Uncertain Weather Research Articles

Monitoring and Information System for Automatic Clothes Drying Based on the Internet of Things

This research develops an Internet of Things (IoT)-based automatic clothes drying monitoring and information system to overcome the challenges of drying clothes due to uncertain weather changes. The system integrates NodeMCU ESP8266 as the main controller with various sensors such as rain, light, temperature, and humidity sensors to detect environmental conditions. The servo motor is used as a driving force to move the clothesline. The smartphone-based user interface was developed using the MIT App Inventor to enable remote monitoring and control. The test results showed that the system successfully detected weather changes and responded automatically, providing real-time information to users. Implementing IoT technology in this system allows for more efficient and practical management of clotheslines, overcoming the limitations of conventional drying methods.

Intelligent system based on a satellite image detection algorithm and a fuzzy model for evaluating sugarcane crop quality by predicting uncertain climatic parameters

The increase in uncertain weather affects agriculture, impacting crop yield and quality, mainly due to the interaction of climatic variables such as temperature, wind speed, and humidity. In addition, soil erosion and nutrient loss are regional problems aggravated by inadequate agricultural practices in developing sugarcane agriculture. The present research proposes an Intelligent System based on a detection algorithm and a fuzzy model to estimate the quality of the sugarcane crop and the probability of the presence of pests and diseases through the prediction of uncertain variables. Wind speed, cloudiness, humidity, and thermal amplitude were considered variables of interest because parameters out of control of these variables generate a state of thermal stress, triggering pests and diseases that reduce crop quality and sugar production. This research uses geospatial information to simplify the exchange of information through a detection algorithm using real-time satellite images and a fuzzy model to estimate crop quality and prevent climate change-related problems. The variables humidity and cloudiness determine sugarcane quality as they are related to crop phenology and the probability that the crop will develop pests and diseases. In contrast, the intelligent system showed a correlation of over 93% for predicting the variables of interest.

Examining Village Financial Management through the Lens of Village Officials' Perspective: Case in Penajam Paser Utara

Purpose: To analyze the extent of management and its implementation in the field of Village financial management in realizing improved services to the community.Design/Methodology/Approach: This type of research is descriptive qualitative. The number of informants in this research was nine people. By using data collection techniques interviews, observation, documentation.Findings: The research findings indicate that village financial management, as perceived by Sidorejo village officials in Penajam sub-district, Penajam Paser Utara district, exhibit well-planned initiatives and effective role fulfillment by all stakeholders. However, challenges persist primarily during the APBDes phase, where delays occur awaiting ADD priority ceiling and regulatory approvals such as PMK DD, and sub-district evaluation results. While implementation progresses smoothly overall, hurdles include slow approval of District APBD impacting village activities, regulatory delays, and uncertain weather conditions affecting project timelines. Additionally, budget discrepancies between field prices and RAB budgets pose challenges. Despite these obstacles, administrative processes function adequately, with active engagement from administrative heads who demonstrate a good understanding of their duties. Reporting mechanisms are robust, featuring progress reports and accountability measures such as APBDes implementation reporting

A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation

Electricity is one of the most important energy sources for the progress of human society. Photovoltaic power generation (PPG) has great potential as a renewable energy source to support sustainable development. However, the unpredictability of photovoltaics because of the uncertain weather conditions, time lags and the complex interactions among related factors may result in instabilities in PPG, which limits its applications in the electric power system. Therefore, accurately identifying the related factors and representing the interactions are the key issues in predicting PPG. For this purpose, a novel seasonal grey prediction model with time-lag and interactive effects, denoted as SGMLI(1,n), is developed to predict the capacity of photovoltaic power generation. More specifically, a time-lag driving term, a periodic driving term, and an interaction driving term are proposed to represent the lag relationship, periodicity, and the interactive effects between two factors, respectively. Utilizing the least square method and the Whale Optimization Algorithm (WOA), the optimal solution to the nonlinear programming problem is estimated. For elaboration and verification, six grey forecasting models, two statistical techniques, and two machine learning models, are employed for comparison with the SGMLI(1,n) model. Experimental results reveal that the SGMLI(1,n) outperforms benchmark models in forecasting PPG.

