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 Horizontal 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 production in short-term time periods. Finally, experimental results for both GHI forecast and Photovoltaic energy prediction are presented and discussed.

Forecasting short-term solar radiation for photovoltaic energy predictions

Andrea Acquaviva;
2018

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 Horizontal 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 production in short-term time periods. Finally, experimental results for both GHI forecast and Photovoltaic energy prediction are presented and discussed.
2018
Proceedings of 7th Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2018)
44
53
Alessandro Aliberti; Lorenzo Bottaccioli; Giansalvo Cirrincione; Enrico Macii; Andrea Acquaviva; Edoardo Patti
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/746138
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