scholarly journals A genetic algorithm for finding realistic sea routes considering the weather

2020 ◽  
Vol 26 (6) ◽  
pp. 801-825
Author(s):  
Stefan Kuhlemann ◽  
Kevin Tierney
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Environmental Sustainability ◽  
Weather Conditions ◽  
Weather Data ◽  
Weather Forecasts ◽  
Travel Time Savings ◽  
Effective Decision ◽  
Effective Decision Support ◽  
The Impact

Abstract The weather has a major impact on the profitability, safety, and environmental sustainability of the routes sailed by seagoing vessels. The prevailing weather strongly influences the course of routes, affecting not only the safety of the crew, but also the fuel consumption and therefore the emissions of the vessel. Effective decision support is required to plan the route and the speed of the vessel considering the forecasted weather. We implement a genetic algorithm to minimize the fuel consumption of a vessel taking into account the two most important influences of weather on a ship: the wind and the waves. Our approach assists route planners in finding cost minimal routes that consider the weather, avoid specified areas, and meet arrival time constraints. Furthermore, it supports ship speed control to avoid areas with weather conditions that would result in high fuel costs or risk the safety of the vessel. The algorithm is evaluated for a variety of instances to show the impact of weather routing on the routes and the fuel and travel time savings that can be achieved with our approach. Including weather into the routing leads to a savings potential of over 10% of the fuel consumption. We show that ignoring the weather when constructing routes can lead to routes that cannot be sailed in practice. Furthermore, we evaluate our algorithm with stochastic weather data to show that it can provide high-quality routes under real conditions even with uncertain weather forecasts.

scholarly journals Addressing the Impact of Industrialized Components on the Cost of Temporary Heating in Cold-climate Regions

2018 ◽  
Author(s):  
Regina Dias Ferreira ◽  
Beda Barkokebas ◽  
Lana Secchi ◽  
Mustafa Gul ◽  
YuXiang Chen ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Residential Building ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Building Envelope ◽  
Cold Climate ◽  
Weather Data ◽  
The Cost ◽  
Construction Schedule ◽  
The Impact

In countries with cold climates such as Canada, the cost of providing space heating during the construction phase, also known as temporary heating, results in a significant additional construction cost, which causes budget deviations thus affecting the projectäó»s financial performance. In fact, the estimation of temporary heating is commonly overlooked due to the uncertainties such as weather forecast and the projectäó»s actual onsite schedule. The cost of temporary heating comprises two parts: (1) the cost of equipment rental, and (2) the fuel consumption required to heat a given area when the temperature falls below a certain threshold. The fuel consumption of the equipment is related to the temperature and exposure of the buildingäó»s envelope to the current weather conditions. Thus, the construction of the building envelope is critical to the reduction of fuel consumption and the consequent temporary heating cost of the project. In this context, the research presented in this paper aims to estimate the impacts of temporary heating for various constructive methods, such as the traditional stick-built practice and a few variations of panelized construction (in regard to the insulation used), by developing a simulation model to observe the variation of weather data, construction schedule, and fuel consumption for each scenario. To perform this analysis, a 4-story residential building located in the city of Edmonton, Alberta, Canada, is used as a case study in which the proposed scenarios are compared in order to address the advantages of industrialized components in reducing the cost of temporary heating.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

scholarly journals Impacts of Microclimate Conditions on the Energy Performance of Buildings in Urban Areas

Buildings ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 189 ◽  
Author(s):  
Javanroodi ◽  
M.Nik
Keyword(s):  
Urban Areas ◽  
Energy Demand ◽  
Weather Conditions ◽  
Energy Performance ◽  
Urban Morphology ◽  
Weather Data ◽  
Architectural Form ◽  
Energy Performance Of Buildings ◽  
Microclimate Conditions ◽  
The Impact

Urbanization trends have changed the morphology of cities in the past decades. Complex urban areas with wide variations in built density, layout typology, and architectural form have resulted in more complicated microclimate conditions. Microclimate conditions affect the energy performance of buildings and bioclimatic design strategies as well as a high number of engineering applications. However, commercial energy simulation engines that utilize widely-available mesoscale weather data tend to underestimate these impacts. These weather files, which represent typical weather conditions at a location, are mostly based on long-term metrological observations and fail to consider extreme conditions in their calculation. This paper aims to evaluate the impacts of hourly microclimate data in typical and extreme climate conditions on the energy performance of an office building in two different urban areas. Results showed that the urban morphology can reduce the wind speed by 27% and amplify air temperature by more than 14%. Using microclimate data, the calculated outside surface temperature, operating temperature and total energy demand of buildings were notably different to those obtained using typical regional climate model (RCM)–climate data or available weather files (Typical Meteorological Year or TMY), i.e., by 61%, 7%, and 21%, respectively. The difference in the hourly peak demand during extreme weather conditions was around 13%. The impact of urban density and the final height of buildings on the results are discussed at the end of the paper.

