of patients. Mean air temperature on the day of stroke and the day preceding stroke was significantly lower in the group of patients discharged with a very good functional status (≤2 points in modified Rankin scale (mRS)) compared to the patients with a bad functional status (>2 points in mRS); respectively: 7.98 ± 8.01 vs. 9.63 ± 7.78; p = 0.041 and 8.13 ± 7.72 vs. 9.70 ± 7.50; p = 0.048). Humidity above 75% on the day of stroke was found to be a factor for excellent functional state (RR 1.61; p = 0.016). The total anterior circulation infarcts (in comparison with stroke in the other localization) were more frequent (70%) during a third quarter moon (p = 0.011). The following parameters had a significant influence on the number of stroke cases in relation to autumn having the lowest number of onsets: mean temperature (OR 1.019 95% CI 1.014–1.024, p < 0.000), humidity (OR 1.028, CI 1.023–1.034, p < 0.0001), wind speed (OR 0.923, 95% CI 0.909–0.937, p < 0.0001), insolation (OR 0.885, 95% CI 0.869–0.902, p < 0.0001), precipitation (OR 0.914, 95% CI 0.884–0.946, p < 0.0001). Conclusion: Air humidity and air temperature on the day of stroke onset as well as air temperature on the day preceding stroke are important for the functional status of patients in the acute disease period. A combination of the following meteorological parameters: lowered mean temperature and low sunshine, high humidity and high wind speed all increase the risk of stroke during the winter period. High humidity combined with high precipitation, low wind speed and low sunshine in the autumn period are associated with the lowest stroke incidence risk. A possible relationship between phases of the moon and the incidence requires further investigation. [ABSTRACT FROM AUTHOR]

Weather information is an important factor in short-term load forecasting (STLF). However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature selection for STLF. The main aim of this paper is to develop a novel methodology based on Fisher information for meteorological variables introduction and variable selection in STLF. Fisher information computation for one-dimensional and multidimensional weather variables is first described, and then the introduction of meteorological factors and variables selection for STLF models are discussed in detail. On this basis, different forecasting models with the proposed methodology are established. The proposed methodology is implemented on real data obtained from Electric Power Utility of Zhenjiang, Jiangsu Province, in southeast China. The results show the advantages of the proposed methodology in comparison with other traditional ones regarding prediction accuracy, and it has very good practical significance. Therefore, it can be used as a unified method for introducing weather variables into STLF models, and selecting their features. [ABSTRACT FROM AUTHOR]

Many studies have shown that the weather greatly affects the tourist count. Understanding weather information, climate change, and how they influence the tourist count in different tourist seasons (peak season, second peak season, off season) can help park planners and managers to analyze the opportunities and risks caused by climate change. This study aimed to predict the visitor count through information on the number of visitors and the weather day for three tourist seasons in a 12-month period. The study was conducted in the Huisun Forest Park of Taiwan based on the peak season (February, July, August, and October), the second peak season (January, April, May, November, and December), and the off-season (March, June, and September), using weather factors and virtual factors (such as whether it is a weekend or not) to establish three multivariate regression models for predicting the daily visitor count. This study assessed the impact of climate change on the visitor count and analyzed possible scenarios of climate change using representative concentration pathways (RCPs), as stated in the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC). The results of this study indicated that the impacts of weather factors on the visitor count is the same for the peak season and the off season. The temperature and relative humidity have a significant impact on the visitor count, and precipitation is not significant. In the second peak season, only the temperature has a significant impact on the visitor count. The relative humidity and precipitation are not significant. The temperature is the most influential factor in all three seasons, and has the highest influence on the peak season, followed by the low season, and then the second peak season. In addition, the number of visitors in Huisun Forest Park is on the rise, according to an analysis of various climate change scenarios (RCP2.6, RCP4.5, RCP6.0, RCP8.5). The results of this study can be used as a reference by forest park managers and future researchers. It is noted that the results were based on the current economic and political situation. The worsening of the entire world situation could break the relationships. [ABSTRACT FROM AUTHOR]