Simulation-Based Approach for Lookahead Scheduling of Onshore Wind Projects Subject to Weather Risk

Executed outdoors in high-wind areas, adverse weather conditions represent a significant risk to onshore wind farm construction activities. While methods for considering historical weather data during pre-construction scheduling are available, approaches capable of quantitatively assessing how short-term weather fluctuations may impact upcoming construction activities have yet to be developed. This study is proposing a hybrid simulation-based approach that uses short-term precipitation, wind speed, and temperature forecasts together with planned and as-built activity durations to develop lookahead (e.g., upcoming 14-day) schedules for improved project planning and control. Functionality and applicability of the method was demonstrated on a case study of a 40 MW onshore wind project, and the method was validated using event validity, face validation, and sensitivity analysis. As expected, favorable weather conditions experienced during the tested lookahead periods resulted in a negligible impact (less than 10% reduction) on the productivity of weather-sensitive activities, which translated into a project delay of one day. The responsiveness of the framework was confirmed through sensitivity analysis, which demonstrated a 50% reduction in productivity resulting from poor weather conditions. The ability of the method to provide decision-support not currently offered by commercially-available scheduling systems was confirmed by subject experts, who endorsed the ability of the method to enhance lookahead scheduling and to facilitate the monitoring and control of weather impact uncertainty on project durations.

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Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽

10.3390/en14113030 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3030

Author(s):

Simon Liebermann ◽

Jung-Sup Um ◽

YoungSeok Hwang ◽

Stephan Schlüter

Keyword(s):

Neural Network ◽

Goodness Of Fit ◽

Renewable Energy Sources ◽

Weather Conditions ◽

Solar Irradiation ◽

Weather Data ◽

Short Term ◽

Climate Conditions ◽

Lstm Network ◽

The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

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Electrochemically activated solutions in beekeeping

Bulletin of Sumy National Agrarian University. The series: Veterinary Medicine ◽

10.32845/bsnau.vet.2019.4.5 ◽

2019 ◽

pp. 28-34

Author(s):

O. Musienko ◽

O. Kysterna ◽

D. Demyanenko

Keyword(s):

Infectious Diseases ◽

Sodium Hypochlorite ◽

Weather Conditions ◽

Active Sodium ◽

Ascosphaera Apis ◽

Adverse Weather Conditions ◽

Adverse Weather ◽

Epizootic Process ◽

And Control ◽

Test Objects

The article studies in detail the disease of the mixed form of infectious diseases of honey bee brood. The characteristics of pathogens, features of diagnostics and control of this disease of honey bees are indicated. When conducting epizootic monitoring of bee diseases encountered in surveyed apiaries, it was found that a large percentage of mycosis lesions is associated with the weakening of bee families resulting from adverse weather conditions and insufficient feed base (50-68.3 %). It was further complicated by the process of varrosy invasion, which occurred in all surveyed apiaries with an invasion intensity of more than 4%. In studying the effect of weather conditions on the epizootic process, a peculiarity of the course of infectious breeding diseases in the bee family was established. It was characterized by the onset of symptoms of ascospherosis (solid chalky pieces in the cell and at the bottom of the hive) during periods of adverse weather (cold, prolonged rain). Symptoms of ascospherosis appeared not only in healthy families but also in families undergoing treatment. During the laboratory examination of dead larvae, cultures of different pathogens were isolated. A study of the contamination of cellular honey, which was selected from sick and conditionally healthy bee families, indicated that Ascosphaera apis culture was more commonly isolated and 100% contaminated. We also calculated the epizootic index of infectious diseases of bees that were found on the apiaries surveyed. Ascospherosis was found to be the longest recorded in comparison with other infectious diseases and the epizootic index was accordingly higher. And the development of European foulbroods, other types of rot and aspergillosis occurred against the background of bee ascospherosis. In a study of intestinal toxic effects of active sodium hypochlorite at concentrations of 0.7%, 0.5% and 0.25% a.d., it was found that the drug did not cause bee death within 72 hours after feeding in any group of bees. Active sodium hypochlorite effectively decontaminates test objects at a concentration of 2.5 g / l for two hours. When using cells from sick families, the concentration of 5.0 g / l was effective.

