Varying Weather Conditions Research Articles

Feasibility of Automated Black Ice Segmentation in Various Climate Conditions Using Deep Learning

Black ice has recently been identified as a major cause of transportation accidents due to detecting difficulties on the road surface. It is crucial to provide traffic users with black ice warnings beforehand to sustain commuting safety. The identification of black ice, however, is a difficult initiative, since it necessitates the installation of sophisticated monitoring stations and demands frequently manual inspection. In order to build an economical automatic black ice detection technique, the datasets are built upon a variety of weather conditions, including clear, snowy, rainy, and foggy conditions, as well as two distinct forms of pavement: asphalt and concrete pavement. The Mask R-CNN model was performed to construct the black ice detection via image segmentation. The deep learning architecture was constructed based on pre-trained convolutional neural network models (ResNetV2) for black ice detection purposes. Different pretrained models and architecture (Yolov4) were then compared to determine which is superior for image segmentation of black ice. Afterward, through the retrieved bounding box data, the degree of danger area is determined based on the number of segmentation pixels. In general, the training results confirm the feasibility of the black ice detection method via the deep learning technique. Within “Clear” weather conditions, the detecting precision can be achieved up to 92.5%. The results also show that the increase in the number of weather types leads to a noticeable reduction in the training precision. Overall, the proposed image segmentation method is capable of real-time detection and can caution commuters of black ice in advance.

The Influence of Varying Atmospheric and Space Weather Conditions on the Accuracy of Position Determination.

Today's technological developments make it possible to use machines to perform specific tasks instead of humans. However, the challenge for such autonomous devices is to precisely move and navigate in constantly changing external environments. In this paper, the influence of varying weather conditions (air temperature, humidity, wind speed, atmospheric pressure, type of satellite systems used/satellites visible, and solar activity) on the accuracy of position determination was analyzed. To reach the receiver, a satellite signal must travel a great distance and pass through all layers of the Earth's atmosphere, the variability of which causes errors and delays. Moreover, the weather conditions for receiving data from satellites are not always favorable. In order to investigate the impact of delays and errors on position determination, the measurements of the satellite signal were conducted, the motion trajectories were determined, and the standard deviations of these trajectories were compared. The results obtained show that it is possible to achieve high precision in determining the position, but varying conditions, such as solar flares or satellites' visibility, meant that not all measurements are able to achieve the required accuracy. The use of the absolute method of satellite signal measurements contributed to this to a large extent. To increase the accuracy of positioning by GNSS systems, it is first of all proposed to use a dual-frequency receiver that eliminates ionospheric refractions.

The influence of inclement weather on electric bus efficiency: Evidence from a developed European network

The daily operational capacity of an electric bus service is affected by many real-world factors, such as variations in weather, route characteristics, and traffic. Before bus operators electrify their fleets, they need to understand the effect inclement weather has on service stability, efficiency, and feasibility. Previous research on this topic is limited, developing models which are difficult to interpret, or physics-based theoretical models, which do not use real-world data and scenarios. To fill this research gap, this research applies a series of ordinary least squares (OLS) regressions to data from an established European electric bus operator to estimate the impact variation in weather conditions has on energy consumption, energy regeneration rates, charge cycles, speed of service, and vehicle emissions. The results show that higher wind speeds and lower temperatures positively correlate with energy consumption and negatively correlate with the total energy regeneration rate. This effect is especially pronounced at freezing temperatures.

Seismoacoustic Explosion Yield and Depth Estimation: Insights from the Large Surface Explosion Coupling Experiment

ABSTRACT The Large Surface Explosion Coupling Experiment (LSECE) is a chemical explosion experiment conducted in Yucca Flat at the Nevada National Security Site in 2020. The experiment included two surface detonations of ∼1000 kg trinitrotoluene equivalent. The main goal of this experiment was to provide the ground-truth data for seismoacoustic wave excitation by large chemical explosions near the ground surface. The seismic and acoustic energy partitioning between the surface is strongly governed by the depth or height of explosions, and either seismic or acoustic-only analysis may have inherent ambiguity in determining explosion yield and depth simultaneously. Previous studies suggested that joint seismoacoustic analysis can resolve the trade-off and reduce the uncertainty of yield and depth estimation dramatically. We demonstrate the capability of seismoacoustic analysis to improve the accuracy of explosion yield and depth estimation with the LSECE data. Local acoustic wave propagation in the atmosphere can be substantially affected by constantly varying weather conditions. Consisting of two detonations before dawn and in the afternoon, LSECE provides unique data to evaluate the model accuracy of acoustic wave propagation and seismoacoustic energy partitioning depending on local atmospheric conditions. We quantitatively evaluate the accuracy of yield and depth estimation depending on atmospheric variability and the improvement achieved by the joint seismoacoustic approach.