Selection of optimal strategy for managing decentralized solar PV systems considering uncertain weather conditions

Solar power is a promising source of energy that is environmentally friendly, sustainable, and renewable. Solar photovoltaic (PV) panels are the most common and mature technology used to harness solar energy. Unfortunately, these panels are prone to dust accumulation, which can have a significant impact on their efficiency. To maintain their effectiveness, solar photovoltaics s must be cleaned regularly. Eight main techniques are used to clean solar panels: natural, manual, mechanical, robotic, drone, coating, electrical, and acoustic. This study aims to identify the best cleaning method using multiple criteria decision-making (MCDM) techniques. Using the Analytical Hierarchy Process (AHP), Quality Function Deployment (QFD), Fuzzy Technique for Order of Preference by Similarities to Ideal Solution (FTOPSIS), and Preference Selection Index (PSI), this research evaluates all eight cleaning methods based on several criteria that are categorized under cost, performance, resource requirement, and safety in Abu Dhabi. The data are collected from surveys completed by experts in solar and sustainable energy. The AHP, QFD, and PSI results identified natural, manual, and surface coating as the best and most effective cleaning methods. Natural cleaning involves using rainwater primarily to remove dirt and dust; manual cleaning requires cleaning agents and wiping clothes; and surface coatings involve applying a layer of hydrophobic material to the panels to repel dust. Identifying the most effective cleaning method for dust removal from solar panels can ensure optimal efficiency recovery at minimal costs and resources.

Resource Allocation in an Uncertain Environment: Application to Snowplowing Operations in Utah

We consider a two-stage planning problem where a fleet of snowplow trucks is divided among a set of independent regions, each of which then designs routes for efficient snow removal. The central authority wishes to allocate trucks to improve service quality across the regions. Stochasticity is introduced by uncertain weather conditions and unforeseen failures of snowplow trucks. We study two versions of this problem. The first aims to minimize the maximum turnaround time (across all regions) that can be sustained with a user-specified probability. The second seeks to minimize the total expected workload that has not been completed within a user-specified time frame. We develop algorithms that solve these problems effectively and demonstrate their practical value through a case application to snowplowing operations in Utah, obtaining solutions that significantly outperform the allocation currently used in practice. Funding: Financial support from the Utah Department of Transportation [Grant 218138]; the National Science Foundation [Grant CMMI-2112758]; and the Mountain-Plains Consortium [Grant 637] is gratefully acknowledged. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0024 .

We need to act now to ensure global food security, and reduce agricultural greenhouse gas emissions

ABSTRACT To feed a rapidly growing human population in an era of climate change and increasingly uncertain weather—such as drought and flood—humanity will need crops with increased yields, greater resiliency to extreme weather, and more resistance to disease. Appropriately enough in the Century of Biology, that means turning to genetic tools such as CRISPR.

Efficiency Of Variety Oven Tobacco Farming 88 In Jerowaru District, East Lombok District

This research can determine the amount of income received by farmers of variety 88 oven tobacco in Jerowaru Village, East Lombok Regency, on average Rp. ,36,257,950,- The amount of income received is proportional to the costs incurred in cultivating oven tobacco variety 88. Farming oven tobacco variety 88 in Jerowaru District, East Lombok Regency, the implementation of farming is relatively efficient, this is indicated by the R/ C Ratio value of 1 .9, in other words R/C > 1. means that cultivating tobacco varieties 88 in Jerowaru District is efficient or feasible to implement or continue. The obstacle faced in cultivating variety 88 oven tobacco is capital, because capital is one of the determining factors for success in carrying out variety 88 oven tobacco farming activities. Next, there is the obstacle of the availability of fertilizer which is relatively high in price, while the final obstacle is the recent uncertain weather factor. Finally, this has become a threat that causes tobacco farming harvests to fail, causing huge losses for tobacco farmers.