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Productivity-Based Management System for Offsite Manufacturing: Case Study of Noralta Lodge

Modular and Offsite Construction (MOC) Summit Proceedings ◽

10.29173/mocs173 ◽

2015 ◽

pp. 439-447

Author(s):

A. Alwisy ◽

S. Bu Hamdan ◽

Z. Ajweh ◽

M Al-Hussein

Keyword(s):

Management System ◽

Large Scale ◽

Labour Productivity ◽

Weather Conditions ◽

Project Planning ◽

Learning Curves ◽

Modular Construction ◽

Task Duration ◽

Resource Levelling ◽

And Control

Large-scale projects entail a zero-tolerance policy in regards to on-time project delivery and project quality. Severe winter conditions in Canada challenge conventional on-site construction activities and raise the risk of project delays and deficiencies. Industrialized (modularized) construction stands as an alternative that provides high quality products in a timely manner. Moreover, modular construction offers manufactured building components in a controlled environment, which ensures that quality standards remain consistent regardless of weather conditions. Once manufactured, modular units are then shipped to the site to be assembled. Two major geographical phases are common in offsite construction: the manufacturing phase, and the on-site installation phase. Consequently, management teams face challenges related to productivity and optimum work sequence in both phases. Traditional project planning and control methods consider the duration of a task as a static entity resulting from the direct relationship between the sizes of the crews on-site and labour productivity. Learning curves, skill-based tasklabour matrices, and resource levelling techniques are factors that imply the dynamic nature of construction tasks; delays in one task may affect other subsequent tasks both directly and indirectly. The Productivity-Based Management System (PBMS) provides opportunities to increase the production rates of task duration, and decrease actual task duration. The proposed research introduces a framework for a PBMS to manage and control the on-site phase of modular construction. In this research, the PBMS is developed, implemented, and then applied to a 1,700- bedroom workforce camp in Fort McMurray, Alberta, Canada.

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Development of a Biologically-Based System for Detection and Tracking of Airborne Herbicides

Weed Technology ◽

10.1017/s0890037x00027809 ◽

1993 ◽

Vol 7 (2) ◽

pp. 404-410 ◽

Cited By ~ 18

Author(s):

Kassim Al-Khatib ◽

Gaylord I. Mink ◽

Guy Reisenauer ◽

Robert Parker ◽

Halvor Westberg ◽

...

Keyword(s):

Computer Model ◽

High Sensitivity ◽

Weather Conditions ◽

Weather Data ◽

Indicator Plant ◽

Field Exposure ◽

Detection And Tracking ◽

Adverse Weather Conditions ◽

Adverse Weather ◽

Herbicide Drift

This study was designed to develop a protocol for using a biologically-based system to detect and tract airborne herbicides. Common bean, lentil, and pea were selected for their quasi-diagnostic sensitivity to chlorsulfuron, thifensulfuron, metsulfuron, tribenuron, paraquat, glyphosate, bromoxynil, 2,4-D, and dicamba. Plants were grown in the greenhouse at Prosser, WA, and placed at 25 exposure sites at weekly intervals between Apr. 2 and Oct. 15, 1991. After 1 wk of field exposure plants were brought back and observed for herbicide symptoms over a 28-d period. Symptoms that developed were compared with symptoms caused by disease, insects, adverse weather conditions, and herbicides applied at different rates under controlled conditions on these species. In addition, if herbicide symptoms were observed, herbicide spray records and weather data in the area were used in a computer model to determine the source of potential herbicide drift. This study demonstrates that indicator plant species selected for high sensitivity to herbicides can be used to monitor the occurrence of herbicide movement.

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A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

International Journal of Photoenergy ◽

10.1155/2014/569249 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 30

Author(s):

Yuan-Kang Wu ◽

Chao-Rong Chen ◽

Hasimah Abdul Rahman

Keyword(s):

Power Output ◽

Solar Power ◽

Peak Load ◽

Weather Conditions ◽

Green Energy ◽

Weather Data ◽

Stable Operation ◽

Short Term ◽

Pv System ◽

Experimental Database

The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV) system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

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The effect of short term exposure to outdoor air pollution on fertility

Reproductive Biology and Endocrinology ◽

10.1186/s12958-021-00838-6 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Mireia González-Comadran ◽

Bénédicte Jacquemin ◽

Marta Cirach ◽

Rafael Lafuente ◽

Thomas Cole-Hunter ◽

...

