Real-time Weather Conditions Research Articles

USING ARTIFICIAL INTELLIGENCE TO ASSESS SOLAR RADIATION FROM THE TOTAL SKY IMAGES

Solar power generation converts solar radiation into electrical energy. It is the most environmentally friendly green energy source in modern times, but the solar radiation reception rate is unstable due to weather. The general weather forecast is for the climate of a large area and cannot provide effective real-time prediction to the area where the power plant generating radiant energy from solar radiation. The sky imager can collect the sky image of the location of the solar power panel in real time, which can help to understand the weather conditions in real time, especially the dynamics of the clouds, which is the main reason for affecting the solar power generation. In this study, the optical flow method was used to analyze the motion vectors of clouds in the sky image, thereby estimating the changes of clouds in a short time, and the correlation between the distribution of clouds in the sky and the radiation of the whole sky images was analyzed through a neural network. The change further predicts the change in radiation across the sky, thereby effectively assessing the efficiency of solar power generation.

Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis.

This study presents an empirical investigation of the impacts of real-time weather conditions on the freeway crash severity. A Bayesian spatial generalized ordered logit model was developed for modeling the crash severity using the hourly wind speed, air temperature, precipitation, visibility, and humidity, as well as other observed factors. A total of 1424 crash records from Kaiyang Freeway, China in 2014 and 2015 were collected for the investigation. The proposed model can simultaneously accommodate the ordered nature in severity levels and spatial correlation across adjacent crashes. Its strength is demonstrated by the existence of significant spatial correlation and its better model fit and more reasonable estimation results than the counterparts of a generalized ordered logit model. The estimation results show that an increase in the precipitation is associated with decreases in the probabilities of light and severe crashes, and an increase in the probability of medium crashes. Additionally, driver type, vehicle type, vehicle registered province, crash time, crash type, response time of emergency medical service, and horizontal curvature and vertical grade of the crash location, were also found to have significant effects on the crash severity. To alleviate the severity levels of crashes on rainy days, some engineering countermeasures are suggested, in addition to the implemented strategies.

Investigation of interunit dispersion in 2D street canyons: A scaled outdoor experiment

Interunit dispersion problems have been studied previously mainly through on-site measurements, wind tunnel tests, and CFD simulations. In this study, a scaled outdoor experiment was conducted to examine the interunit dispersion characteristics in consecutive two-dimensional street canyons. Tracer gas (CO2) was continuously released to simulate the pollutant dispersion routes between the rooms in street canyons. The wind velocity, wind direction, air temperature, and tracer gas concentrations were monitored simultaneously. Two important parameters, the air exchange rate and reentry ratio, were analyzed to reveal the ventilation performance and interunit dispersion of the rooms in the street canyons. Based on the real-time weather conditions, it was found that the ventilation performance of the source room varied according to the room location. The air exchange rate distribution of the leeward-side room was more stable than that of the windward side. The tracer gas was mainly transported in the vortex direction inside the street canyon, and the highest reentry ratio was observed at the room nearest to the source room along the transportation route. In addition, under real weather conditions, the rooms in the street canyon have a high probability of experiencing a high reentry ratio based on the maximum reentry ratio of each room. This study provides authentic airflow and pollutant dispersion information in the street canyons in an urban environment. The dataset of this experiment can be used to validate further numerical simulations.

A Review of Dynamic Thermal Line Rating Methods With Forecasting

Power flow, on both AC and DC overhead transmission lines, is limited to keep the conductor temperature below a maximum (TCMAX) specified to limit both conductor sag and the aging of conductors and splices over time. This power flow limit (line thermal rating) varies with weather conditions along the line corridor but, for simplicity, static line ratings (SLR) are normally calculated for “suitably conservative” annual or seasonal weather conditions. Dynamic line ratings (DLR) change with the real-time weather conditions along the line, are usually higher than SLR, and are more complex to use in system operation. Forecasting of DLR requires forecasting of line corridor weather conditions but makes DLR more useful to power system operations. This paper discusses DLR methods including forecast techniques and presents various field applications.