Analisis Faktor Yang Mempengaruhi Produktivitas Tenaga Kerja pada Proyek Pembangunan Perumahan Opra City Gresik Jawa Timur

Labor productivity is one of the main elements in determining the success of a construction project. The purpose of this research was to find out what factors influence labor productivity. This research was conducted at the Opra City Gresik Housing Development Project. The method used to find the relationship between productivity and factors that influence productivity is the Productivity Rating method, multiple linear regression analysis method, t test, F test (Anova) and coefficient of determination using statistical software, namely SPSS version 23. There are other analysis methods that are used. used are the validity test, reliability test and normality test used in testing the questionnaire. The level of worker productivity is 81.80%. This can be said to be quite satisfactory, because the Worker Utility Factor is more than 50%. There are seven factors that are most dominant in influencing productivity, namely uncertain weather, lack of material availability, damaged equipment, education level, age, worker motivation and quality of supervision.

The role of battery energy storage in mitigating demand fluctuations of distribution networks caused by uncertain weather conditions using multi-objective Bonobo Optimizer

The role of battery energy storage in mitigating demand fluctuations of distribution networks caused by uncertain weather conditions using multi-objective Bonobo Optimizer

Co-cultivation of sweet basil (Ocimum basilicum L.) with food crops for efficient resource utilization and increasing the farm profits

Ocimum basilicum L., or sweet basil, is a valuable aromatic and medicinal plant that is used in aromatherapy to energize, relax, and stimulate the mind. It is also used in the pharmaceutical industry for its anti-ageing and therapeutic qualities. Farmers who depend only on conventional food crops run the danger of crop failure as a result of unanticipated circumstances like uncertain weather, pest outbreaks, or disease epidemics. In contrast, intercropping offers a kind of insurance against total crop loss by diversifying farm outputs. The present study aims to increase field productivity and efficient use of land resources under sweet basil-based inter-cropping systems. In the experiment, 17 different treatments (12 treatments comprising of inter-cropping of sweet basil (55,555 plants/ha plant population) with four conventional crops namely maize (Zea mays L.), pearlmillet (Pennisetum glaucum L.), pigeonpea (Cajanus cajan L.) and okra (Abelmoschus esculentus L.) at three plant populations, i.e., 100% (55,555 plants/ha), 75% (37,037 plants/ha), and 50% (27,777 plants/ha). Also, all five sole crops (4 conventional crops and sweet basil) were planted. The study findings showed that the maximum fresh herb yield of sole sweet basil culture at 100% plant population (14.2 t/ha with 0.70% oil content and 99.2 kg/ha oil yield) was almost equal to that of co-cultivation of sweet basil with pigeonpea at 50% plant population (14 t/ha with 0.70% oil content and 98.2 kg/ha oil yield). Similarly, LER varied from 1.50 to 1.90 and the ATER varied from 1.32 to 1.82. The inter-cropping system based on sweet basil is an advantageous strategy to maximize resource use efficiency. Nevertheless, net returns of sweet basil were found to be maximum with pigeon pea (1762–1881 USD/ha) followed by okra (1507–1638 USD/ha), maize (1042–1310 USD/ha) and pearl millet (809–1029 USD/ha). Moreover, for better herb yield and net returns, it was more profitable for the farmers to co-cultivate sweet basil with pigeon pea at 50% plant population.

