Weather Data Research Articles

Monitoring anomalies on large-scale energy and water balance components by coupling remote sensing parameters and gridded weather data.

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied with MODIS images and gridded weather data from 2007 to 2021, to monitor the energy balance components and their anomalies, in the Atlantic Forest (AF) and Caatinga (CT) biomes inside the coastal agricultural growing zone, Northeast Brazil. Considering the long-term data, the Rn values between the biomes are not significantly different, however presenting distinct Rn partitions into latent (λE), sensible (H), and ground (G) heat fluxes between biomes. The Rn values annual averages are 9.40 ± 0.21 and 9.50 ± 0.23MJm-2 d-1, for AF and CT, respectively. However, for respectively AF and CT, they are respectively 5.10 ± 1.14MJm-2 d-1 and 4.00 ± 0.99MJm-2 d-1 for λE; 3.80 ± 1.12MJm-2 d-1 and 5.00 ± 1.00MJm-2 d-1 for H; 0.50 ± 0.12MJm-2 d-1 and 0.40 ± 0.10MJm-2 d-1 for G, yielding respective mean evaporative fraction (Ef = λE/(Rn - G) values of 0.60 ± 0.12 and 0.50 ± 0.15. Anomalies on λE, H, and Ef were detected through standardized index for these energy balance components by comparing the results for the years 2018 to 2021 with the long-term values from 2007 to each of these years, showing that the energy fluxes between surfaces and the lower atmosphere, and then the root-zone moisture conditions for both biomes, may strongly vary along seasons and years, with alternate positive and negative anomalies. These assessments are important for water policies as they can picture suitable periods and places for rainfed agriculture as well as the irrigation needs in irrigated agriculture, allowing rational agricultural environmental management while minimizing water competitions among other water users, under climate and land-use changes conditions.

Design and Implementation of an IoT- Based Weather Monitoring System forEnhanced Chicken Farm

The goal of the project is to address the difficulties faced by chicken farmers in properly controlling the environmental conditions of their farms through the design and implementation of an Internet of Things- based weather monitoring system. It investigates how Internet of Things (IoT) technology can be used to remotely monitor and regulate weather conditionsthat are critical to the health and productivity of chicken. The overall goals of the initiative are to maximize production efficiency, enhance animal welfare, and improve management methods on poultry farms. Chicken farms are extremely vulnerable to changes in temperature, humidity, and air quality, among other environmental factors. Unpredictable or unfavorable weather patterns can cause stress, lower egg production, and higher bird mortality rates. The goal of the project is to create an affordable, user-friendly weather monitoring system that will enable farmers to keep an eye on, control, and maintain the perfect environment for their chickens. The project's primary design focus is on placing wireless sensors all throughout the chicken farm to continuously monitor important meteorological factors. Through a user- friendly online or mobile application, farmers can obtain real-time weather data and receive alerts thanks to the sensors' connection to a central IoT network. In order to adapt the heating, cooling, and ventilation systems to variations in the weather, the system also includes automated actuators. The Internet of Things (IoT)-based weather monitoring system showed considerable advantages for chicken farmers after undergoing extensive testing and deployment.

Microalgae bio-reactive façade: System thermal–biological optimization

This article explores numerically the biotechnological performances of microalgae biofaçade. The model computes the system’s thermal behavior using a radiative-convective approach accounting for location on Earth and actual weather data. In a coupled manner, it simulates the microalgae culture behavior, i.e. light-driven growth and cell pigment content acclimation. In addition, it features refinement such as wavelength-dependent biomass optical properties and thermal-modulated biological rates. Thanks to this model, operation strategies and design possibilities were evaluated using actual weather data for a biofaçade module deployed in Marseille in 2023. Investigations revealed that a semi-batch mode of operation, while simplistic, is the most efficient way to operate a biofaçade if sole biological production is considered (about 18.0 ± 0.9 kg per year, 2.44 ± 0.12 g/L output concentration). However, if intended as an office glazing, turbidostat mode of operation should be preferred for aesthetic and visual comfort reasons (about 19.1 ± 1.1 kg per year, 0.64 ± 0.07 g/L output concentration). System optimization also confirmed the experimental observation that the system could be prone to overheating. Nevertheless, while overheating can be mitigated by increasing the reservoir thickness, this strategy is detrimental to the average output concentration. Finally, location-specific optimization revealed that a standard biofaçade module could be deployed over France, and system performances are derived for the whole country thanks to the weather forecast agency data.

