[HTML][HTML] A data-driven framework for medium-term electric vehicle charging demand forecasting
The rapid phase-in of electric vehicles (EV) will cause unprecedented issues with managing
the supply of electricity and charging stations. It is in the interest of utility providers and …
the supply of electricity and charging stations. It is in the interest of utility providers and …
Probabilistic residential load forecasting based on micrometeorological data and customer consumption pattern
A prior knowledge of residential load demand is critical for power system operations at the
distribution level, such as economic dispatch, demand response and energy storage …
distribution level, such as economic dispatch, demand response and energy storage …
Building load forecasting using deep neural network with efficient feature fusion
The energy consumption of buildings has risen steadily in recent years. It is vital for the
managers and owners of the building to manage the electric energy demand of the …
managers and owners of the building to manage the electric energy demand of the …
Hybrid deep learning mechanism for charging control and management of Electric Vehicles
AK Venkitaraman, VSR Kosuru - European Journal of Electrical …, 2023 - ejece.org
In perspective of their environmental friendliness and energy efficiency, Electric Vehicles
(EVs) are posing a threat to traditional gasoline automobiles. Identifying the future charging …
(EVs) are posing a threat to traditional gasoline automobiles. Identifying the future charging …
[HTML][HTML] Data-driven charging demand prediction at public charging stations using supervised machine learning regression methods
Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a
distribution network and its reliability. Generally, monitoring energy consumption has …
distribution network and its reliability. Generally, monitoring energy consumption has …
A novel cross-case electric vehicle demand modeling based on 3D convolutional generative adversarial networks
Electric Vehicle (EV) demand modeling constitutes the cornerstone of studies aiming to
facilitate the integration of EVs into the power system. The different characteristics of the EV …
facilitate the integration of EVs into the power system. The different characteristics of the EV …
[HTML][HTML] Machine learning-based management of electric vehicles charging: Towards highly-dispersed fast chargers
Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power
grid as it avoids distribution system overloads, increases power quality, and decreases …
grid as it avoids distribution system overloads, increases power quality, and decreases …
Mid-term electricity load prediction using CNN and Bi-LSTM
Electricity is one of the critical role players to build an economy. Electricity consumption and
generation can affect the overall policy of the country. Such importance opens an area for …
generation can affect the overall policy of the country. Such importance opens an area for …
[HTML][HTML] Short-term load forecasting for electric vehicle charging station based on niche immunity lion algorithm and convolutional neural network
Y Li, Y Huang, M Zhang - Energies, 2018 - mdpi.com
Accurate and stable prediction of short-term load for electric vehicle charging stations is of
great significance in ensuring economical and safe operation of electric vehicle charging …
great significance in ensuring economical and safe operation of electric vehicle charging …
A deep learning model for short-term power load and probability density forecasting
Accurate load forecasting is critical for power system planning and operational decision
making. In this study, we are the first to utilize a deep feedforward network for short-term …
making. In this study, we are the first to utilize a deep feedforward network for short-term …