Authors:
Alessandro Aliberti
1
;
Lorenzo Bottaccioli
1
;
Giansalvo Cirrincione
2
;
Enrico Macii
1
;
Andrea Acquaviva
1
and
Edoardo Patti
1
Affiliations:
1
Politecnico di Torino, Italy
;
2
Universite de Picardie Jules Verne, France
Keyword(s):
Solar Radiation Forecast, Artificial Neural Networks, Photovoltaic System, Energy Forecast, Renewable Energy.
Related
Ontology
Subjects/Areas/Topics:
Case Studies and Innovative Applications for Smart(Er) Cities
;
Energy and Economy
;
Energy Management Systems (EMS)
;
Energy-Aware Systems and Technologies
;
Load Balancing in Smart Grids
;
Renewable Energy Resources
;
Smart Cities
;
Smart Grids
Abstract:
In the world, energy demand continues to grow incessantly. At the same time, there is a growing need to reduce CO2 emissions, greenhouse effects and pollution in our cities. A viable solution consists in producing energy by exploiting renewable sources, such as solar energy. However, for the efficient use of this energy, accurate estimation methods are needed. Indeed, applications like Demand/Response require prediction tools to estimate the generation profiles of renewable energy sources.
This paper presents an innovative methodology for short-term (e.g. 15 minutes) forecasting of Global Hor- izontal Solar Irradiance (GHI). The proposed methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of solar radiation samples collected for four years by a real weather station. Then GHI forecast, the output of the neural network, is given as input to our Photovoltaic simulator to predict energy producti
on in short-term time periods. Finally, experimental results for both GHI forecast and Photovoltaic energy prediction are presented and discussed.
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