A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship
Abstract
:1. Introduction
2. Methodology
2.1. Dataset Acquisition
2.2. Modeling Methodologies
2.2.1. CNN–LSTM (Direct)
2.2.2. CNN–LSTM (Parallel)
2.2.3. CNN–Bidirectional LSTM (Direct)
2.2.4. CNN–Bidirectional LSTM (Parallel)
2.3. Experiment Procedures
2.4. Design Implementation of Models
3. Validation of Generalization Capabilities of the Models
- All data used in learning and assessing were equal, and learning was repeated five times.
- All LSTM and CNN hyperparameters were tuned equally for every model combination, and RMSE was used. The stored RMSE data distribution was used when comparing model performances.
- Statistical analysis was performed using the RMSE value obtained from repeated learning when comparing model performances.
- Average value: used as a value representing performance;
- Standard deviation: used as an indicator for assessing dispersion;
- Minimum value: used for estimating standard deviation;
- Quartile: use range of learning results set;
- Maximum value: used for estimating standard deviation.
- Evaluation functions such as precision or recall, which are used for classification, were not considered numeric data and used for learning. The proximity between the forecast and actual values was considered instead.
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Bodansky, D. Regulating greenhouse gas emissions from ships: The role of the International Maritime Organization. In Ocean Law Debates; Brill Nijhoff: Santa Cruz, CA, USA, 2018; pp. 478–501. [Google Scholar]
- Joung, T.-H.; Kang, S.-G.; Lee, J.-K.; Ahn, J. The IMO initial strategy for reducing Greenhouse Gas (GHG) emissions, and its follow-up actions towards 2050. J. Int. Marit. Saf. Environ. Aff. Shipp. 2020, 4, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Serra, P.; Fancello, G. Towards the IMO’s GHG goals: A critical overview of the perspectives and challenges of the main options for decarbonizing international shipping. Sustainability 2020, 12, 3220. [Google Scholar] [CrossRef] [Green Version]
- Kose, S.; Sekban, D.; Ozkok, M. Determination of port-induced exhaust gas emission amounts and investigation of environmental impact by creating emission maps: Sample of Trabzon port. Int. J. Sustain. Transp. 2021, 15, 1–11. [Google Scholar] [CrossRef]
- Reusser, C.A.; Pérez Osses, J.R. Challenges for Zero-Emissions Ship. J. Mar. Sci. Eng. 2021, 9, 1042. [Google Scholar] [CrossRef]
- Saxe, H.; Larsen, T. Air pollution from ships in three Danish ports. Atmos. Environ. 2004, 38, 4057–4067. [Google Scholar] [CrossRef]
- Cardoso, A.J.M.; Popkov, E.; Koptjaev, E. Evolution and development prospects of electric propulsion systems of large sea ships. In Proceedings of the 2020 International Ural Conference on Electrical Power Engineering (UralCon), Chelyabinsk, Russia, 22–24 September 2020; pp. 296–303. [Google Scholar]
- Hansen, J.F.; Wendt, F. History and state of the art in commercial electric ship propulsion, integrated power systems, and future trends. Proc. IEEE 2015, 103, 2229–2242. [Google Scholar] [CrossRef]
- McCoy, T.J. Trends in ship electric propulsion. In Proceedings of the IEEE Power Engineering Society Summer Meeting, Chicago, IL, USA, 21–25 July 2002; pp. 343–346. [Google Scholar]
- Pestanam, H. Future trends of electric propulsion and implications to ship design. Proc. Martech 2014, 1, 1–10. [Google Scholar]
- Sáiz, V.M.M.; López, A.P. Future trends in electric propulsion systems for commercial vessels. J. Marit. Res. 2007, 4, 81–100. [Google Scholar]
- Kanellos, F.D.; Anvari-Moghaddam, A.; Guerrero, J.M. A cost-effective and emission-aware power management system for ships with integrated full electric propulsion. Electr. Power Syst. Res. 2017, 150, 63–75. [Google Scholar] [CrossRef] [Green Version]
- Nuchturee, C.; Li, T.; Xia, H. Energy efficiency of integrated electric propulsion for ships—A review. Renew. Sustain. Energy Rev. 2020, 134, 110–145. [Google Scholar] [CrossRef]
- Xie, C.; Zhang, C. Research on the ship electric propulsion system network power quality with flywheel energy storage. In Proceedings of the 2010 Asia-Pacific Power and Energy Engineering Conference, Chengdu, China, 28–31 March 2010; pp. 1–3. [Google Scholar]
- Kim, Y.-R.; Kim, J.-M.; Jung, J.-J.; Kim, S.-Y.; Choi, J.-H.; Lee, H.-G. Comprehensive Design of DC Shipboard Power Systems for Pure Electric Propulsion Ship Based on Battery Energy Storage System. Energies 2021, 14, 5264. [Google Scholar] [CrossRef]