scholarly journals Weather Impact on Airport Performance

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 109 ◽  
Author(s):  
Michael Schultz ◽  
Sandro Lorenz ◽  
Reinhard Schmitz ◽  
Luis Delgado
Keyword(s):  
Traffic Management ◽  
Probability Distributions ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Data Set ◽  
Airport Capacity ◽  
Weather Events ◽  
The Individual ◽  
The Impact

Weather events have a significant impact on airport performance and cause delayed operations if the airport capacity is constrained. We provide quantification of the individual airport performance with regards to an aggregated weather-performance metric. Specific weather phenomena are categorized by the air traffic management airport performance weather algorithm, which aims to quantify weather conditions at airports based on aviation routine meteorological reports. Our results are computed from a data set of 20.5 million European flights of 2013 and local weather data. A methodology is presented to evaluate the impact of weather events on the airport performance and to select the appropriate threshold for significant weather conditions. To provide an efficient method to capture the impact of weather, we modelled departing and arrival delays with probability distributions, which depend on airport size and meteorological impacts. These derived airport performance scores could be used in comprehensive air traffic network simulations to evaluate the network impact caused by weather induced local performance deterioration.

scholarly journals Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1309 ◽  
Author(s):  
Eva Lucas Segarra ◽  
Hu Du ◽  
Germán Ramos Ruiz ◽  
Carlos Fernández Bandera
Keyword(s):  
Energy Demand ◽  
Forecast Error ◽  
Weather Forecast ◽  
Building Energy ◽  
Weather Data ◽  
Weather Forecasts ◽  
Hourly Data ◽  
The Future ◽  
The Impact ◽  
Near Future

The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.

scholarly journals Estimating the Impact of High-Fidelity Rainfall Data on Traffic Conditions and Traffic Prediction

2021 ◽  
pp. 036119812110263
Author(s):  
Anatolii Prokhorchuk ◽  
Nikola Mitrovic ◽  
Usman Muhammad ◽  
Aleksandar Stevanovic ◽  
Muhammad Tayyab Asif ◽  
...  
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Weather Conditions ◽  
Rainfall Data ◽  
Weather Data ◽  
Traffic Prediction ◽  
Percentage Error ◽  
Traffic Network ◽  
Radar Images ◽  
The Impact

Accurate prediction of network-level traffic parameters during inclement weather conditions can greatly help in many transportation applications. Rainfall tends to have a quantifiable impact on driving behavior and traffic network performance. This impact is often studied for low-resolution rainfall data on small road networks, whereas this study investigates it in the context of a large traffic network and high-resolution rainfall radar images. First, the impact of rainfall intensity on traffic performance throughout the day and for different road categories is analyzed. Next, it is investigated whether including rainfall information can improve the predictive accuracy of the state-of-the-art traffic forecasting methods. Numerical results show that the impact of rainfall on traffic varies for different rainfall intensities as well as for different times of the day and days of the week. The results also show that incorporating rainfall data into prediction models improves their overall performance. The average reduction in mean absolute percentage error (MAPE) for models with rainfall data is 4.5%. Experiments with downsampled rainfall data were also performed, and it was concluded that incorporating higher resolution weather data does indeed lead to an increase in performance of traffic prediction models.

scholarly journals STUDY AND SIMULATION OF THE FUEL CONSUMPTION OF A VEHICLE WITH RESPECT TO AMBIENT TEMPERATURE AND WEATHER CONDITIONS

2020 ◽  
Vol 7 (1) ◽  
pp. 24-35
Author(s):  
Mbelle Bisong Samuel ◽  
Paune Felix ◽  
Youmene Nongosso Miguel ◽  
Tambere Samam Cyrille ◽  
Pierre Kisito Talla
Keyword(s):  
Mathematical Model ◽  
Ambient Temperature ◽  
Fuel Consumption ◽  
Weather Conditions ◽  
The State ◽  
Microsoft Excel ◽  
Greenhouse Emissions ◽  
Standard Operating ◽  
Main Factors ◽  
The Impact

