Forecasting of renewable energy-related time series by NARX ANN for electrical grid management
| Autor: | A. Di Piazza · M. C. Di Piazza · G. La Tona · M. Luna |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | The 13th International Conference of IMACS TC1 Committee (ELECTRIMACS 2019), Salerno, Italy, 20-23 maggio 2019 info:cnr-pdr/source/autori:A. Di Piazza · M. C. Di Piazza · G. La Tona · M. Luna/congresso_nome:The 13th International Conference of IMACS TC1 Committee (ELECTRIMACS 2019)/congresso_luogo:Salerno, Italy/congresso_data:20-23 maggio 2019/anno:2019/pagina_da:/pagina_a:/intervallo_pagine |
| Popis: | Forecasting of meteorological variables is crucial for accurate planning and management of electrical power grids, aiming at improving overall efficiency and performance. In this paper, an artificial neural network (ANN)-based technique is investigated for short-term forecasting of the hourly wind speed and solar radiation. Specifically, the non-linear autoregressive network with exogenous inputs (NARX) ANN is considered, compared to other models, and then selected to perform multi-step-ahead forecasting. Different time horizons have been considered in the range between 8 and 24 hours ahead. The main advantage of the proposed method is that it reconciles good forecasting performance with a very simple network structure, which is potentially implementable on a low-cost processing platform. |
| Databáze: | OpenAIRE |
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