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Sistema web de suporte a mobilidade multimodal em smart campus usando algoritmos baseados em inteligência artificial e análise estatística
(Universidade Federal do Pará, 2023-08-16) SILVA, Edinho do Nascimento da; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
Urban mobility in a large city largely depends on public transport, which plays a vital role in facilitating the flow of citizens between different areas where a variety of goods and services, such as health and physical activities, are available. According to the UN, it is expected that more than 60% of the world’s population will live in urban areas by 2050, which increases the need to make the entire transport system more sustainable and energy efficient. For this, the transition from modes powered by fossil fuels to options powered by electricity is a watershed that will require the implementation of new infrastructure to provide electricity to future modes. Such installation of energy points, called points of electric recharge of electric modes, is a technical, economic and energetic challenge. To discuss this current and relevant topic, this paper presents the results obtained from the Amazon Multimodal Intelligent System Project (SIMA), developed in partnership between the company Norte Energia and the Federal University of Pará (UFPA), focusing on the implementation at UFPA’s Smart Campus in Belém. In possession of the implementation of the structure, this branch of the project, made use of the infrastructure project for the development of a software-based solution that serves as support in decision-making on the management of the energy efficiency of an intelligent environment, that is, a Smart Campus. In view of the above, this work has as its main focus the design and development of an energy efficiency management system that converts the data generated by electric modes in order to be useful in the management of the energy efficiency of the entire multimodal system. For this, several algorithms were implemented in the software, namely: linear regression, general regression neural network and moving average. From this work it is possible to conclude that the tool can be useful to help modal managers to achieve reductions in energy consumption, consequently, reducing operating costs and increasing equipment longevity, which will also impact capital costs.