The consumption of fuel in vehicles depends on many factors such as the state of the roads, the state of the engine and the driver’s behavior. A mathematical model for evaluating vehicle fuel consumption on a 100 km interval at standard operating weather conditions was developed. This mathematical model developed took into consideration many factors, but the main factors were those related to weather conditions and temperature. Here a new simulation program for determining the influence of temperature and weather conditions on fuel consumption is built using the software Matlab. For efficient simulations the model uses a set of data for an SUV and then makes varying only the parameters that are related to weather and temperature for the simulation. During the simulation process, a set of 10 vehicle models and 8 roads conditions were chosen to run down the simulations and only the parameters of temperature, the drag coefficient and coefficient of rolling resistances respectively were subjected to variations during each of the simulations. Upon simulation, different results were obtained for the different parameters considered. For every 15% drop in temperature, 0.1litre, 0.12litre and 0.04litre increase in fuel consumption for the set of parameters chosen was noticed. These results were analyzed and interpreted with the help of Microsoft Excel and were found to be satisfactory given that it permits manufacturers and car users to have a notion of the impact of ambient temperature and weather conditions on fuel consumption, thereby promoting optimum usage of fuel, hence reducing the effect of greenhouse emissions in the atmosphere.

scholarly journals Weather Analysis of Flight Delay at Husein Sastranegara Airport

2019 ◽  
Vol 4 (2) ◽  
pp. 89-98
Author(s):  
Yedi Dermadi ◽  
Shinta Devi Lukitasari ◽  
Annisaa Nurhayati
Keyword(s):  
Weather Conditions ◽  
Air Transportation ◽  
Weather Data ◽  
Flight Delays ◽  
Weather Forecasts ◽  
Weather Information ◽  
Directorate General ◽  
Flight Delay ◽  
Flight Cancellations ◽  
Bad Weather

Flight is an activity that is very vulnerable to weather conditions. The accuracy of weather information strongly supports flight activities. The effects of bad weather on flights include flight delays and flight cancellations. Based on data on flight delays from the Directorate General of Air Transportation of the Ministry of Transportation from January to March 2019 at Husein Sastranegara Airport, it is known that 20-30% of flight delays are caused by weather constraints. To estimate flight delays based on weather forecasts, weather data analysis is carried out to determine the type of weather that is endangering flights and causing flight delays. The analysis was carried out using the K-NN and Random Forest algorithms

scholarly journals SENSITIVITY ANALYSIS OF AQUACROP EVAPOTRANSPIRATION TO WEATHER STATION DISTANCE

AGROFOR ◽  
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.

VP65 HTA Beyond 2020: Need For “Professionalization”?

2019 ◽  
Vol 35 (S1) ◽  
pp. 91-91
Author(s):  
Evidence Nyamadzawo
Keyword(s):  
Core Competencies ◽  
Critical Factor ◽  
Professional Autonomy ◽  
Ethical Standards ◽  
Technical Competence ◽  
Capability Building ◽  
The Status ◽  
Effective Decision ◽  
Effective Decision Support ◽  
The Impact

IntroductionHealth Technology Assessment (HTA) should continue evolving in order to effectively meet stakeholder expectations in the next decade. There is need for strengthening current expertise while developing new capabilities to keep up with rapid innovations in health care. Institutionalizing knowledge and skill through professionalization of HTA is a critical factor for successful “smart capability building” and the practice of HTA beyond 2020.MethodsProfessionalism is understood to mean different things by different people. This paper does not primarily focus on whether HTA is or should be a “profession” but on the development of institutions, structures, and attitudes that are characteristic of a profession and the impact they could have on the practice of knowledge and skill in HTA. “Professionalization” is used in this case to refer to the status of institutions, structures and attitudes and the process of establishing them. Professional standards include both ethical standards and standards of (technical) competence. Ethics applies to standards of competence, because stakeholders expect professionals to adhere to standards of competence and ethical standards. I will argue for the institutionalization of the practice of HTA knowledge and skill for mutual benefit and the prevention of exploitative and unjust use of HTA capabilities and processes. Is expertise necessary in HTA? Yes, conducting HTA requires specialized skills and knowledge. Effective decision-support requires multidisciplinary and efficient HTA teams. Core competencies and smart capabilities must be clearly defined and institutionalized for the production of effective HTA in the coming decade. Credentialism also becomes necessary for protecting the public from the consequences of bad evidence and bad choices. It will also protect the integrity of HTA practitioners and bolster professional autonomy.ResultsProfessionalization is instrumental in the development of ethical standards and standards of technical competence necessary for successful HTA practice.ConclusionsHTAi should professionalize HTA to facilitate and guide “smart capability building”.

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