Analisis Risiko Operasional Pada Proses Pembuatan Kerupuk Udang Sanah Kabupaten Tanjung Jabung Barat

The importance of knowledge about operational risks for umkm partners in order to be able to control or handle the risks that must be handled and be able to solve the problems being faced, so this research aims (1) to find out the identification of operational risks in the process of making Sanah shrimp crackers in West Tanjung Jabung district ( 2) to find out the operational risk analysis of the process of making Sanah shrimp crackers in West Tanjung Jabung district, (3) to find out the efforts made by umkm actors to overcome operational risks. This research uses qualitative methods in this research using data sources obtained from the owner of the Sanah shrimp cracker business and the Sanah shrimp cracker employees. The results of this research are: In identifying operational risks, there are four types of risks as follows: human resource risks include risks of events such as unsuccessful steaming, unsuccessful cutting of crackers, long frying of crackers, failed packaging and inadequate training. Technological risks include inappropriate machines and packaging equipment that does not function properly. As well as process risks, there are risks from raw materials that do not match the dosage. And external risks include risks such as unstable raw materials and uncertain weather factors. The risk analysis that occurs in Sanah shrimp crackers has a risk level of high, medium and low. As for the aspects included in the high category, there are three risks, namely inadequate training, inappropriate packaging equipment and raw materials that do not match the measurements. And there are also medium level operational risks including unsuccessful steaming, frying for too long, inappropriate machines, unstable raw materials, weather factors that result in long drying. And there are also two levels of low risk, namely cutting crackers, failed packaging. Efforts to overcome operational risks that occur in the Sanah shrimp cracker business include requiring or providing training to employees about managing shrimp crackers and employees also need to have self-awareness to be more skilled and more thorough.

A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests

Regional forecasting is crucial for a balanced energy delivery system and for achieving the global transition to clean energy. However, regional wind forecasting is challenging due to uncertain weather prediction and its high dimensional nature. Most solutions are limited to single-turbine or farm/park forecasting; therefore, this work proposes a day-ahead regional wind power forecasting framework using deep Convolutional Neural Networks (CNN) with context-aware turbine maps and Conformal Quantile Regression (CQR) to generate quantile forecasts with valid coverage.Additionally, this work introduces the use of the Split Conformal Predictive System (SCPS) to generate valid prediction distributions, which has not yet been proposed for wind power forecasting in general. As well as a new method to generate calibrated prediction distributions based on SCPS and Quantile Regression Forests (QRF). This new method, named Split Conformal Distribution Regression Forests (SCDRF), allows for conditional conformal predictive distribution that increases efficiency compared to SCPS while maintaining valid coverage. SCDRF, together with CNNs and context-aware turbine maps, outperforms the existing models on the evaluated dataset, reducing the pinball loss by 5.89% while having more flexibility due to the generation of prediction distributions that can be used to generate any quantile prediction without retraining the model.

RSSI Measurement Analysis of Zigbee-Based Wireless Sensor Networks in Various Topologies for Solar Panel Monitoring

Abstract— Solar panels require performance monitoring when energy shortages occur in the area. Monitoring solar panels requires a detection device to detect solar panels, including the ACS712 current sensor for monitoring the output current of the solar panel, a DC voltage sensor for monitoring battery performance, and a DHT11 temperature sensor for monitoring the temperature of the solar panel storage battery. The WSN technology that can work is ZigBee communication. This ZigBee communication research uses various topology methods, including star, tree, and mesh topologies. Solar panel monitoring tests are carried out indoors and outdoors. The ZigBee communication device used is the XBee S2C device, which operates at a frequency of 2.4 GHz. From a comparison of various topologies, the mesh topology is a topology with good performance because it has the characteristic of choosing a router with the fastest path to reach the coordinator. It is proven by monitoring solar panels with an optimal distance of 80 m, which is carried out from outdoors with an RSSI value of -84 dBm, a throughput of 0.55 kbps, and a delay of 2.77 ms, producing power for 150 minutes with an average current of 0.14 A, voltage of 9.86 V, and temperature of 31.48°C. The number of disturbances and weather conditions greatly affect signal propagation, which can hamper the data transmission process. Throughput fluctuates due to uncertain weather conditions, and delay fluctuates due to a lot of interference from other radio signals.
 Keyworrds— Wireless Sensor Nirbael (WSN), RSSI, ZigBee, Panel Surya, Thinger.IO

