DATA RELIABLE ENERGY PREDICTION FOR ELECTRIC BUSES USING ARTIFICIAL NEURAL NETWORK

Abstract

ABSTRACT: Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. The developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate. We introduce a data driven approach for characterization and predictive classification of electric city buses by powerful machine learning algorithms. These challenges encompass critical factors such as range anxiety charge rate optimization and the longevity of energy storage in EVs. By analyzing existing literature, we delve into the role that AI can play in tackling these challenges and enabling efficient energy management in EMS. This paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement to expand the interest of researchers and scholars in this topic and to jointly promote the application of artificial intelligence in the field of power systems. Unlike these studies this article estimates the energy consumption of all the electric buses that circulate in the city of Santiago Chile during the studied period using full disaggregated GPS data and empirical measurements on some sensitized electric buses. The neural network is employed to learn the current driving information and main knowledge after the simplified correlation of characteristic parameters and meanwhile the genetic algorithm is adopted to optimize the initial weight and thresholds of networks. This analysis allows decision-makers to target investment by determining the buses with higher energy consumption savings in the face of budget constraints.