Exploiting Multi-Modal Fusion for Urban Autonomous Driving Using Latent Deep Reinforcement Learning

Human driving decisions are the leading cause of road fatalities. Autonomous driving naturally eliminates such incompetent decisions and thus can improve traffic safety and efficiency. Deep reinforcement learning (DRL) has shown great potential in learning complex tasks. Recently, researchers investigated various DRL-based approaches for autonomous driving. However, exploiting multi-modal fusion to generate perception and motion prediction and then leveraging these predictions to train a latent DRL has not been targeted yet. To that end, we propose enhancing urban autonomous driving using multi-modal fusion with latent DRL. A single LIDAR sensor is used to extract bird's-eye view (BEV), range view (RV), and residual input images. These images are passed into LiCaNext, a real-time multi-modal fusion network, to produce accurate joint perception and motion prediction. Next, predictions are fed with another simple BEV image into the latent DRL to learn a complex end-to-end driving policy ensuring safety, efficiency, and comfort. A sequential latent model is deployed to learn more compact representations from inputs, leading to improved sampling efficiency for reinforcement learning. Our experiments are simulated on CARLA and evaluated against state-of-the-art DRL models. Results manifest that our method learns a better driving policy that outperforms other prevailing models. Further experiments are conducted to reveal the effectiveness of our proposed approach under different environments and varying weather conditions.

No-till imparts yield stability and greater cumulative yield under variable weather conditions in the southeastern USA piedmont

No-till imparts yield stability and greater cumulative yield under variable weather conditions in the southeastern USA piedmont

Atmospheric Boundary Layer Height: Inter-Comparison of Different Estimation Approaches Using the Raman Lidar as Benchmark

This work stems from the idea of improving the capability to measure the atmospheric boundary layer height (ABLH) in variable or unstable weather conditions or in the presence of turbulence and precipitation events. A new approach based on the use of rotational and roto-vibrational Raman lidar signals is considered and tested. The traditional gradient approach based on the elastic signals at wavelength 532 nm is also considered. Lidar data collected by the University of Basilicata Raman lidar (BASIL) within the Special Observation Period 1 (SOP 1) in Cardillargues (Ceveninnes–CV supersite) during the Hydrological Cycle in the Mediterranean Experiment (HyMeX) were used. Our attention was specifically focused on the data collected during the period 16–21 October 2012. ABLH estimates from the Raman lidar were compared against other innovative methods, such as the recently established Morphological Image Processing Approach (MIPA) and the temperature gradient technique applied to potential temperature obtained from radio-sounding data. For each considered methodology, a statistical analysis was carried out. In general, the results from the different methodologies are in good agreement. Some deviations have been observed in correspondence with quite unstable weather conditions.

Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System

The study presents an optimal control approach for managing a hybrid Photovoltaic/Wind Turbine/Battery system in an isolated area. The system includes multiple energy sources connected to a DC bus through DC/DC converters for maximum power point tracking. The proposed hybrid MPPT approach (HMPPT) manages the energy production from different sources, while the power flow method is used to balance the load and renewable power. The study shows that integrating the HMPPT algorithm and power flow approach results in improved system performance, including increased power generation and reduced stress on the batteries. The study also proposes an accurate sizing method to further improve system efficiency. The study demonstrates the effectiveness of the proposed approach by presenting results for twelve different days with varying weather conditions. The results show that the proposed approach effectively manages the energy production and load, resulting in optimal system performance. This study provides valuable insights into the optimal control of hybrid renewable energy systems, and highlights the importance of considering different energy sources and optimal sizing for maximizing system efficiency.

A Numerical-Hierarchical Framework for Predicting Volume Changes in Expansive Soils under Variable Surface and Weather Conditions

This research developed a numerical-hierarchical framework that captured surface conditions and climate parameters. Volume changes under distinct scenarios of surface boundary, antecedent moisture, and meteorological parameters were predicted using a coupled seepage-deformation model. Risk was hierarchically based on expert judgment for surface scenarios (Stage-I indices) and normal distribution for antecedent moisture and atmospheric parameters scenarios (Stage-II indices). Results indicated seasonal volumetric changes with minor variations of −5 mm from January to April, a steady settlement of −17 mm by June, and a gradual heave of +8 mm by December. All Stage-I indices showed similar trends such that the fluctuations were highest for vegetation, followed by slope, then by cover, and lowest for loading. Volume changes gradually reduced with depth and diminished at 3.1 m. Similar seasonal and profile trends were generally found for most Stage-II indices. Nonetheless, different trends under wet and dry conditions were observed for initial water content, precipitation, and air temperature. For the datum scenario, risk was non-existent till February, increased to 2.3 by June, diminished by October, and rose back to 1.0 by December. Similar values of cyclic variations in risk were found in most urban facilities. Volume changes were found to be two times higher in parks, insignificant for roads, half for five story buildings, and one-fourth for pipes under roads. Among the Stage-II indices, risk for the initial water content inhibited seasonal variations whereas that for precipitation was about half with a wider distribution; all the other indices showed about one-third the values. Under a higher occurrence probability of 0.129, a magnified risk was observed for all the indices such that the most critical were the initial water content and precipitation.