Effect of weather conditions on leans of one Eugenia Grandis tree in Singapore

An approach to model the effect of weather conditions on tree stability is presented, where the weather conditions include wind, rainfall, temperature, humidity and solar radiation. The weather conditions could affect the rainwater infiltration and evapotranspiration, hence change the water content in the soil and influence the soil properties such as stiffness and strength when resisting the wind drags on the tree. One-dimensional (1D) modelling in GeoStudio 2012 was used to analyse the pore-water pressure head distribution in soil under the recorded weather conditions for 20 days. Three-dimensional (3D) modelling in ANSYS 19.1 was then used to analyse the leans of one Eugenia Grandis tree deformed by wind drags in Singapore during the 20 days. The soil modulus and soil strength were considered as functions of the water content in the soil, which were computed from VADOSE modelling in GeoStudio 2012 under the real time weather conditions measured from the field instrumentations. Moreover, the wind drags were calculated from a previous study that analysed the drag coefficient on the same Eugenia Grandis tree. The leans of the Eugenia Grandis tree under wind drags during the studied period were computed by incorporating the change in soil stiffness and soil strength due to the change in water content of the soil. The computed tree leans from the analyses increased by 16.5% when the soil strength and soil modulus changed with water content of the soil as compared to the computed tree leans without the incorporation of the changes in soil strength as an effect of weather conditions and assuming a constant soil modulus of 20 MPa. It is suggested that effect of weather conditions on soil should be considered in the analyses related to tree deformations.

Snow Detection using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest

Snowfall negatively affects pavement and visibility conditions, making it one of the major causes of motor vehicle crashes in winter weather. Therefore, providing drivers with real-time roadway weather information during adverse weather is crucial for safe driving. Although road weather stations can provide weather information, these stations are expensive and often do not represent real-time trajectory-level weather information. The main motivation of this study was to develop an affordable in-vehicle snow detection system which can provide trajectory-level weather information in real time. The system utilized SHRP2 Naturalistic Driving Study video data and was based on machine learning techniques. To train the snow detection models, two texture-based image features including gray level co-occurrence matrix (GLCM) and local binary pattern (LBP), and three classification algorithms: support vector machine (SVM), k-nearest neighbor (K-NN), and random forest (RF) were used. The analysis was done on an image dataset consisting of three weather conditions: clear, light snow, and heavy snow. While the highest overall prediction accuracy of the models based on the GLCM features was found to be around 86%, the models considering the LBP based features provided a much higher prediction accuracy of 96%. The snow detection system proposed in this study is cost effective, does not require a lot of technical support, and only needs a single video camera. With the advances in smartphone cameras, simple mobile apps with proper data connectivity can effectively be used to detect roadway weather conditions in real time with reasonable accuracy.

Forecasting of poor visibility episodes in the vicinity of Tenerife Norte Airport

Aviation safety is a priority that may be compromised by adverse weather conditions. This is the case for poor visibility in the vicinity of airports, which can pose a risk during takeoff and landing. For airports that are prone to fog because of their location, an accurate forecast of poor-visibility episodes is vital. However, the forecasting of low clouds is still a challenge in numerical weather prediction, especially when an airport is near complex terrain for which the use of non-hydrostatic mesoscale models is mandatory. All these factors are present at Tenerife Norte Airport, which is commonly affected by poor visibility from low clouds related to persistent trade winds and moist flows from the Atlantic Ocean.In this paper, several methods for estimating visibility based on mesoscale model outputs are tested. Use of the HARMONIE-AROME model is encouraged because of its excellent performance in the detection of poor-visibility episodes (False Alarm Ratio = 0.34–0.38; Frequency Of Misses = 0.22–0.38, depending on the model version and method used). In addition, the use of satellite application facilities is proposed for the nowcasting of low clouds affecting the airport area. Specifically, we used products that estimate cloud type, cloud top altitude, and integrated water vapor content in the boundary layer. Finally, an application is presented for the monitoring of weather conditions in real time to estimate poor-visibility risk.