Continuous-Time Multivariate Analysis

The starting point for much of multivariate analysis (MVA) is an n × p data matrix whose n rows represent observations and whose p columns represent variables. Some multivariate datasets, however, may be best conceptualized not as n discrete p-variate observations, but as p curves or functions defined on a common time interval. Here we introduce a framework for extending techniques of multivariate analysis to such settings. The proposed continuous-time multivariate analysis (CTMVA) framework rests on the assumption that the curves can be represented as linear combinations of basis functions such as B-splines, as in the Ramsay-Silverman representation of functional data; but whereas functional data analysis extends MVA to the case of observations that are curves rather than vectors—heuristically, n × p data with p infinite—we are instead concerned with what happens when n is infinite. We present continuous-time extensions of the classical MVA methods of covariance and correlation estimation, principal component analysis, Fisher’s linear discriminant analysis, and k-means clustering. We show that CTMVA can improve on the performance of classical MVA, in particular for correlation estimation and clustering, and can be applied in some settings where classical MVA cannot, including variables observed at disparate time points. CTMVA is illustrated with a novel perspective on a well-known Canadian weather dataset, and with applications to datasets involving international development, brain signals, and air quality. The proposed methods are implemented in the publicly available R package ctmva. Supplementary materials for this article are available online.

The weather today rocks or sucks for my tree: exploring the understanding of climate impacts on forests at high school level through tweets

Abstract. With the progression of global warming, impacts on the human sphere will undoubtedly increase. One prominent example at mid-latitudes is the stress on forests caused by climate change, which the BayTreeNet project (https://baytreenet.de/, last access: 30 July 2024) addresses from an interdisciplinary viewpoint. Scientists from physical climatology, dendroecology, and educational research collaborate to examine how long-term changes in weather patterns affect the state of trees, and how the atmosphere–tree relation can be used to the advantage of improving the communication of climate change effects to, in particular, high school students. This article presents a 1-week case study for the summer of 2021, when a distinct variability in weather patterns induced significant tree responses. The students of seven selected partner schools commented on the measured tree and weather data, which were available in real time, in the form of tweets and in conjunction with their educational geography program. The analyses of the tweets reveal that the students succeed in verbalizing the measured weather and, furthermore, manage to draw links between the stem diameter changes in trees and the weather variability. Problems arise with the use of less perceivable variables like the sap flow in the trees; also, the student posts exhibit shortcomings in establishing causal connections. Hence, the case study points to a discrepancy between describing basic environmental information and appreciating or understanding the underlying mechanistic links. This point will serve to refine future classroom concepts and, moreover, to enhance the communication of climate change effects on forests to the general public.

Evaluating influence of weather on Ascochyta blight severity in chickpea using correlation network, regression and principal component analysis

