Forecasting Short-term Solar Radiation for Photovoltaic Energy Predictions

Alessandro Aliberti, Lorenzo Bottaccioli, Giansalvo Cirrincione, Enrico Macii, Andrea Acquaviva, Edoardo Patti

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

Paper Citation

in Harvard Style

Aliberti A., Bottaccioli L., Cirrincione G., Macii E., Acquaviva A. and Patti E. (2018). Forecasting Short-term Solar Radiation for Photovoltaic Energy Predictions.In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-292-9, pages 44-53. DOI: 10.5220/0006683600440053

in Bibtex Style

@conference{smartgreens18,
author={Alessandro Aliberti and Lorenzo Bottaccioli and Giansalvo Cirrincione and Enrico Macii and Andrea Acquaviva and Edoardo Patti},
title={Forecasting Short-term Solar Radiation for Photovoltaic Energy Predictions},
booktitle={Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2018},
pages={44-53},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006683600440053},
isbn={978-989-758-292-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Forecasting Short-term Solar Radiation for Photovoltaic Energy Predictions
SN - 978-989-758-292-9
AU - Aliberti A.
AU - Bottaccioli L.
AU - Cirrincione G.
AU - Macii E.
AU - Acquaviva A.
AU - Patti E.
PY - 2018
SP - 44
EP - 53
DO - 10.5220/0006683600440053