Keyword(s):

Air Pollution ◽

Sensitivity Analysis ◽

Particulate Matter ◽

Significant Risk ◽

Acute Exposure ◽

Reproductive Outcomes ◽

Short Term ◽

Short Term Exposure ◽

The Impact ◽

Subacute Exposure

Abstract Background There is evidence to suggest that long term exposure to air pollution could be associated with decreased levels of fertility, although there is controversy as to how short term exposure may compromise fertility in IVF patients and what windows of exposure during the IVF process patients could be most vulnerable. Methods This prospective cohort study aimed to evaluate the impact of acute exposure that air pollution have on reproductive outcomes in different moments of the IVF process. Women undergoing IVF living in Barcelona were recruited. Individual air pollution exposures were modelled at their home address 15 and 3 days before embryo transfer (15D and 3D, respectively), the same day of transfer (D0), and 7 days after (D7). The pollutants modelled were: PM2.5 [particulate matter (PM) ≤2.5 μm], PMcoarse (PM between 2.5 and 10μm), PM10 (PM≤10 μm), PM2.5 abs, and NO2 and NOx. Outcomes were analyzed using multi-level regression models, with adjustment for co-pollutants and confouding factors. Two sensitivity analyses were performed. First, the model was adjusted for subacute exposure (received 15 days before ET). The second analysis was based on the first transfer performed on each patient aiming to exclude patients who failed previous transfers. Results One hundred ninety-four women were recruited, contributing with data for 486 embryo transfers. Acute and subacute exposure to PMs showed a tendency in increasing miscarriage rate and reducing clinical pregnancy rate, although results were not statistically significant. The first sensitivity analysis, showed a significant risk of miscarriage for PM2.5 exposure on 3D after adjusting for subacute exposure, and an increased risk of achieving no pregnancy for PM2.5, PMcoarse and PM10 on 3D. The second sensitivity analysis showed a significant risk of miscarriage for PM2.5 exposure on 3D, and a significant risk of achieving no pregnancy for PM2.5, PMcoarse and PM10 particularly on 3D. No association was observed for nitrogen dioxides on reproductive outcomes. Conclusions Exposure to particulate matter has a negative impact on reproductive outcomes in IVF patients. Subacute exposure seems to increase the harmful effect of the acute exposure on miscarriage and pregnancy rates. Nitrogen dioxides do not modify significantly the reproductive success.

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Effect of Adverse Weather Conditions on Vehicle Braking Distance of Highways

Civil Engineering Journal ◽

10.28991/cej-030967 ◽

2018 ◽

Vol 4 (1) ◽

pp. 46 ◽

Cited By ~ 10

Author(s):

Ali Abdi Kordani ◽

Omid Rahmani ◽

Amir Saman Abdollahzadeh Nasiri ◽

Sid Mohammad Boroomandrad

Keyword(s):

Friction Coefficient ◽

Weather Condition ◽

Weather Conditions ◽

Dynamic Responses ◽

Adverse Weather Conditions ◽

Adverse Weather ◽

Simulation Based ◽

Different Types ◽

Braking Distance ◽

Margin Of Safety

The effect of adverse weather conditions on the safety of vehicles moving on different types of roads and measuring its margin of safety have always been a major research issue of highways. Determining the exact value of friction coefficient between the wheels of the vehicle and the surface of the pavement (usually Asphalt Concrete) in different weather conditions is assumed as a major factor in design process. An appropriate method is analyzing the dynamic motion of the vehicle and its interactions with geometrical elements of road using dynamic simulation of vehicles. In this paper the effect of changes of friction coefficient caused by the weather conditions on the dynamic responses of three types of vehicles: including Sedan, Bus, and Truck based on the results of Adams/car Simulator are investigated. The studies conducted on this issue for different weather conditions suggest values ranging from 0.04 to 1.25. The results obtained from simulation based on Adams/car represent that the friction coefficient in values of 0.9, 0.8, 0.7, 0.6 do not effect on braking distance significantly and it is possible to attribute them all to dry weather condition. However, as it was anticipated the values of 0.5, 0.4, 0.28 and 0.18 have significant differences in braking distance. Hence, the values of 0.5, 0.4, 0.28 and 0.18 can be attributed to wet, rainy, snowy and icy conditions respectively.