Aim: Weather parameters play a critical role in the survival and propagation of the causal pathogen Ascochyta rabiei. In the absence of vertical host resistance, the present study was designed to explore the weather-disease interrelationship for effective disease management. Methodology: Field experiments were conducted with three chickpea cultivars and three sowing times for two years. The relationship between the percent severity index (PSI) at weekly interval and observed weather data was explored using multivariate statistical techniques. Results: The correlation networks revealed that temperature and relative humidity parameters were negatively and positively associated with the blight PSI, respectively. Humid thermal ratio (HTR) significantly correlated with PSI among all three chickpea cultivars. The linear regression of PSI with weather variables exhibited a significant negative slope (β) with all thermal variables whereas a strong positive β was displayed with relative humidity and HTR. The Best subset multiple regression models further affirmed that PSI in three chickpea cultivars can be strongly predicted with models constituted with temperature, humidity and HTR. Principal component analysis showed that PC1 and PC2 explained 88.2% variability with weather variables affecting the PSI. The clustering of thermal and humidity condition parameters around the first two PCs explained their pivotal role in disease severity progress. Interpretation: The multivariate analysis revealed that air temperature, relative humidity and HTR had significant influence on periodic Ascochyta blight severity in chickpea crop. Present exploration will be vital in developing more targeted disease prediction and management strategies to secure chickpea production. Key words: Ascochyta blight, Best subset regression, Chickpea, Correlation network, Weather variables

Crop yield estimation uncertainties at the regional scale for Saxony, Germany

AbstractIn times of climate change and global population growth, agricultural yield forecasts play an increasingly important role. For example, predicting yields as early as possible in the event of a drought is crucial for decision‐makers in politics, government, and business. The aim of this study was to provide precise yield predictions at agricultural regions as early as possible with a minimum amount of weather data. Random forest models were used for this purpose. Although more than 290,000 datasets were available for analysis, all models tended to be heavily overfitting, which can be explained by the strong fragmentation of the input data by crop, region, and prediction time. The models reacted very differently to unknown datasets. It was found that the regionally trained models achieved lower (≥10%) relative root mean square errors (RRMSEs) than the supra‐regionally trained models. Rapeseed and barley achieved good predictions. Wheat had good potential, too. Corn, potatoes, and sugar beet achieved often too high RRMSEs. The results showed that targeted model selection for each region and an extension of the training time series could enable very good regional yield forecasts for rapeseed and cereals in the future.

PSIII-30 Development of an application programming interface (API) to automate downloading and processing of precision livestock data

Abstract Advancements in technology have ushered in a new era of sensor-based measurement and management of livestock production systems. These sensor-based technologies have the ability to automatically monitor feeding, growth, and enteric emissions for individual animals across confined and extensive production systems. One challenge with sensor-based technologies is the large amount of data generated, which can be difficult to access, process, visualize, and monitor information in real time to ensure equipment is working properly and animals are utilizing it correctly. A solution to this problem is the development of application programming interfaces (APIs) to automate downloading, visualizing, and summarizing datasets generated from precision livestock technology. For this methods paper, we develop three APIs and accompanying processes for rapid data acquisition, visualization, systems tracking, and summary statistics for three technologies (SmartScale, SmartFeed, and GreenFeed) manufactured by C-Lock Inc (Rapid City, SD). Program R markdown documents and example datasets are provided to facilitate greater adoption of these techniques and to further advance precision livestock technology. The methodology presented successfully downloaded data from the cloud and generated a series of visualizations to conduct systems checks, animal usage rates, and calculate summary statistics. These tools will be essential for further adoption of precision technology. There is huge potential to further leverage APIs to incorporate a wide range of datasets such as weather data, animal locations, and sensor data to facilitate decision-making on times scales relevant to researchers and livestock managers.

Data-driven approach to evaluate the impact of hull roughness on main engine load of river-sea ships

Hull roughness increases the ship's resistance during navigation, significantly increasing fuel consumption and emissions. The study proposed the hull fouling factors (HFFs) to evaluate the time-varying impact of hull roughness on main engine load (MEL) for the three bulk carriers. Initially, ships' Automatic Identification System data, operational data, and weather and sea state data were fused to develop predictive models for MEL. Specifically, the Random Forest (RF) model outperformed others in predicting MEL. Then, by adopting a controlled variable experiment, the RF model was utilized to evaluate the impact of hull roughness on MEL as the days since clean (DSC) increased. Hull roughness analysis results showed that the HFFs of ship operated primarily on sea routes showed an increasing trend with the increasing DSCs. In contrast, the HFFs of ships primarily operated on the river-sea routes did not change significantly. The approach revealed significant temporal variations in hull roughness for ships operating in different routes. Besides, the proposed HFFs could capture the impact of sea temperature, salinity, current velocity and ship speed on hull roughness. Overall, the finding provides new insights into the time-varying hull roughness impacts on sea-to-river ships, and supports hull cleaning strategies, energy management and emission estimates.