Responses in honeybee and bumblebee activity to changes in weather conditions

Insect pollination, and in particular pollination by bees, is a highly valued ecosystem service that ensures plant reproduction and the production of high-quality crops. Bee activity is known to be influenced by the weather, and as the global climate continues to change, the flying frequency and foraging behaviour of bees may also change. To maximise the benefits of pollination in a changing world, we must first understand how current weather conditions influence the activity of different bee species. This is of particular interest in a country such as Ireland where inclement weather conditions are nominally sub-optimal for foraging. We observed honeybee (Apis mellifera) and buff-tailed bumblebee (Bombus terrestris) activity across a variety of weather conditions at seven apple orchards to determine how four weather variables (temperature, relative humidity, solar radiation, wind) influenced the flight activity of each species. Each orchard contained three honeybee and three bumblebee colonies, and so we were able to observe a colony of each species concurrently in the same weather conditions. Overall, honeybees were more sensitive to changes in weather than bumblebees and could be more predisposed to future changes in within-day weather conditions. Our results indicate bumblebees could compensate for low honeybee activity in inclement conditions, which supports the theory that pollinator diversity provides resilience. This may be particularly important in management of pollinators in crops that flower in the spring when weather is more variable, and to allow varied responses to global climate change.

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.

Real-time energy optimization and scheduling of buildings integrated with renewable microgrid

Real-time energy optimization is essential for effective load scheduling, cost reduction, maintaining demand and supply balance, and ensuring reliable power system operations. However, real-time energy optimization is challenging due to the unpredictable nature of renewable energy sources (RES) and the behavior of electric loads. On this note, a rigid model is required that can deal with this dilemma. Thus, the Lyapunov optimization technique (LOT) emerged as a solution for the real-time energy optimization problem. This work investigates a smart home equipped with inflexible loads (TV, computer, light, etc.), flexible loads (EVs, HVAC, water heaters, etc.), and RES (photovoltaic and wind energy) in a grid-connected mode that ensures energy trading (purchasing and selling of energy). The aim is to optimize total cost, thermal discomfort cost, and batteries and EVs charging/discharging using LOT by real-time energy optimization, which does not require any system parameters to be anticipated. The proposed algorithm employs LOT for four queues to solve the real-time energy optimization problem. Simulations are conducted for different scenarios and varying weather conditions to endorse the effectiveness of the developed real-time energy optimization solution in various aspects of the performance metrics.

Modelling and Simulation of the Hybrid System PV-Wind

In this paper, we focused on modeling and simulation of a hybrid solar-wind energy system, consisting of a photovoltaic cell and a wind turbine driven by a Permanent Magnet Synchronous Generator (PMSG). The proposed system gives details of the hybrid solar-wind system. In the PV subsystem, there will be a photovoltaic energy subsystem, MPPT controller, and a DC-DC boost converter. The wind turbine subsystem includes a wind turbine energy subsystem, PMSG, and MPPT controller. The MPPT controllers were designed to extract the maximum power regardless of the weather conditions and temperatures. The P and O algorithm is introduced in the MPPT used in the PV energy subsystem and on the control of the machine side converter (MSC). The proposed wind turbine energy subsystem is based on Direct Torque and Flux Control (DTFC). The simulated results are demonstrated to show that the hybrid solar-wind generating system can realize a maximum power point tracking control of wind and solar power to satisfy the energy demand over an extensive range under unpredictable atmospheric conditions. The system has been demonstrated by MATLAB/Simulink and tested for variable weather conditions and temperatures.

Deep Learning Architecture for UAV Traffic-Density Prediction

The research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average.