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SENSITIVITY ANALYSIS OF AQUACROP EVAPOTRANSPIRATION TO WEATHER STATION DISTANCE

AGROFOR ◽

10.7251/agreng1603148s ◽

2016 ◽

Vol 1 (3) ◽

Author(s):

Claire SIMONIS ◽

Bernard TYCHON ◽

Françoise GELLENSMEULENBERGHS

Keyword(s):

Sensitivity Analysis ◽

Water Availability ◽

Weather Conditions ◽

Actual Evapotranspiration ◽

Weather Data ◽

Grass Cover ◽

Weather Station ◽

Grid Data ◽

The Impact ◽

Wheat Plot

Water balance calculation is essential for reliable agricultural management, and theactual evapotranspiration (ET) is the most complicated balance term to estimate. Inagriculture, the most common method used is based on Penman-Monteith referenceevaporation is determined from weather conditions for an unstressed grass cover,further multiplied by crop specific and soil water availability coefficients to obtainthe actual evapotranspiration. This approach is also used in the AquaCrop model.This model has proven to be accurate when all weather data are locally available.However, in many cases, weather data can’t be collected on the site due to thelimited number of stations and the vast region covered by each of them. Instead,data are often collected at many kilometers from the study site. The question wewant to study is: how does evapotranspiration accuracy evolves with respect toweather station distance? A winter wheat plot in Lonzée (Belgium) was studiedduring the 2014-2015 agricultural seasons. Actual evapotranspiration wassimulated with AquaCrop thanks to the weather data collected at 3 differentdistances from the study site: on the site (data collected by a fluxnet station), 20km, 50 km and 70km from the site. The non-on-site weather data were derivedfrom spatially interpolated 10 km grid data. These results were then compared tothe fluxnet station evapotranspiration measurements to assess the impact of theweather station distance. Substantial differences, which were found between thefour cases, evoking the importance of assimilating satellite derived ET products(e.g. MSG) into AquaCrop.

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Short-Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion

Complexity ◽

10.1155/2021/6662959 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Yue Hou ◽

Zhiyuan Deng ◽

Hanke Cui

Keyword(s):

Traffic Flow ◽

Effective Means ◽

Weather Conditions ◽

Weather Data ◽

Multiple Time ◽

Traffic Data ◽

Flow Data ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction

Short-term traffic flow prediction is an effective means for intelligent transportation system (ITS) to mitigate traffic congestion. However, traffic flow data with temporal features and periodic characteristics are vulnerable to weather effects, making short-term traffic flow prediction a challenging issue. However, the existing models do not consider the influence of weather changes on traffic flow, leading to poor performance under some extreme conditions. In view of the rich features of traffic data and the characteristic of being vulnerable to external weather conditions, the prediction model based on traffic data has certain limitations, so it is necessary to conduct research studies on traffic flow prediction driven by both the traffic data and weather data. This paper proposes a combined framework of stacked autoencoder (SAE) and radial basis function (RBF) neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. Firstly, SAE is used to process the traffic flow data in multiple time slices to acquire a preliminary prediction. Then, RBF is used to capture the relation between weather disturbance and periodicity of traffic flow so as to gain another prediction. Finally, another RBF is used for the fusion of the above two predictions on decision level, obtaining a reconstructed prediction with higher accuracy. The effectiveness and robustness of the proposed model are verified by experiments.

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Impact of weather conditions on airport arrival delay and throughput

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-12-2020-0318 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Álvaro Rodríguez-Sanz ◽

Javier Cano ◽

Beatriz Rubio Fernández

Keyword(s):

Decision Making ◽

Weather Conditions ◽

Weather Data ◽

Arrival Process ◽

Data Set ◽

Content Type ◽

Weather Events ◽

Adverse Weather ◽

Arrival Processes ◽

The Impact

Purpose Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed. Design/methodology/approach This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results. Findings The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration. Originality/value This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

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