Subfield-level crop yield mapping without ground truth data: A scale transfer framework

Ongoing advances in satellite remote sensing data and machine learning methods have enabled crop yield estimation at various spatial and temporal resolutions. While yield mapping at broader scales (e.g., state or county level) has become common, mapping at finer scales (e.g., field or subfield) has been limited by the lack of ground truth data for model training and evaluation. Here we present a scale transfer framework, named Quantile loss Domain Adversarial Neural Networks (QDANN), that leverages knowledge from county-level datasets to map crop yields at the subfield level. Based on the strategy of unsupervised domain adaptation, QDANN is trained on labeled county-level data and unlabeled subfield-level data, with no requirement for yield information at the subfield level. We evaluate the proposed method applied to Landsat imagery and Gridmet weather data for maize, soybean, and winter wheat fields in the United States, using as reference data yield monitor records from roughly one million field-year observations. The model is compared with several process-based and machine learning-based benchmark approaches that train on simulated yield records or county-level data. QDANN-estimated yields achieved an R2 score (RMSE) of 48 % (2.29 t/ha), 32 % (0.85 t/ha), and 39 % (1.40 t/ha) for maize, soybean, and winter wheat in comparison with the ground-based yield measures, respectively. These performances are higher than benchmark approaches and are nearly as good as models trained on field-level data. When aggregated to the county level, the improvement achieved by QDANN is more pronounced and the R2 scores (RMSE) improved to 78 % (0.98 t/ha), 62 % (0.37 t/ha), and 53 % (1.00 t/ha) for maize, soybean, and winter wheat, respectively. This study demonstrates that the proposed scale transfer framework can serve as a reliable approach for yield mapping at the subfield level when there is no access to fine-scale yield information. Based on the QDANN model, we have generated and made publicly available 30-m annual yield maps for major crop-producing states in the U.S. since 2008.

Exploration of a model driven by climate to simulate pond water temperature in aquaculture systems

IntroductionInterannual climate variability in the Asian mega deltas has been posing a wide range of climate risks in the aquaculture systems of the region. Water temperature variation is one of the key risks related to disease outbreak, fish health, and loss and damage in fish production. However, Climate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Little research has been done to translate weather data into water temperature forecasts using mechanistic models in order to provide farmers with relevant forecasting information in the context of climate services.MethodsThe advantage of mechanistic models over statistical models is that they are based on physical processes and can therefore be used in a wider range of environmental conditions. In this study, we used an energy balance model to investigate its ability to simulate pond water temperature at daily and seasonal timescales in the southwest and northeast regions of Bangladesh. The model was able to adequately simulate pond water temperature at a daily timescale using publicly available weather data, and the accuracy of the model was lower at the study site with very heavy rainfall events.ResultsSensitivity analyses showed that the model was also able to simulate the impact of air temperature cold and hot spells on the pond water temperature. Connecting the model with seasonal air temperature forecasts resulted in very small variations in the forecasted seasonal pond water temperature, in large part due to the low variability observed in water temperature at seasonal scale in the study sites.DiscussionClimate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Hence, these improved predictions are important to help fish-farmers make informed decisions for managing associated climate risks.

The Impact of Air Pollution Information on Individuals' Exercise Behavior: Empirical Study Using Wearable and Mobile Devices Data.

