scholarly journals Forecasting Charging Demand of Electric Vehicles Using Time-Series Models

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1487
Author(s):  
Yunsun Kim ◽  
Sahm Kim
Keyword(s):  
Electric Vehicles ◽  
Short Term Memory ◽  
Moving Average ◽  
Exogenous Variables ◽  
Autoregressive Integrated Moving Average ◽  
Historic Data ◽  
Auto Regressive ◽  
Charging Stations ◽  
Ev Charging ◽  
Privacy Issues

This study compared the methods used to forecast increases in power consumption caused by the rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced by various factors, including the condition of a vehicle, the battery’s state-of-charge (SOC), and the distance to the destination. However, power suppliers cannot easily access this information due to privacy issues. Despite a lack of individual information, this study compared various modeling techniques, including trigonometric exponential smoothing state space (i.e., Trigonometric, Box–Cox, Auto-Regressive-Moving-Average (ARMA), Trend, and Seasonality (TBATS)), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and long short-term memory (LSTM) modeling, based on past values and exogenous variables. The effect of exogenous variables was evaluated in macro- and micro-scale geographical areas, and the importance of historic data was verified. The basic statistics regarding the number of charging stations and the volume of charging in each region are expected to aid the formulation of a method that can be used by power suppliers.

scholarly journals Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1650 ◽  
Author(s):  
Bong-Gi Choi ◽  
Byeong-Chan Oh ◽  
Sungyun Choi ◽  
Sung-Yul Kim
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Traffic Load ◽  
Queueing Model ◽  
Traffic Data ◽  
Electric Vehicle Charging ◽  
Supply Equipment ◽  
Charging Stations ◽  
Ev Charging ◽  
Charging Demand

Establishing electric vehicle supply equipment (EVSE) to keep up with the increasing number of electric vehicles (EVs) is the most realistic and direct means of promoting their spread. Using traffic data collected in one area; we estimated the EV charging demand and selected priority fast chargers; ranging from high to low charging demand. A queueing model was used to calculate the number of fast chargers required in the study area. Comparison of the existing distribution of fast chargers with that suggested by the traffic load eliminating method demonstrated the validity of our traffic-based location approach.

scholarly journals Perspectives development of vehicle charging stations

2019 ◽  
Vol 24 (6) ◽  
pp. 106-108
Author(s):  
Grzegorz Parzonko
Keyword(s):  
Electric Vehicles ◽  
The Law ◽  
Vehicle Charging ◽  
Charging Stations ◽  
Ev Charging

The article presents the existing barriers and possibilities of development of charging stations for electric vehicles. The law on electromobility is discussed. Attention was paid to the problems related to the demand for electricity. The solutions of hybrid charging circuits of EV charging stations are presented.

scholarly journals Investigation of THD Analysis in Residential Distribution Systems with Different Penetration Levels of Electric Vehicles

2018 ◽  
Vol 9 (1) ◽  
pp. 21-27
Author(s):  
Jayababu Badugu ◽  
Y.P. Obulesu ◽  
Ch. Sai Babu
Keyword(s):  
Electric Vehicles ◽  
Distribution System ◽  
Distribution Systems ◽  
Harmonic Distortion ◽  
Voltage Distortion ◽  
Electric Vehicle Charging ◽  
Private And Public ◽  
Residential Distribution ◽  
Charging Stations ◽  
Ev Charging

Electric Vehicles (EVs) are becoming a viable transportation option because they are environmentally friendly and provide solutions to high oil prices. This paper investigates the impacts of electric vehicles on harmonic distortions in urban radial residential distribution systems. The accomplishment of EV innovation relies on the accessibility of EV charging stations. To meet the power demand of growing EVs, utilities are introducing EV charging stations in private and public areas; this led to a change in the residential distribution system infrastructure. In this paper, an urban radial residential distribution system with the integration of an electric vehicle charging facility is considered for investigation. An impact of different EV penetration levels on voltage distortion is analysed. Different penetration levels of EVs into the residential distribution system are considered. Simulation results are presented to validate the work carried out in this paper. An attempt has been made to establish the relationship between the level of penetration of the EVs and voltage distortion in terms of THD (Total Harmonic Distortion)

Deep learning-based container throughput forecasting: a triple bottom line approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonali Shankar ◽  
Sushil Punia ◽  
P. Vigneswara Ilavarasan
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Bottom Line ◽  
Content Type ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
Memory Network ◽  
Long Short Term Memory

PurposeContainer throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.Design/methodology/approachA novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.FindingsThe result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”Originality/valueA novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).

scholarly journals APLICAÇÃO DE MODELOS DE SÉRIES TEMPORAIS NA PREVISÃO DO PREÇO DO FARELO DE SOJA

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
José Airton Azevedo Dos Santos
Keyword(s):  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Term Memory ◽  
Auto Regressive ◽  
Long Short Term Memory

Resumo: O mercado da soja tem como uma de suas características a flutuação do preço do produto. Tal característica decorre de fatores que estão fora do controle do produtor, como variações na oferta e na demanda, intempéries climáticas, etc. Neste contexto, este trabalho tem como objetivo avaliar a eficácia de modelos de séries temporais, na sua forma univariada, na previsão do preço do farelo de soja no estado do Paraná. A base de dados, disponibilizada pela Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), apresenta uma série histórica, do preço do farelo de soja, no período entre 2011 e 2020, totalizando 111 observações. Modelos de previsão, baseados em Redes Neurais LSTM (Long Short-Term Memory) e ARIMA (Auto-Regressive Integrated Moving Average), foram implementados na linguagem Python. Resultados obtidos, dos dois modelos, foram comparados. Verificou-se, para um horizonte de curto prazo, que os dois modelos de previsão fornecem estimativas confiáveis para o preço do farelo de soja.

scholarly journals Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines

2021 ◽  
Vol 15 (11) ◽  
pp. 34
Author(s):  
Kobkiat Saraubon ◽  
Nuttapong Wiriyanuruknakon ◽  
Natdanai Tangthirasunun
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Polynomial Regression ◽  
Moving Average ◽  
Thin Layers ◽  
Prevention System ◽  
Auto Regressive ◽  
Distribution Line ◽  
Long Short Term Memory ◽  
Transmission And Distribution

Flashover on transmission and distribution line insulators occurs when the insulator’s resistance drops to a critical level and causes frequent power outages. Thin layers of dust, salt, and airborne particles, gradually deposited on the surface of insulators, as well as humidity, form an electrolyte which causes flashover.  In this paper, a flashover prevention system using IoT technology and machine learning is proposed in order to reduce loss and increase power reliability. The system includes an IoT module, a service and clients. The IoT module prototype was installed at a distribution line pole located in Pracha-utit, Bangkok, Thailand and had collected data for thirty-four months. The data were pre-processed and split for the training process and evaluation. In this study, we built and compared four models including linear regression, polynomial regression, Auto-regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The results revealed that the LSTM model outperformed (<em>R</em><sup>2</sup>=.931, RMSE= 530.74) the others.

scholarly journals COVID-19 Infection Forecasting based on Deep Learning in Iran

2020 ◽  
Author(s):  
Mehdi Azarafza ◽  
Mohammad Azarafza ◽  
Jafar Tanha
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
National Level ◽  
Absolute Error ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
Error Metrics ◽  
Squared Error ◽  
Better Than

Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

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