A cohesive structure of Bi-directional long-short-term memory (BiLSTM) -GRU for predicting hourly solar radiation

Uncertain weather scenarios have an impact on the output of solar farms and therefore affect the security of the grid. It is advantageous for power system operators to forecast solar energy to balance the load generation and for optimal power scheduling. The most promising deep-learning techniques to combine weather variables with precise measurements of solar irradiance are not widely discussed. To close this research gap and produce better prediction results, this article aims to formulate and compare two distinctive deep learning algorithms for using time series forecasting approaches to predict solar irradiance. For multivariate data, the forecasting technique Bi-Directional Long Short-Term Memory (BiLSTM), and BiLSTM-GRU (Gated Recurrent Unit) Dropout, are examined in this study. The output results from the proposed model are compared with other benchmark models based on performance error measurements, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Mean Absolute Percentage Error (MAPE). It was found that the proposed hybrid method, BiLSTM-GRU with dropout outperformed the other methods in terms of solar irradiance predicting accuracy. The analysis presented the best RMSE of 1.55 and MAE of 1.13 for BiLSTM and RMSE of 1.40 and MAE of 0.91 for BiLSTM-GRU architecture using hyperparameter tuning. The comparison results show that the prediction accuracy is improved by tuning the hyperparameters.

Designing and constructing of a laboratory scale concrete roof tile dryer

There are some problems with the drying process in the concrete tiles local industry, especially due to uncertain weather, where the drying process relies on sunlight. In addition, roof tile dryers have not yet hit the market. A laboratory-scale dryer is needed to develop a full-scale concrete roof tile dryer. This research aims to design and manufacture a laboratory-scale concrete tile dryer. The dryer will be used to investigate the characteristics of the concrete roof drying. The design was carried out using the Pahl and Beitz method, in three stages, namely planning, conceptual design, and detailed design. In the planning stage, a requirement list was stated, in the conceptual design stage, selected tools are determined for every sub-function, while in the detail design stage, shop drawings were produced. To produce a better distribution of air velocity and temperature, at this detailed design stage, a Solidworks CFD numerical analysis was used. The dimension of the drier is 1600 x 1100 x 1100 mm³, with a capacity of 60 - 180 kg, with air velocity of 3 m/s and variable temperature. The drying chamber consists of 3 layers. The heat source is LPG flame and electric heater. The fresh air is flown to the LPG burner, through a circulating fan into the drying chamber, where the concrete roof tiles are situated. The roof tile dryer was constructed and tested. The test results show that the air velocity at the top layer accorded with the CFD result, while in the second layer and third layer of the drying chamber, the air velocity was relatively small. The temperature in each layer of the drying chamber shows the same value, in this case, the temperature for each layer is 38°C, while the set point at the thermostat was 40°C. The relative humidity of the drying air was 21.7% while the ambient air relative humidity was 52%.

Posture analysis of the workers of railway maintenance unit

Maintenance of railways is done periodically by the railway maintenance unit. The work activities executed in uncertain weather and with the use of relatively heavy equipments are done in a quick manner. Therefore, work posture examination is required using Rapid Entire Body Assessment (REBA) and Nordic Body Map (NBM) methods. This study aimed to find out the work posture of the workers in the railway maintenance unit of Resort IV.6 Ketapang, North Lampung. The study was qualitative in nature using a case study approach. The data were collected through interviews and observation using Nordic Body Map (NBM) forms. Data collection from informants was conducted using purposive sampling with 1 key informant and 4 triangular informants, with the characteristics of already working for more than 3 years and also being the workers in the railway raising projects. The results of the study showed that, based on REBA calculation, the score obtained upon the observation of the informants’ working posture was 10, which was categorized as high. As such, immediate identification is required regarding the informants’ working posture. Based on the results of the study, it is concluded that working postures of the workers in the railway maintenance unit have effects on the pain in the muscle parts of the workers. Therefore, Improvement on working postures should be made immediately. In addition, during their work using working equipments, they should do short stretching to reduce the pain complained.