Techno-economic optimization and real-time comparison of sun tracking photovoltaic system for rural healthcare building

This paper studies the optimum sizing and techno-economic feasibility of photovoltaic systems for rural healthcare building located in a south Indian State, Tamil Nadu, which experiences tropical weather climate. In this study, fixed tilt, annual optimum tilt (AOT), and six other sun tracking installation types are analyzed using hybrid optimization of multiple energy resource (HOMER) energy to understand the power generation potential. As a first step, weekly and monthly optimum tilt angles and AOT are estimated for the study location. The selection of the optimum configuration is arrived with the lowest values of initial cost, operating cost, total net present cost, and levelized cost of energy. It has been found that AOT with fixed installation is the optimum configuration for the study location. However, the performance would vary when the system is put to operate in practical conditions. In order to understand the varying performance, a prototype model is implemented for horizontal axis sun tracking system. The performance of the prototype is analyzed as per the varying real-time weather conditions for four days typically considering the sandstorm day, cloudy day, clear day, and partially cloudy day.

Iot Based Weather Station Using Raspberry Pi 3

Weather is the day-to-day state of atmosphere that is hard to predict which affects the activities of mankind and has great significance in many different domains. However, the current weather station in the market is expensive and bulky which cause inconvenience. The aim of this project is to design a weather station with real time notifications for climatology monitoring, interface it to a cloud platform and analyse weather parameters. In this project, a weather station is assembled using SparkFun Weather Shield and Weather Meter and Arduino Uno R3 to collect weather parameters. Data collected from the sensors are then stored into Google Cloud SQL using Raspberry Pi 3 Model B which acts as a gateway between them and analysis of weather data are done. A website and mobile application are developed using Google Data Studio and Android Studio respectively to display the real-time weather conditions in graphical presentation which are accessible by administrator and users. Users will receive notification regarding the weather conditions at that particular place on social media platform regularly and irregularly. Weather prediction is done in short term which allows users to get themselves prepared for their future plan in the next thirty minutes.

Applying a random parameters Negative Binomial Lindley model to examine multi-vehicle crashes along rural mountainous highways in Malaysia

Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial – Lindley (RPNB-L) and Random Parameters Negative Binomial – Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries.

Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data

IntroductionDriving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction. MethodCrash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models. ResultsModel estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood. ConclusionsThe study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated.

Weather and cycling: Mining big data to have an in-depth understanding of the association of weather variability with cycling on an off-road trail and an on-road bike lane

Although cycling is an easy and popular form of physical activity and urban travel, barriers exist. In particular, cycling is more likely and more severely to be affected by inclement weather than the motorized modes as the cyclists are entirely exposed to outdoor environment. Understanding the weather-cycling relationship is of great importance to academics and practitioners for cycling activity analysis and promotion. This study contributes to an in-depth understanding of how the changes in weather conditions affect cycling on an off-road trail and an on-road (bridge) bike lane at both daily and hourly scales across four seasons. The paper compares the weather-cycling relationship based on day of week and time of day combinations. The autocorrelation effect of cycling itself and the lagging effect of weather elements are also examined. The findings indicate that cycling is significantly self-dependent especially at the finer temporal scales. Weather have a very different influence on bicycle usage of off-road trails versus on-road bike lanes. When it rains its negative impact not only continues but also significantly affects the cycling within previous one hour. At the daily level, weekend cycling on the trail is less likely to be affected by weather as compared to cycling on the bike lane, whilst inverse is true for weekday cycling. Cycling is most likely to be affected by weather conditions in spring and least likely to be affected in winter. Cycling pattern which is more unrelated to weather at the aggregated level tends to be more flexibly adjusted according to the real-time weather conditions at the disaggregated level. Cyclists on weekends especially during the weekend peak hours (11 AM–4 PM) tend to have more flexibility to adjust their cycling schedule before or after the adverse weather conditions than on weekdays. In addition, cyclists with utilitarian purposes are more likely to shift from cycling to other modes (e.g., transit) due to real-time bad weather conditions in weekdays than in weekends, especially during weekday peak hours (7–9 AM and 4–6 PM). The results provide weather officials, transport agencies and research institutions with valuable information for cycling activity analysis and promotion by considering the effects of weather events especially rainfall.

