Simulation of time series wind speed at an international airport

Wesonga, Ronald; Nabugoomu, Fabian; Ababneh, Faisal; Owino, Abraham Yeyo

doi:10.1177/0037549718788955

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Popular models for time series forecasting include autoregressive integrated moving average (ARIMA) [23], exponential smoothing (ETS) [24], vector autoregression (VAR), seasonal autoregressive integrated moving average (SARIMA) [25], and long short-term memory (LSTM) [26]. These models can handle both stationary and non-stationary time series data, and have been widely used in various fields such as finance, economics, energy, and weather forecasting [27,28].…”

Section: Time Series Forecastingmentioning

confidence: 99%

“…Aircraft landing and take-off (LTO) emissions have drawn much interest in research because they contribute significantly to air pollution from aviation activities [28]. However, the GSE systems at the airside that assist with managing, operating, and maintaining aircraft could significantly impact airport emissions.…”

Section: Estimating Electric Ground Support Equipment Energy Demandmentioning

confidence: 99%

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Energy Forecasting Model for Ground Movement Operation in Green Airport

et al. 2023

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The aviation industry has driven economic growth and facilitated cultural exchange over the past century. However, concerns have arisen regarding its contribution to greenhouse gas emissions and potential impact on climate change. In response to this challenge, stakeholders have proposed the use of electric ground support vehicles, powered by renewable energy sources, at airports. This solution aims to not only reduce emissions, but to also lower energy costs. Nonetheless, the successful implementation of such a system relies on accurate energy demand forecasting, which is influenced by flight data and fluctuations in renewable energy availability. This paper presents a novel data-driven, machine-learning-based energy prediction model that compared the performance of the Facebook Prophet and vector autoregressive integrated moving average algorithms to develop time series models to forecast the ground movement operation net energy demand in the airport, using historical flight data and an onsite airport-based PV power system (ASPV). The results demonstrate the superiority of the Facebook Prophet model over the vector autoregressive integrated moving average (VARIMA), highlighting its utility for airport operators and planners in managing energy consumption and preparing for future electrified ground movement operations at the airport.

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Section: Time Series Forecastingmentioning

confidence: 99%

Section: Estimating Electric Ground Support Equipment Energy Demandmentioning

confidence: 99%

Energy Forecasting Model for Ground Movement Operation in Green Airport

et al. 2023

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“…Wesonga, Nabugoomu, Ababneh, and Owino [14], in their work, assessed the autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models on the wind speed time series problem. Data on wind speed and minimum and maximum temperatures were evaluated.…”

Section: Related Workmentioning

confidence: 99%

An Application of Artificial Neural Network for Wind Speeds and Directions Forecasts in Airports

Lawrence¹,

Garba

Malgwi

et al. 2022

EJECE

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Wind speed patterns are highly dynamic and non-linear and thus cannot be accurately forecasted using conventional linear regression models. In this work, Artificial Neural Network (ANN) technique was applied to forecast wind speeds and directions in airports. Monthly data of maximum temperature, minimum temperature, wind speed, wind direction, relative humidity and wind run for Yola International Airport were collected from 1995 to 2021 from Nigerian Meteorological Agency (NIMET) Abuja-Nigeria. Six Neural Network models were built. ANN with no hidden layers, ANN model with one hidden layer and two dropout layers, ANN model with four hidden layers and three dropout layers, ANN model with eight hidden layers, ANN model with nine hidden layers and finally, ANN model with ten hidden layers. Back Propagation training algorithm was implemented using the PYTHON toolbox. Each of the models was trained using the training dataset and validated using the validation dataset. To test the forecasting ability of each of the models we tested it using unknown data that is the test dataset. The results from each of the models were organized and assessed in terms of the magnitude of the statistical error between the measured result and the real data. This was achieved by measuring the average of the Mean Square Errors (MSE) and Mean Absolute Error (MAE) for each of the models used for forecasting both wind speeds and directions. The results show that Multilayer perceptron with ten hidden layers with (MSE) = 0.92 and (MAE) = 0.73 emerged as the most preferred model for wind speeds forecast while the multilayer perceptron with four hidden layers with (MSE) = 1,858 and (MAE) = 35 emerged the most preferred model for wind directions forecast. Future research can be carried out to improve the accuracy of the model for wind direction forecasts.

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Conditional Status Detection for Factory Management System with Optimized Predictive Modeling

Raut

2020

Algorithms for Intelligent Systems

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Simulation of time series wind speed at an international airport

Cited by 4 publications

References 37 publications

Energy Forecasting Model for Ground Movement Operation in Green Airport

Energy Forecasting Model for Ground Movement Operation in Green Airport

An Application of Artificial Neural Network for Wind Speeds and Directions Forecasts in Airports

Conditional Status Detection for Factory Management System with Optimized Predictive Modeling

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