A Lightweight CNN for Wind Turbine Blade Defect Detection Based on Spectrograms

Since wind turbines are exposed to harsh working environments and variable weather conditions, wind turbine blade condition monitoring is critical to prevent unscheduled downtime and loss. Realizing that common convolutional neural networks are difficult to use in embedded devices, a lightweight convolutional neural network for wind turbine blades (WTBMobileNet) based on spectrograms is proposed, reducing computation and size with a high accuracy. Compared to baseline models, WTBMobileNet without data augmentation has an accuracy of 97.05%, a parameter of 0.315 million, and a computation of 0.423 giga floating point operations (GFLOPs), which is 9.4 times smaller and 2.7 times less computation than the best-performing model with only a 1.68% decrease in accuracy. Then, the impact of difference data augmentation is analyzed. The WTBMobileNet with augmentation has an accuracy of 98.1%, and the accuracy of each category is above 95%. Furthermore, the interpretability and transparency of WTBMobileNet are demonstrated through class activation mapping for reliable deployment. Finally, WTBMobileNet is explored in drones image classification and spectrogram object detection, whose accuracy and mAP@[0.5, 0.95] are 89.55% and 70.7%, respectively. This proves that WTBMobileNet not only has a good performance in spectrogram classification, but also has good application potential in drone image classification and spectrogram object detection.

Automatic Vacant Parking Places Management System Using Multicamera Vehicle Detection

A multicamera system for detecting cars and mapping them into parking spaces in a parking lot is described in this study. A limited number of sequences and without more difficult reality conditions (illumination changes and various weather conditions) were used to evaluate approaches from a cutting-edge system that function correctly under controlled conditions. However, the majority of them only provide parts of systems, typically detectors, rather than the entire system. The proposed system was made to work in real-world situations that take into account a variety of occlusion scenarios, changes in lighting, and weather. For the purpose of design and validation, a brand-new multicamera data set was recorded. Two of the system’s object detector results are shown, and several provided postprocessing stages are used to build the system. The findings clearly indicate that the suggested method works appropriately in demanding settings such as near entire occlusions, lighting variations, and varying weather conditions.

A Lyapunov-Based Model Predictive Control Approach for Photovoltaic Microgrid Integration via Multilevel Flying Capacitor Inverter

In this study, we introduce a Model Predictive Control (MPC) approach based on a Lyapunov energy function, ensuring global asymptotic stability for a single-phase multilevel flying capacitor inverter (FCI) that interfaces with photovoltaic systems and microgrids. The defined cost function is derived from the Lyapunov energy function, harnessing the stored energy within the capacitor and inductor. This choice is rooted in the principle that as long as total energy is continuously dissipated, the system’s states will eventually reach the equilibrium point. Moreover, this cost function eliminates the need for tuning the weighting factors, a process that can be cumbersome and time-consuming due to the lack of clear guidelines. To assess the efficacy of the proposed control strategy, we conducted simulations using MATLAB/Simulink under varying weather conditions. The results obtained demonstrate that the MPC strategy not only ensures overall stability but also delivers high-quality sinusoidal current with minimal total harmonic distortion (THD), practical low steady-state error in the grid current, and rapid dynamic response, even in the face of changing weather conditions.

Exploring the performance, simulation, design, and construction of a closed solar swimming pool in Kirkuk city

Indoor swimming pools are sports or entertainment facilities that require substantial energy to heat the pool water and maintain a comfortable atmosphere in compliance with international standards. However, traditional methods of heating swimming pools using fuels or electricity often result in high operational costs and environmental pollution. To address these challenges, solar water heating has emerged as the most significant and environmentally friendly technology. Consequently, the construction of solar-powered swimming pools has become a prominent issue, drawing considerable attention from governments worldwide. Solar energy is currently being utilized in various applications, with water heating in residential settings being one of the most popular ones. Iraq, known for its high solar energy potential, stands to benefit greatly from adopting and designing solar swimming pools. The proposed design incorporates essential components such as the swimming pool, pump, filter, control valves, and the solar collector. This study explores the influence of flow rate on the solar collector's performance and its relationship with pool size under varying weather conditions in Kirkuk city. The month of February, characterized by lower solar radiation intensity and air temperature, was selected for the investigation. This study provides insights into heating indoor swimming pools using solar energy, examining the types of solar collectors, filters, and pumps involved. By offering guidance in the system design process, our research can be instrumental in facilitating the installation of such systems.

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.

Enhancing Energy Reliability and Balance with Fuzzy Logic Controlled Microgrid System

This paper presents a microgrid energy management system that encompasses a combination of solar panels with maximum power point tracking (MPPT), a battery storage unit connected by a bidirectional converter and a wind turbine. The microgrid is designed to maintain a balance between energy supply and demand under varying weather conditions and loads. The system employs a fuzzy logic controller to regulate the power flow between the loads and sources, ensuring reliable and efficient operation. The microgrid is of the DC type, providing power to both DC and AC loads. The proposed system is designed and implemented using Matlab/Simulink and evaluated using simulation studies. The obtained results show the large extent to which the recommended approach ensures a steady energy supply under different load and weather scenarios, indicating that the proposed microgrid energy management system is a viable and efficient solution for small-scale renewable energy systems.