Prediction of short-term future driving conditions can contribute to energy management of
plug-in hybrid electric vehicles and subsequent improvement of their fuel economy. In this …

Transportation is an important factor that affects energy consumption, and driving behavior is
one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to …

In this paper, an optimized model based on Convolutional neural network (CNN) models is
proposed for transport energy forecasting in Republic of China (Taiwan). This study employs …

Modern computing resources, including machine learning-based techniques, are used to
maintain stability between the demand and supply of electricity. Machine learning is widely …

Since electric vehicles (EVs) could reduce the growing concerns on environmental pollution
issues and relieve the social dependency of fossil fuels, the EVs market is fast increased in …

This study tested exhaust emissions and fuel consumption for a hybrid electric vehicle (HEV)
in real-world conditions using a portable emissions measurement system (PEMS). A …

Today's energy resources are closer to consumers due to sustainable energy and advanced
technology. To that end, ensuring a precise prediction of energy consumption at the …

The development of data-driven building energy consumption prediction models has gained
more attention in research due to its relevance for energy planning and conservation …

The limited drive range (The maximum distance that an EV can travel.) of Electric Vehicles
(EVs) is one of the major challenges that EV manufacturers are attempting to overcome. To …

Large-scale and high-precision predictions of the charging energy required for electric
vehicles (EVs) are essential to ensure the safety of EVs and provide reliable inputs for grid …