Physical exercise and exposure to air pollution have counteracting effects on individuals' health outcomes. Knowledge on individuals' real-time exercise behavior response to different pollution information sources remains inadequate. This study aims to examine the extent to which individuals avoid polluted air during exercise activities in response to different air pollution information sources. We used data on individuals' exercise behaviors captured by wearable and mobile devices in 83 Chinese cities over a 2-year time span. In our data set, 35.99% (5896/16,379) of individuals were female and 64% (10,483/16,379) were male, and their ages predominantly ranged from 18 to 50 years. We further augmented the exercise behavior data with air pollution information that included city-hourly level measures of the Air Quality Index and particulate matter 2.5 concentration (in µg/m3), and weather data that include city-hourly level measures of air temperature (ºC), dew point (ºC), wind speed (m/s), and wind direction (degrees). We used a linear panel fixed effect model to estimate individuals' exercise-aversion behaviors (ie, running exercise distance at individual-hour, city-hour, or city-day levels) and conducted robustness checks using the endogenous treatment effect model and regression discontinuity method. We examined if alternative air pollution information sources could moderate (ie, substitute or complement) the role of mainstream air pollution indicators. Our results show that individuals exhibit a reduction of running exercise behaviors by about 0.50 km (or 7.5%; P<.001) during instances of moderate to severe air pollution, and there is no evidence of reduced distances in instances of light air pollution. Furthermore, individuals' exercise-aversion behaviors in response to mainstream air pollution information are heightened by different alternative information sources, such as social connections and social media user-generated content about air pollution. Our results highlight the complementary role of different alternative information sources of air pollution in inducing individuals' aversion behaviors and the importance of using different information channels to increase public awareness beyond official air pollution alerts.

Developing Predictive Models and Early Warning Systems for Invading Pathogens: Wheat Rusts.

Innovations in aerobiological and epidemiological modeling are enabling the development of powerful techniques to infer connectivity networks for transboundary pathogens in ways that were not previously possible. The innovations are supported by improved access to historical and near real-time highly resolved weather data, multi-country disease surveillance data, and enhanced computing power. Using wheat rusts as an exemplar, we introduce a flexible modeling framework to identify characteristic pathways for long-distance spore dispersal within countries and beyond national borders. We show how the models are used for near real-time early warning systems to support smallholder farmers in East Africa and South Asia. Wheat rust pathogens are ideal exemplars because they continue to pose threats to food security, especially in regions of the world where resources for control are limited. The risks are exacerbated by the rapid appearance and spread of new pathogenic strains, prodigious spore production, and long-distance dispersal for transboundary and pandemic spread.

Analysis of the Status of Irrigation Management in North Carolina

Farmers in North Carolina are turning to irrigation to reduce the impacts of droughts and rainfall variability on agricultural production. Droughts, rainfall variability, and the increasing demand for food, feed, fiber, and fuel necessitate the urgent need to provide North Carolina farmers with tools to improve irrigation management and maximize water productivity. This is only possible by understanding the current status of irrigated agriculture in the state and investigating its potential weaknesses and opportunities. Thus, the objective of this study was to perform a comprehensive analysis of the current state of irrigation management in North Carolina based on 15-year data from the Irrigation and Water Management Survey by the United States Department of Agriculture–National Agricultural Statistics Service (USDA-NASS). The results indicated a reduction in irrigation acres in the state. Also, most farms in the state have shifted to efficient sprinkler irrigation systems from gravity-fed surface irrigation systems. However, many farms in North Carolina still rely on traditional irrigation scheduling methods, such as examining crop conditions and the feel of soil in deciding when to irrigate. Hence, there are opportunities for enhancing the adoption of advanced technologies like soil moisture sensors and weather data to optimize irrigation schedules for improving water efficiency and crop production. Precision techniques and data-based solutions empower farmers to make informed, real-time decisions, optimizing water use and resource allocation to match the changing environmental conditions. The insights from this study provide valuable information for policymakers, extension services, and farmers to make informed decisions to optimize agricultural productivity and conserve water resources.

