A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

• Published in: Energies (Basel) Vol. 16; no. 13; p. 5092
• Main Authors: Uyanık, Tayfun, Bakar, Nur Najihah Abu, Kalenderli, Özcan, Arslanoğlu, Yasin, Guerrero, Josep M., Lashab, Abderezak
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Algorithms; Analysis; Artificial intelligence; Case studies; data-driven; Datasets; Decision trees; Distributed generation; Electrical loads; Energy consumption; Energy efficiency; Forecasting; Fuel consumption; generator load prediction; maritime; Maritime industry; Massive data points; Neural networks; Planning; Power; Prediction models; Regression analysis; shipboard microgrid; Ships; Support vector machines; Tanker ships; Tankers
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16135092

Energy efficiency and operational safety practices on ships have gained more importance due to the rules set by the International Maritime Organization in recent years. While approximately 70% of the fuel consumed on a commercial ship is utilized for the propulsion load, a significant portion of the remaining fuel is consumed by the auxiliary generators responsible for the ship’s onboard load. It is crucial to comprehend the impact of the electrical load on the ship’s generators, as it significantly assists maritime operators in strategic energy planning to minimize the chance of unexpected electrical breakdowns during operation. However, an appropriate handling mechanism is required when there are massive datasets and varied input data involved. Thus, this study implements data-driven approaches to estimate the load of a chemical tanker ship’s generator using a 1000-day real dataset. Two case studies were performed, namely, single load prediction for each generator and total load prediction for all generators. The prediction results show that for the single generator load prediction of DG1, DG2, and DG3, the decision tree model encountered the least errors for MAE (0.2364, 0.1306, and 0.1532), RMSE (0.2455, 0.2069, and 0.2182), and MAPE (17.493, 5.1139, and 7.7481). In contrast, the deep neural network outperforms all other prediction models in the case of total generation prediction, with values of 1.0866, 2.6049, and 14.728 for MAE, RMSE, and MAPE, respectively.