Intelligent hybrid deep learning models for enhanced shipboard solar irradiance prediction and charging station

Sudharshan Konduru and C. Naveen

Renewable Energy, 2024, vol. 235, issue C

Abstract: Electrifying marine routes with solar energy significantly reduces ocean carbon emissions, playing a vital role in climate regulation. Solar irradiance forecasting ensures reliable power despite unpredictable sea weather, necessitating innovative model development. This research presents a forecasting model designed for the optimal placement of solar charging stations, providing hour-ahead solar irradiance predictions for onboard solar vessels. India (Jawaharlal Nehru Port Trust)– United States (Port of New York and New Jersey) sea route is selected to test our proposed forecasting model, as it aligns with busy shipping operations and long-distance trade requirements for electrification. Two distinct procedures by two case studies are used to select optimum data to predict the suitable locations for solar charging stations along the navigation route. In the first case study (Medium data analysis – MDA), the data are selected based on a statistical analysis of twenty years of hourly solar irradiance data from 201 locations. In the second case study (Large data analysis – LDA), two years of seasonal data from 201 locations are chosen and combined to form a large amount of hourly data. For both case studies, a hybrid solar irradiance forecasting model (ARIMA – Bi – LSTM) is developed by integrating the deep learning model with the time series model and fine-tuning them using optimization algorithms (PSO and GA). In MDA, the PSO optimization achieves mean absolute error (MAE) values of 0.32, 0.85, 1.83, 1.40, and 1.33 W/m2 for locations 1, 2, 3, 4, and 5, respectively, significantly outperforming the GA, which has MAE values of 1.36, 1.65, 3.25, 0.92, and 1.41 W/m2 for the same locations. In LDA, the PSO model achieves MAE values of 0.79, 0.91, 0.28, and 0.39 W/m2 for winter, rain, summer, and spring, respectively, also outperforming the GA, which has MAE values of 1.54, 2.17, 0.99, and 1.46 W/m2 for the same seasons. Comparatively, the LDA reinforces its best alignment to predict optimal charging station locations.

Keywords: Solar irradiance forecasting; Suez Canal sea route; Deep learning models; Trade improvement; Time series models; Residual ensembling (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148124013491
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:235:y:2024:i:c:s0960148124013491

DOI: 10.1016/j.renene.2024.121281

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().