Unsupervised weathering identification of grottoes sandstone via statistical features of acoustic emission signals and graph neural network

Weathering features of sandstone heritage can be recognized by using artificial intelligence (AI) based surrogate models, and most models perform classification tasks for types based on precise labels. But there are lack of prior validated knowledge of the weathering or untagged historical data for complex weathering conditions in many cases. To this aim, a unsupervised graph neural network (GNN) based on the statistical features of the acoustic emission (AE) signals is constructed. Firstly, taking unweathered sandstone as a reference, we define 4 weathering levels of sandstone ranging from I to IV based on pore indicators. We selected 11 statistical features that are high correlated with pore of sandstone. Then, this GNN is constructed and trained by 2880 sets of statistical measured AE signals. Compared with AEs, LOF and IF models, GNN achieves the best identification performance among the four evaluation criteria. Each iteration of the GNN network is fitting the feature information of the signals and their neighbors. By data dimensionality reduction techniques, when the GNN stops iterating, it will be easy to distinguish unweathered AE signals from weathered one by comparing the reconstruction error of each signal. Furthermore, when the nearest neighbor’s k gradually increases, the AUC of GNN also gradually increases and then tend to stable when k equals to 50–100. While the hidden layers of the network aggregates less information about the neighborhood features of the signals and cannot distinguish significantly between unweathered and weathered signals when the value of k is small. As the depth of the network deepens, the feature values between signals become more and more similar, their reconstruction errors in the output layer of the network to become more similar, making it difficult to distinguish unweathered AE signals from weathered AE signals via GNN. Meanwhile, GNN adopts more AE features and considers the similarity between each features. This can greatly eliminate various errors caused by wave velocity measurement, greatly improving the robustness of AE detection. Hence, the GNN model presented addresses the limitations of relying solely on P-wave velocity measurements to assess the degree of sandstone weathering at stone cultural heritage.

Impact of temperatures on grassland transfer strategy: Evidence from the Qinghai-Tibet plateau in China

The increasing severity and complexity climate change present significant challenges to agricultural and social activities. This study investigates the effects of extreme temperatures on grassland transfer strategies, utilizing micro-level survey datasets obtained from pastoral areas of the Qinghai-Tibet Plateau in China, which are then matched with weather data. Employing the piecewise linear method and the temperature bins approach, the results reveal that extreme high temperature weather has a positive and significant effect on grassland transfer decisions. Specifically, each additional day of high-temperature weather throughout the year increases the overall probability of grassland transfer by 0.52%. Additionally, the analysis reveals a U-shaped relationship between temperature and grassland transfer-in decision, with the turning point occurring in the of 5–8 °C range. Furthermore, the findings indicate that grassland transfer decisions are predominantly influenced by moderating effects of factors such as the grassland quality and non-pastoral income. Moreover, herder households characterized by high income levels and younger demographics exhibit a notable capability in rapidly applying adaptive strategies to cope with temperature variations. Projections for future climate change scenarios indicate that extreme temperatures will significantly negatively impact grassland transfer-out, while positively influencing transfer-in.

The Analysis and Improvement of User Interface Design on Climate Information Service Mobile Application Using the Lean UX Method

Info BMKG is an iOS and Android app providing weather, temperature, air quality, and earthquake data across Indonesia. Issues related to the usability of the BMKG Info application were identified through observations and feedback found on the Google Play Store, which has a rating of 4.4. The responses identified included an unattractive appearance, limited features, increasing complexity, and a need for more innovation, necessitating usability evaluation and UI design improvements to enhance user experience. The analysis and redesign follow the Lean UX approach and Heuristic Evaluation method. Usability testing, specifically the Post-Study System Usability Questionnaire, is used to assess user satisfaction. The study finds that implementing Lean UX significantly improves the system's quality and user experience. UI design enhancements, based on usability testing, improve navigation, information clarity, and ease of use. Recommendations result in better outcomes, with Heuristic Evaluation yielding scores of H1 = 1 (System status visibility), H2 = 1 (Match between the system and the real world), H4 = 1 (Consistency and standards), H5 = 1 (Error prevention), H7 = 1 (Flexibility and efficiency of use), H8 = 1 (Aesthetic and minimalist design), and H9 = 1 (Help users recognize, diagnose, and recover from errors). The Post-Study System Usability Questionnaire method shows an improvement from high to a better category.