Green Vessel Scheduling with Weather Impact and Emission Control Area Consideration

Emissions of maritime transport have been a critical research topic with the substantial growth in the global shipping industry, encompassing both the expansion of the world fleet and the increased distances it has been covering recently. The International Maritime Organization (IMO) has enforced some regulations to mitigate ship Greenhouse Gas (GHG) emissions, which affect vessels’ operational practice, and further affect service reliability. In this paper, some compliance methods (two-speed strategy, fuel switching, and LNG) against Emission Control Areas (ECAs) at the operational level are examined regarding if and how they impact the liner shipping schedule and service reliability; meanwhile, uncertain weather conditions and port times, as the main uncertain factors, are also involved. Then, a bi-objective fuzzy programming model is formulated and solved by the augmented ε-constraint approach, which generates a set of Pareto solutions by balancing the economic and environmental sustainability. Some findings can be concluded through the experimental results, including that, firstly, to meet uncertain weather conditions at sea requires strong robustness; secondly, ECA regulations can negatively affect the liner shipping service level; moreover, slow steaming is an immediate and effective measure to reduce GHG emissions; and, furthermore, ship routing choice could have a significant influence on ship emissions and service reliability.

Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning

Unmanned Aerial Vehicles (UAVs), i.e., drones, are expected to be widely used in various applications, such as parcel delivery and passenger transport, with the benefits of mitigating traffic congestion and reducing carbon emissions. In this paper, we study a UAV path planning problem under uncertain weather conditions, and design a data-driven dynamic decision support system for multiple types of UAVs. To this end, we categorize all relevant costs into three types, namely, economic, environmental, and social costs, and formulate a nonlinear two-stage stochastic programming model to establish optimal paths for UAV missions under weather uncertainty. We then discretize the nonlinear model and propose a tight linear approximation for the discretized problem to allow for a near real-time implementation. To quantify weather uncertainty, we propose a weather scenario generation algorithm to map ensemble-based weather forecast information to airspace blockage maps. With comprehensive computational studies through simulations, we show that our proposed stochastic approach can lower operating costs by an average of around 6%, where the savings increase as weather conditions become more severe and complex. We also find that, for missions operated by small UAVs, it is not sufficient to determine a path solely based on economic cost minimization, but it should rather be through total cost minimization, which involves environmental and social costs. Considering only the economic cost in the optimization may lead to much higher non-economic costs. However, for missions operated by large UAVs, it is sufficient to determine paths through economic cost optimization, as including environmental and social costs in the optimization process does not result in solutions that are much different from those obtained by considering only the economic costs. For both small and large UAVs, a path established solely through environmental or social cost minimization may not be economically sustainable, as doing so would imply very high economic costs.

A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors

The rise in Arctic temperatures has caused sea ice to melt, making the region more navigable through a new, shorter shipping route. Navigating the Arctic poses high risks due to extreme environmental conditions. This study proposes scalable data-driven predictive models to assess the risk of Arctic navigation under uncertain weather and sea ice conditions. Machine learning is applied to predict the type of Arctic incidents and identify their risk factors. Several models are investigated, and the most accurate model for the two Arctic routes, the North West Passage (NWP) and the Northern Sea Route (NSR), is determined based on several validation metrics. Random forest and Naïve Bayes models provide the best accuracy and F1-score for NWP and NSR, respectively. The wind speed, vessel type, length, and age are important risk factors for NWP while temperature at 2 m above the surface, vessel length, and age are important for NSR. Partial dependence plots are used to investigate the effect of each feature on predicting each incident type. Equipment failures are more common among newer and longer vessels. Collision related incidents are more likely to be predicted for longer vessels, while grounding related incidents are more frequent at higher air temperature.