Energy Control Strategy for Large-Scale Grid Connected PV System with Batteries

The biggest challenge of solar energy is the intermittency of energy produced, since it depends on the availability of sun and real-time weather conditions. This intermittency is a concern for large-scale grid connected photovoltaic (PV) system to provide stable and reliable energy production. Battery energy storage system (BESS) can support to solve this intermittency by producing stable energy and provide smooth generation. Integrating the BESS with PV system in large-scale grid will control the energy flow from PV system to make sure to produce stable energy and balance between the demand and generation. This paper investigates an energy management mechanism for large-scale grid connected photovoltaic (PV) systems with batteries. The proposed system consists of a 1 MW PV system and 1 MW lithium-ion batteries connected to the grid by DC-DC converters and DC-AC inverters. MATLAB-SIMULINK is used to simulate the PV-batteries system under different circumstances of various irradiance and temperature levels. The proposed energy management strategy is verified by the results of simulation.

A Vision for Incorporating Environmental Effects into Nitrogen Management Decision Support Tools for U.S. Maize Production.

Meeting crop nitrogen (N) demand while minimizing N losses to the environment has proven difficult despite significant field research and modeling efforts. To improve N management, several real-time N management tools have been developed with a primary focus on enhancing crop production. However, no coordinated effort exists to simultaneously address sustainability concerns related to N losses at field- and regional-scales. In this perspective, we highlight the opportunity for incorporating environmental effects into N management decision support tools for United States maize production systems by integrating publicly available crop models with grower-entered management information and gridded soil and climate data in a geospatial framework specifically designed to quantify environmental and crop production tradeoffs. To facilitate advances in this area, we assess the capability of existing crop models to provide in-season N recommendations while estimating N leaching and nitrous oxide emissions, discuss several considerations for initial framework development, and highlight important challenges related to improving the accuracy of crop model predictions. Such a framework would benefit the development of regional sustainable intensification strategies by enabling the identification of N loss hotspots which could be used to implement spatially explicit mitigation efforts in relation to current environmental quality goals and real-time weather conditions. Nevertheless, we argue that this long-term vision can only be realized by leveraging a variety of existing research efforts to overcome challenges related to improving model structure, accessing field data to enhance model performance, and addressing the numerous social difficulties in delivery and adoption of such tool by stakeholders.

Implementation of Agrometeorological Early Warning System for Weather Risk Management in South Korea

The purpose of the farmstead-specific early warning service system for weather risk management is to develop custom-made risk management recommendations for individual farms threatened by climate change and its variability. This system quantifies weather conditions into a “weather risk index” that is customized to crop and its growth stage. When the risk reaches the stage where it can cause any damage to the crops, the system is activated and the corresponding warning messages are delivered to the farmer’s mobile phone. The messages are sent with proper recommendations that farmers can utilize to protect their crops against potential damage. Currently, the technology necessary to make the warning system more practical has been developed, including technology for forecasting real-time weather conditions, scaling down of weather data to the individual farm level and risk assessments of specific crops. Furthermore, the scientific know-how has already been integrated into a web-based warning system (http://new.agmet.kr). The system is provided to volunteer farmers with direct, one-on-one weather data and disaster warnings along with relevant recommendations. In 2016, an operational system was established in a rural catchment (1,500 k㎡) in the Seomjin river basin.

A middleware platform for the validation and utilization of short-term weather forecast data for office buildings

Simulations are widely used to calculate the thermal environment and energy consumption of buildings. The results of these simulations are affected by weather data, thus making the selection of appropriate weather data essential. Typical weather data are used to analyze a building’s thermal performance, but this is not appropriate for analyzing the performance of an actual building controlled by a building energy management system, which responds to real-time weather conditions. Such a building needs short-term weather forecast data to calculate the energy consumption. However, an evaluation of the validity of weather forecast data has not been performed. This study quantitatively analyzes the validation of real-time weather forecast data. A middleware platform was developed to combine weather forecast data and the EnergyPlus software using Grasshopper software. In addition, weather forecast data and actual weather data were compared to evaluate the forecast validation and a predictive control method using weather forecast data was devised. The application of real-time control was found to reduce the electricity costs incurred for cooling by 10% relative to there being no control, and by 2% relative to fixed temperature control.