Artificial neural network-based models for short term forecasting of solar PV power output and battery state of charge of solar electric vehicle charging station

The main objective of this study is to develop ANN-based predictive models for short-term forecasting of solar PV power output and battery state of charge. The 3Ds energy model that integrates Decarbonization, Digitalization, and Decentralization of the energy system to facilitate the shift towards sustainable energy sources is used for this electric vehicle project. The experimental set up includes solar PV panels, a solar inverter, a battery inverter, and a battery bank. Additionally, smart meters were installed to collect real-time performance data from the solar PV-powered electric vehicle (EV) charging station. The weather and real-time system performance data were used to develop short term forecasting models based on Artificial Neural Networks (ANN) and the Levenberg-Marquardt method, to predict the performance of the system (power output and state of the charge) ahead. The R values for the prediction models of solar photovoltaic (PV) specific power and battery state of charge fall within the range of 0.9957–0.9969 and 0.9990 to 0.9996, respectively. Furthermore, the mean squared errors (MSEs) for the artificial neural network (ANN) models pertaining to solar photovoltaic (PV) power output and battery state of charge exhibit a variety of values. Specifically, the MSEs for solar PV power output range from 1.242 x 10^-4 to 1.579 x 10^-4, while the MSEs for battery status of charge range from 0.1889 to 0.3402. Artificial neural network (ANN) models possess considerable promise for practical implementations as they simplify intricate connections among inputs, parameters, and outputs in real-world scenarios. The level of accuracy and short-term predictive models of the solar PV powered EV charging station are very important for achieving a balance between the supply of solar photovoltaic (PV) system and the demand for electric vehicles (EVs). Predictive models will help build complex control mechanisms for controlling and optimizing solar PV-powered charging stations, supervising their operation and maintenance, and simplifying renewable energy pre-purchase. Future works will include the development of an energy management system to improve the efficiency of the solar stations charging the EVs, the establishment of blockchain networks for both the solar PV system and battery bank, and the integration of cybersecurity protocols for the charging station.

Improving Lameness Detection in Cows: A Machine Learning Algorithm Application

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 weeks. A Random Forest classifier, using input features selected by the Boruta Algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the precision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high resolution data from other systems, such as automated milking systems.

Assessing PEM fuel cell performance in a geothermal Cogeneration system for peak-time energy storage

In this investigation, a novel geothermal power plant was designed, combining absorption chiller units, PEMFC, EPEM, and ORC. We conducted a comprehensive case study across four continents—Asia, Oceania, Europe, and America—leveraging weather data from various cities to evaluate the system's efficacy. Our modeling utilized EES (Engineering Equation Solver) software, optimizing the setup via the RSM with three key objective functions: exergy efficiency, hydrogen production, and cost rate. The primary focus was on delivering electricity to residential properties during peak demand periods. Exploring six scenarios for organic fluids in organic Rankine cycles, we gauged energy, exergy, and overall system performance. The TOPSIS method led us to select scenario 3, employing R123 and R134a refrigerants, as the optimal choice. The results of the optimization showcased impressive figures: an exergy efficiency of 81.816%, a hydrogen production rate of 25.119 kg/h, and a cost rate of 15.967 $/h for the system's most efficient configuration. Economic analysis highlighted the organic Rankine cycle units 1 and 2 as the components with the highest costs. Our evaluation extended to various cities—Aomori, Grosseto, Lhasa, Wellington, and San Diego—assessing the electricity, heating, and cooling needs of residential complexes based on the system's performance.