Short-term Local Forecasting by Artificial Intelligence Techniques and Assess Related Social Effects from Heterogeneous Data

Gong, Bing (2017). Short-term Local Forecasting by Artificial Intelligence Techniques and Assess Related Social Effects from Heterogeneous Data. Tesis (Doctoral), E.T.S.I. Industriales (UPM). https://doi.org/10.20868/UPM.thesis.47668.

Descripción

Título: Short-term Local Forecasting by Artificial Intelligence Techniques and Assess Related Social Effects from Heterogeneous Data
Autor/es:
  • Gong, Bing
Director/es:
  • Ordieres-Meré, Joaquín
Tipo de Documento: Tesis (Doctoral)
Fecha: 2017
Materias:
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Ingeniería de Organización, Administración de Empresas y Estadística
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

This work aims to use the sophisticated artificial intelligence and statistic techniques to forecast pollution and assess its social impact. To achieve the target of the research, this study is divided into several research sub-objectives as follows: First research sub-objective: propose a framework for relocating and reconfiguring the existing pollution monitoring networks by using feature selection, artificial intelligence techniques, and information theory. Second research sub-objective: comprehensively assess the influences factors of socio-economic activities on pollutions at district level in a macro environment, in order to explore the pollution formation mechanism. Third research sub-objective: introduce an innovative method of pollution forecasting, including regional factors, and apply it to a case study to predict ozone threshold exceedances. Fourth research sub-objective: provide a way to develop a multi-level energy usage awareness system, which can provide energy usage, CO2 emission, and the cost of energy usage at operation, product, and order level. Fifth research sub-objective: identify the patterns of dynamic changes of energy usage, CO2 emission, and GDP by using cluster algorithms and find the corresponding driven factors for the clusters differences.

Más información

ID de Registro: 47668
Identificador DC: http://oa.upm.es/47668/
Identificador OAI: oai:oa.upm.es:47668
Identificador DOI: 10.20868/UPM.thesis.47668
Depositado por: Archivo Digital UPM 2
Depositado el: 15 Sep 2017 07:09
Ultima Modificación: 15 Sep 2017 07:30
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