Reducing CO2 emissions is a key goal of the strategy for a low-carbon economy and for the choice of greenhouse gas emission mitigation path. An effective forecasting method can represent a useful tool for managing renewable energies in microgrids and mitigating carbon dioxide emission. In this study is evaluated the trend of CO2 emission in Iran, Canada and Italy and compared the CO2 emission from consumption of energy sources: Coal - Natural Gas - Petroleum and other refined hydrocarbons - Renewable Energies. Furthermore, a proposed intelligent method has been provided for CO2 emission forecasting based on Generalized Regression Neural Network and Grey Wolf Optimization. Furthermore, the proposed method has been used for renewable energies generation (Wind power and Solar power) forecasting in the microgrid of Favignana island (Italy). The obtained results confirm the higher accuracy of the proposed method in long-term CO2 emission forecasting and short-term renewable energies generation as compared with other several methods.

Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology / Heydari, Azim; Garcia, Davide Astiaso; Keynia, Farshid; Bisegna, Fabio; Santoli, Livio De. - In: ENERGY PROCEDIA. - ISSN 1876-6102. - 159:(2019), pp. 154-159. (Intervento presentato al convegno 2018 Renewable Energy Integration with Mini/Microgrid, REM 2018 tenutosi a Rhodes) [10.1016/j.egypro.2018.12.044].

Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology

Heydari, Azim;Garcia, Davide Astiaso;Bisegna, Fabio;Santoli, Livio De
2019

Abstract

Reducing CO2 emissions is a key goal of the strategy for a low-carbon economy and for the choice of greenhouse gas emission mitigation path. An effective forecasting method can represent a useful tool for managing renewable energies in microgrids and mitigating carbon dioxide emission. In this study is evaluated the trend of CO2 emission in Iran, Canada and Italy and compared the CO2 emission from consumption of energy sources: Coal - Natural Gas - Petroleum and other refined hydrocarbons - Renewable Energies. Furthermore, a proposed intelligent method has been provided for CO2 emission forecasting based on Generalized Regression Neural Network and Grey Wolf Optimization. Furthermore, the proposed method has been used for renewable energies generation (Wind power and Solar power) forecasting in the microgrid of Favignana island (Italy). The obtained results confirm the higher accuracy of the proposed method in long-term CO2 emission forecasting and short-term renewable energies generation as compared with other several methods.
2019
2018 Renewable Energy Integration with Mini/Microgrid, REM 2018
CO2 emission forecasting; energy consumption; generalized regression neural network; grey wolf optimizer; renewable energy; energy
04 Pubblicazione in atti di convegno::04c Atto di convegno in rivista
Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology / Heydari, Azim; Garcia, Davide Astiaso; Keynia, Farshid; Bisegna, Fabio; Santoli, Livio De. - In: ENERGY PROCEDIA. - ISSN 1876-6102. - 159:(2019), pp. 154-159. (Intervento presentato al convegno 2018 Renewable Energy Integration with Mini/Microgrid, REM 2018 tenutosi a Rhodes) [10.1016/j.egypro.2018.12.044].
File allegati a questo prodotto
File Dimensione Formato  
Heydari_Renewable_2019.pdf

solo gestori archivio

Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 765.16 kB
Formato Adobe PDF
765.16 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1272504
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 63
  • ???jsp.display-item.citation.isi??? 50
social impact