A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Non-linear Autoregressive Neural Networks to Forecast Short-Term Solar Radiation for Photovoltaic Energy Predictions
Nowadays, green energy is considered as a viable solution to hinder CO2 emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover 40% of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, solar energy will play a crucial role. However, reliable models and tools are needed to forecast and estimate with a good accuracy the renewable energy production in short-term time periods. These tools will unlock new services for smart grid management. In this paper, we propose an innovative methodology for implementing two different non-linear autoregressive neural networks to forecast Global Horizontal Solar Irradiance (GHI) in short-term time periods (i.e. from future 15 to 120min). Both neural networks have been implemented, trained and validated exploiting a dataset consisting of four years of solar radiation values collected by a real weather station. We also present the experimental results discussing and comparing the accuracy of both neural networks. Then, the resulting GHI forecast is given as input to a Photovoltaic simulator to predict energy production in short-term time periods. Finally, we present the results of this Photovoltaic energy estimation discussing also their accuracy.
Non-linear Autoregressive Neural Networks to Forecast Short-Term Solar Radiation for Photovoltaic Energy Predictions
Nowadays, green energy is considered as a viable solution to hinder CO2 emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover 40% of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, solar energy will play a crucial role. However, reliable models and tools are needed to forecast and estimate with a good accuracy the renewable energy production in short-term time periods. These tools will unlock new services for smart grid management. In this paper, we propose an innovative methodology for implementing two different non-linear autoregressive neural networks to forecast Global Horizontal Solar Irradiance (GHI) in short-term time periods (i.e. from future 15 to 120min). Both neural networks have been implemented, trained and validated exploiting a dataset consisting of four years of solar radiation values collected by a real weather station. We also present the experimental results discussing and comparing the accuracy of both neural networks. Then, the resulting GHI forecast is given as input to a Photovoltaic simulator to predict energy production in short-term time periods. Finally, we present the results of this Photovoltaic energy estimation discussing also their accuracy.
Non-linear Autoregressive Neural Networks to Forecast Short-Term Solar Radiation for Photovoltaic Energy Predictions
Aliberti, Alessandro (author) / Bottaccioli, Lorenzo (author) / Cirrincione, Giansalvo (author) / Macii, Enrico (author) / Acquaviva, Andrea (author) / Patti, Edoardo (author) / Donnellan B., Klein C., Helfert M., Gusikhin O., Pascoal A. / Aliberti, Alessandro / Bottaccioli, Lorenzo / Cirrincione, Giansalvo
2019-01-01
Article/Chapter (Book)
Electronic Resource
English
DDC:
690
Forecasting short-term solar radiation for photovoltaic energy predictions
| BASE | 2018
Water consumption in distribution networks. Short term demand forecast
| British Library Conference Proceedings | 1995
Effectiveness of neural networks and transfer learning to forecast photovoltaic power production
| BASE | 2023
Short-term office building elevator energy consumption forecast using SARIMA
| Taylor & Francis Verlag | 2020