Single-vehicle crashes along rural mountainous highways in Malaysia: An application of random parameters negative binomial model

Mountainous highways generally associate with complex driving environment because of constrained road geometries, limited cross-section elements, inappropriate roadside features, and adverse weather conditions. As a result, single-vehicle (SV) crashes are overrepresented along mountainous roads, particularly in developing countries, but little attention is known about the roadway geometric, traffic and weather factors contributing to these SV crashes. As such, the main objective of the present study is to investigate SV crashes using detailed data obtained from a rigorous site survey and existing databases. The final dataset included a total of 56 variables representing road geometries including horizontal and vertical alignment, traffic characteristics, real-time weather condition, cross-sectional elements, roadside features, and spatial characteristics. To account for structured heterogeneities resulting from multiple observations within a site and other unobserved heterogeneities, the study applied a random parameters negative binomial model. Results suggest that rainfall during the crash is positively associated with SV crashes, but real-time visibility is negatively associated. The presence of a road shoulder, particularly a bitumen shoulder or wider shoulders, along mountainous highways is associated with less SV crashes. While speeding along downgrade slopes increases the likelihood of SV crashes, proper delineation decreases the likelihood. Findings of this study have significant implications for designing safer highways in mountainous areas, particularly in the context of a developing country.

Dynamic rating of overhead transmission lines over complex terrain using a large-eddy simulation paradigm

Dynamic Line Rating (DLR) enables rating of power line conductors using real-time weather conditions. Conductors are typically operated based on a conservative static rating that assumes worst case weather conditions to avoid line sagging to unsafe levels. Static ratings can cause unnecessary congestion on transmission lines. To address this potential issue, a simulation-based dynamic line rating approach is applied to an area with moderately complex terrain. A micro-scale wind solver — accelerated on multiple graphics processing units (GPUs) — is deployed to compute wind speed and direction in the vicinity of powerlines. The wind solver adopts the large-eddy simulation technique and the immersed boundary method with fine spatial resolutions to improve the accuracy of wind field predictions. Statistical analysis of simulated winds compare favorably against wind data collected at multiple weather stations across the testbed area. The simulation data is then used to compute excess transmission capacity that may not be utilized because of a static rating practice. Our results show that the present multi-GPU accelerated simulation-based approach — supported with transient calculation of conductor temperature with high-order schemes — could be used as a non-intrusive smart-grid technology to increase transmission capacity on existing lines.

Autonomous Mission Replanning Method for Imaging Satellites Considering Real-Time Weather Conditions

During the observations made by imaging satellites, meteorological factors are likely to change frequently. The vagaries of weather conditions and significant effects on the actual observation results mean that there is an urgent need to apply more intelligence to satellite mission planning. Thus, this paper describes an autonomous replanning method for imaging satellites that considers the real-time weather conditions. Considering the characteristics of different input data, this method replans the low-yield task set and fine-tunes others to improve profitability. Moreover, the proposed method can heuristically select the appropriate adjustment rule to achieve autonomous satellite mission planning. A series of simulations with various task quantities and in different environments shows that the proposed method can respond effectively to real-time weather changes, and can steadily improve the total profits in a variety of weather conditions during Earth observation activities.

Unlocking the benefits of real‐time thermal ratings through probabilistic power network planning

Real-time thermal ratings (RTTRs) is an emerging technique used to calculate the rating of electrical conductors based on local, real-time weather conditions; this leads to an increased rating with respect to conventional approaches the majority of the time, and can be used to increase the energy yield of distributed generators, support the network during outages and defer network reinforcement. Unfortunately it is not presently recognised in network planning, design and security of supply standards. This represents a barrier to utilising RTTRs in power networks, and must be addressed. This study presents a new, probabilistic method for accounting for the variable ratings during network planning. This is coupled with an analysis of the risk of being unable to supply customers in a network adopting variable ratings, compared with the risk in the same network using conventional ratings; hence the method proposed in this study allows additional load to be connected to the network at a quantified level of risk. Finally, this method could be applied to other emerging network technologies and techniques such as demand side management or energy storage.