Machine Learning Based PEVs Load Extraction and Analysis

Mansour-Saatloo, Amin; Moradzadeh, Arash; Mohammadi‐Ivatloo, Behnam; Ahmadian, Ali; Elkamel, Ali

doi:10.3390/electronics9071150

Cited by 26 publications

(17 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modeling of nonlinear relationships between input data is one of the most important features of this technique, even for very small volumes of data. The structural architecture and mathematical modeling of GRNN are fully presented in [41].…”

Section: Heating Load Demand Forecasting Resultsmentioning

confidence: 99%

A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Heating Load Demand Forecasting Resultsmentioning

confidence: 99%

A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proximity of the MSE and RMSE indicator values to zero indicated the accuracy of the network performance [37]. The mathematical formulation for calculating each of the indicators used in this paper is as follows [38]:…”

Section: Simulation Resultsmentioning

confidence: 99%

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

et al. 2021

Self Cite

View full text Add to dashboard Cite

Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.

show abstract

“…In [79], the authors proposed a hybrid KDE using both Gaussian and diffusion-based KDE (GKDE and DKDE) to predict the stay duration and charging demand of EVs. In [80], authors employed a generalized regression neural network (GRNN) model to predict the charging load. In [81], the authors predicted the charging demands of electric bus charging stations using an SVM and the wolf pack algorithm.…”

Section: Machine Learning For Charging Demand Predictionmentioning

confidence: 99%

Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review

Deb

2021

Energies

View full text Add to dashboard Cite

As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is an intricate process involving various activities, such as charging station placement, charging demand prediction, and charging scheduling. This planning process involves interactions between power distribution and the road network. The advent of machine learning has made data-driven approaches a viable means for solving charging infrastructure planning problems. Consequently, researchers have started using machine learning techniques to solve the aforementioned problems associated with charging infrastructure planning. This work aims to provide a comprehensive review of the machine learning applications used to solve charging infrastructure planning problems. Furthermore, three case studies on charging station placement and charging demand prediction are presented. This paper is an extension of: Deb, S. (2021, June). Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. In the 2021 5th International Conference on Smart Grid and Smart Cities (ICSGSC) (pp. 16–22). IEEE. I would like to confirm that the paper has been extended by more than 50%.

show abstract

Machine Learning Based PEVs Load Extraction and Analysis

Cited by 26 publications

References 36 publications

A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review

Contact Info

Product

Resources

About