Clustering Methods for Power Quality Measurements in Virtual Power Plant

Aksan, Fachrizal; Jasiński, Michał; Sikorski, Tomasz; Kaczorowska, Dominika; Rezmer, J.; Suresh, Vishnu; Leonowicz, Zbigniew; Kostyła, Paweł; Szymańda, Jarosław M.; Janík, P.

doi:10.3390/en14185902

Cited by 13 publications

(2 citation statements)

References 32 publications

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“…The Passport Business Database (Passport) [66] was used as the source of data for the K-Means Clustering Algorithm method [111]. Data with exceptions identified and previously excluded from tabular comparisons were downloaded for the full analysis.…”

Section: Clusterization Resultsmentioning

confidence: 99%

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

2022

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The Industry 4.0 idea influences the development of both charging stations and electromobility development, due to its emphasis on device communication, cooperation, and proximity. Therefore, in electromobility development, growing attention is paid to chargers’ infrastructure density and automotive electric vehicles’ accessibility. The main goal of this scientific paper was to present the electromobility development represented in the number of charging stations and its infrastructure development calculations. In this study, the sequence of methods was used to indicate and explore the research gap. The first was the Structured Literature Review (SLR) variation method. The second method was the classical tabular comparison of gathered results. The third research method was a cluster analysis based on secondary data with cross-country comparisons of the number of charging stations and electric cars. Therefore, this paper presents a theoretical discussion and practical business implications based on the achieved results of clusters and rankings. The main finding of this paper is that charging stations play a pivotal role in electromobility development in countries with already developed road infrastructure and maritime transportation. The charging stations can support energetic infrastructure, especially in countries with vast geographical distances. The charging stations and electric vehicles statistics presented in ratios and ranks proved similarities in the electromobility development patterns in the analyzed countries. This paper also presents the limitations of the performed study and identifies possible future research avenues.

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Section: Clusterization Resultsmentioning

confidence: 99%

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

2022

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“…The main objective of this phase is to prepare and convert the raw data into a format suitable for the HDL model. The implementation of data pre-processing is very important for any type of deep learning model as it can improve the model accuracy by improving the quality of the data and extracting valuable information from the data [ 36 ]. In this work, various data pre-processing techniques were used, ranging from normalising the data to splitting the dataset.…”

Section: Methodsmentioning

confidence: 99%

CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany

Aksan

Suresh

et al. 2023

Sensors

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The massive installation of renewable energy sources together with energy storage in the power grid can lead to fluctuating energy consumption when there is a bi-directional power flow due to the surplus of electricity generation. To ensure the security and reliability of the power grid, high-quality bi-directional power flow prediction is required. However, predicting bi-directional power flow remains a challenge due to the ever-changing characteristics of power flow and the influence of weather on renewable power generation. To overcome these challenges, we present two of the most popular hybrid deep learning (HDL) models based on a combination of a convolutional neural network (CNN) and long-term memory (LSTM) to predict the power flow in the investigated network cluster. In our approach, the models CNN-LSTM and LSTM-CNN were trained with two different datasets in terms of size and included parameters. The aim was to see whether the size of the dataset and the additional weather data can affect the performance of the proposed model to predict power flow. The result shows that both proposed models can achieve a small error under certain conditions. While the size and parameters of the dataset can affect the training time and accuracy of the HDL model.

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Thermal error modeling of motorized spindle considering the effect of milling head heat source

Dai,

Li,

Zhan

et al. 2023

Int J Adv Manuf Technol

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Clustering Methods for Power Quality Measurements in Virtual Power Plant

Cited by 13 publications

References 32 publications

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany

Thermal error modeling of motorized spindle considering the effect of milling head heat source

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