In recent times, the consistent growth of wave energy makes it one of the most promising forms of renewable energy. Due to the intermittency and non-stationary nature of waves, the grid integration of these renewable energy sources involves a series of complex power conditioning stages to deliver grid electric power that meets the corresponding quality standards. Furthermore, to enable optimal management and operation of a smart grid power system, forecasting the wave power delivered to the grid is essential. In this paper, we present a novel approach based on Long Short-Term Memory Neural Network to forecast the wave power delivered to the grid of a Wave Energy Converter (WEC) - the ISWEC, which is a device able to harvest sea energy by exploiting the inertial effect of a gyroscope - in short-time horizons (e.g. 1min). The data for the analysis was obtained from a simulator that combines a model of the ISWEC device and the power conditioning grid integration for this particular WEC. In addition, to investigate the effectiveness of downsampling, we compared the performance behavior of the raw dataset and downsampled versions of it. The results showed that as the downsampling increases, so does the forecasting accuracy: the forecasting performance of the raw dataset returned the worst results, while the one of the dataset with the biggest downsampling studied returned the best.

LSTM for Grid Power Forecasting in Short-Term from Wave Energy Converters / FONTANA CRESPO, RAFAEL NATALIO; Aliberti, Alessandro; Bottaccioli, Lorenzo; Macii, Enrico; Fighera, Giorgio; Patti, Edoardo. - (2023), pp. 1495-1500. (Intervento presentato al convegno 47th IEEE Annual Computers, Software, and Applications Conference (COMPSAC 2023) tenutosi a Torino (Italy) nel 27-29 June 2023) [10.1109/COMPSAC57700.2023.00230].

LSTM for Grid Power Forecasting in Short-Term from Wave Energy Converters

Rafael Natalio Fontana Crespo;Alessandro Aliberti;Lorenzo Bottaccioli;Enrico Macii;Edoardo Patti
2023

Abstract

In recent times, the consistent growth of wave energy makes it one of the most promising forms of renewable energy. Due to the intermittency and non-stationary nature of waves, the grid integration of these renewable energy sources involves a series of complex power conditioning stages to deliver grid electric power that meets the corresponding quality standards. Furthermore, to enable optimal management and operation of a smart grid power system, forecasting the wave power delivered to the grid is essential. In this paper, we present a novel approach based on Long Short-Term Memory Neural Network to forecast the wave power delivered to the grid of a Wave Energy Converter (WEC) - the ISWEC, which is a device able to harvest sea energy by exploiting the inertial effect of a gyroscope - in short-time horizons (e.g. 1min). The data for the analysis was obtained from a simulator that combines a model of the ISWEC device and the power conditioning grid integration for this particular WEC. In addition, to investigate the effectiveness of downsampling, we compared the performance behavior of the raw dataset and downsampled versions of it. The results showed that as the downsampling increases, so does the forecasting accuracy: the forecasting performance of the raw dataset returned the worst results, while the one of the dataset with the biggest downsampling studied returned the best.
2023
979-8-3503-2697-0
File in questo prodotto:
File Dimensione Formato  
Paper_Univariate_Grid_Power_Forecasting.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Pubblico - Tutti i diritti riservati
Dimensione 445.67 kB
Formato Adobe PDF
445.67 kB Adobe PDF Visualizza/Apri
LSTM_for_Grid_Power_Forecasting_in_Short-Term_from_Wave_Energy_Converters.pdf

accesso riservato

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.07 MB
Formato Adobe PDF
1.07 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2980936