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. Thesis (Doctoral), E.T.S.I. Industriales (UPM). https://doi.org/10.20868/UPM.thesis.47668.

Description

Title: Short-term Local Forecasting by Artificial Intelligence Techniques and Assess Related Social Effects from Heterogeneous Data
Author/s:
  • Gong, Bing
Contributor/s:
  • Ordieres-Meré, Joaquín
Item Type: Thesis (Doctoral)
Date: 2017
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería de Organización, Administración de Empresas y Estadística
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 47668
DC Identifier: http://oa.upm.es/47668/
OAI Identifier: oai:oa.upm.es:47668
DOI: 10.20868/UPM.thesis.47668
Deposited by: Archivo Digital UPM 2
Deposited on: 15 Sep 2017 07:09
Last Modified: 15 Mar 2018 23:30
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