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Predictive Agents for the Forecast of CO2 Emissions Issued from Electrical Energy Production and Gas Consumption

  • Seif Eddine BouzianeEmail author
  • Mohamed Tarek Khadir
Conference paper
  • 77 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1076)

Abstract

Energy production is nowadays compulsory for human well-being. However, the current energy systems and increasing demand come with a heavy environmental cost, hence, forecasting the environmental impact of energy production became a crucial task in order to control and reduce pollutant emissions while monitoring energy production. This paper describes an agent-based approach for forecasting CO2 emissions issued from energy production and consumption in the Algerian city of Annaba using data provided by the Algerian electricity and gas distribution company SONALGAZ. The proposed approach consists of combining artificial neural networks (ANN) forecasting models with an agent-based architecture in order to give the ability to the autonomous agents to forecast the hourly gas consumption and electrical production using dedicated ANNs. Forecasted values will then be used to calculate the equivalent amount of emitted CO2 for both energy sources.

Keywords

Neural network Agent-based Short-term forecasting Carbon dioxide 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Laboratoire de Gestion Electronique de Documents (LabGED), Department of Computer ScienceUniversity Badji MokhtarAnnabaAlgeria

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