A simulation based approach to forecast a demand load curve for a container terminal using battery powered vehicles

Grundmeier, Nico; Hahn, Axel; Ihle, Norman; Runge, Serge; Meyer-Barlag, Claas

doi:10.1109/ijcnn.2014.6889838

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…(Moscoso-Lopez,JA et al ( 2016)) [4], (Dragan D et 2020)) [11]. In addition to conventional forecasting methods, scholars have innovated forecasting methods, based on the fact that the process of forecasting methods is highly dynamic, and a simulation-based method for forecasting demand load pro les has emerged (Grundmeier N et al (2014)) [12]. Multiple regression and AW-BP prediction methods based on system order parameters have also emerged (Ya B (2016)) [13].…”

Section: Progress Of Research On Port Logistics Development Forecastingmentioning

confidence: 99%

Forecast of Guangzhou port logistics demand based on BP-NN

Lyu,

Feng,

Liu

2024

Preprint

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In recent years, the port economy of our country has developed rapidly. Guangzhou Port is an important node of the maritime transportation of the Belt and Road, connecting the hinterland economy of our country with the countries along the Belt and Road, which is of great significance to promote the economic development of the hinterland of our country. It is of great significance to predict the freight development demand of Guangzhou port scientifically and reasonably, which is beneficial to optimize the infrastructure construction and logistics system planning of Guangzhou port. This paper selects the port cargo throughput, foreign trade cargo throughput, container cargo throughput three index values to measure the freight development of Guangzhou port. Firstly, the GM(1,1) model and BP neural network model are constructed to predict the freight demand of Guangzhou port. Then, the GM(1,1) model and BP neural network model are combined to predict again. By comparing the three models, the results show that the accuracy of the combined model is better than that of the single model. The combined model of BP neural network and GM(1,1) can be effectively applied in the prediction of Guangzhou port logistics demand. Finally, the combined model of BP neural network and GM(1,1) is used to forecast the freight development demand of Guangzhou Port in 2022–2024, which provides reference for the development planning of Guangzhou Port.

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Section: Progress Of Research On Port Logistics Development Forecastingmentioning

confidence: 99%

Forecast of Guangzhou port logistics demand based on BP-NN

Lyu,

Feng,

Liu

2024

Preprint

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“…Within the frame of the project, a simulation model forecasting the logistic processes and the related electricity demand at the container terminal was developed (see also Grundmeier, Hahn, Ihle, Runge, & Meyer-Barlag, 2014). Because transport systems are often highly dynamic and depend on many different influence factors, it is often impossible to model these systems completely mathematically.…”

Section: Simulation-based Analysis Of Container Transportsmentioning

confidence: 99%

Using battery-electric AGVs in container terminals — Assessing the potential and optimizing the economic viability

Schmidt

Meyer-Barlag

Eisel

et al. 2015

Research in Transportation Business & Management

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“…A central task in these energy communities will be the planning and management of flexibility potentials and electricity production. By improving community load forecasts, energy management can be improved, costs can be lowered and CO 2 emissions reduced (Wen et al 2019;Grundmeier et al 2014). While (day-ahead) load forecasting plays an important role on all levels of future smart grids, we specifically focus on energy communities in this work.…”

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confidence: 99%

Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data

2022

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Accurate day-ahead load forecasting is an important task in smart energy communities, as it enables improved energy management and operation of flexibilities. Smart meter data from individual households within the communities can be used to improve such forecasts. In this study, we introduce a novel hybrid bi-directional LSTM-XGBoost model for energy community load forecasting that separately forecasts the general load pattern and peak loads, which are later combined to a holistic forecasting model. The hybrid model outperforms traditional energy community load forecasting based on standard load profiles as well as LSTM-based forecasts. Furthermore, we show that the accuracy of energy community day-ahead forecasts can be significantly improved by using smart meter data as additional input features.

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A simulation based approach to forecast a demand load curve for a container terminal using battery powered vehicles

Cited by 10 publications

References 5 publications

Forecast of Guangzhou port logistics demand based on BP-NN

Forecast of Guangzhou port logistics demand based on BP-NN

Using battery-electric AGVs in container terminals — Assessing the potential and optimizing the economic viability

Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data

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