- Kim, K.; Park, K.; Ahn, J.; Roh, G.; Chun, K. A study on applicability of Battery Energy Storage System (BESS) for electric propulsion ships. In Proceedings of the 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), Busan, Korea, 1–4 June 2016; pp. 203–207. [Google Scholar]
- He, Y.; Fan, A.; Wang, Z.; Liu, Y.; Mao, W. Two-phase energy efficiency optimisation for ships using parallel hybrid electric propulsion system. Ocean Eng. 2021, 238, 1–12. [Google Scholar] [CrossRef]
- Zhu, J.; Chen, L.; Wang, X.; Yu, L. Bi-level optimal sizing and energy management of hybrid electric propulsion systems. Appl. Energy 2020, 260, 114–134. [Google Scholar] [CrossRef]
- Jaster, T.; Rowe, A.; Dong, Z. Modeling and simulation of a hybrid electric propulsion system of a green ship. In Proceedings of the 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA), Senigallia, Italy, 10–12 September 2014; pp. 1–6. [Google Scholar]
- Geertsma, R.; Negenborn, R.; Visser, K.; Hopman, J. Design and control of hybrid power and propulsion systems for smart ships: A review of developments. Appl. Energy 2017, 194, 30–54. [Google Scholar] [CrossRef]
- Kim, K.; An, J.; Park, K.; Roh, G.; Chun, K. Analysis of a supercapacitor/battery hybrid power system for a bulk carrier. Appl. Sci. 2019, 9, 1547. [Google Scholar] [CrossRef] [Green Version]
- Yuan, Y.; Zhang, T.; Shen, B.; Yan, X.; Long, T. A fuzzy logic energy management strategy for a photovoltaic/diesel/battery hybrid ship based on experimental database. Energies 2018, 11, 2211. [Google Scholar] [CrossRef] [Green Version]
- Ovrum, E.; Bergh, T. Modelling lithium-ion battery hybrid ship crane operation. Appl. Energy 2015, 152, 162–172. [Google Scholar] [CrossRef]
- Lan, H.; Wen, S.; Hong, Y.-Y.; David, C.Y.; Zhang, L. Optimal sizing of hybrid PV/diesel/battery in ship power system. Appl. Energy 2015, 158, 26–34. [Google Scholar] [CrossRef] [Green Version]
- Hou, J.; Sun, J.; Hofmann, H. Adaptive model predictive control with propulsion load estimation and prediction for all-electric ship energy management. Energy 2018, 150, 877–889. [Google Scholar] [CrossRef]
- Lim, C.-O.; Park, B.-C.; Lee, J.-C.; Kim, E.S.; Shin, S.-C. Electric power consumption predictive modeling of an electric propulsion ship considering the marine environment. Int. J. Nav. Archit. Ocean Eng. 2019, 11, 765–781. [Google Scholar] [CrossRef]
- Gao, D.; Jiang, Y.; Zhao, N. A novel load prediction method for hybrid electric ship based on working condition classification. Trans. Inst. Meas. Control. 2020, 44, 5–14. [Google Scholar] [CrossRef]
- Xiao, J.; Zhang, T.; Wang, X. Ship power load prediction based on RST and RBF neural networks. In Proceedings of the International Symposium on Neural Networks, Chongqing, China, 30 May–1 June 2005; pp. 648–653. [Google Scholar]
- Zhang, Q.; Zhang, H.; Chen, Y. Electric Power Load Prediction based on Temporal Semantic Information and LSTM. In Proceedings of the 2020 Eighth International Conference on Advanced Cloud and Big Data (CBD), Taiyuan, China, 5–6 December 2020; pp. 153–156. [Google Scholar]
- Ma, Y.; Oslebo, D.; Maqsood, A.; Corzine, K. Pulsed-Power Load Monitoring for an All-Electric Ship: Utilizing the Fourier Transform Data-Driven Deep Learning Approach. IEEE Electrif. Mag. 2021, 9, 25–35. [Google Scholar] [CrossRef]
- Hou, J.; Sun, J.; Hofmann, H. Control development and performance evaluation for battery/flywheel hybrid energy storage solutions to mitigate load fluctuations in all-electric ship propulsion systems. Appl. Energy 2018, 212, 919–930. [Google Scholar] [CrossRef]
- Xiros, N.I.; Kyrtatos, N.P. A neural predictor of propeller load demand for improved control of diesel ship propulsion. In Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held Jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No. 00CH37147), Patras, Greece, 19 July 2000; pp. 321–326. [Google Scholar]
- Elkafas, A.G.; Elgohary, M.M.; Zeid, A.E. Numerical study on the hydrodynamic drag force of a container ship model. Alex. Eng. J. 2019, 58, 849–859. [Google Scholar] [CrossRef]
- Molland, A.F.; Turnock, S.R.; Hudson, D.A. Ship Resistance and Propulsion; Cambridge University Press: Cambridge, UK, 2017. [Google Scholar]
- Valueva, M.V.; Nagornov, N.; Lyakhov, P.A.; Valuev, G.V.; Chervyakov, N.I. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Math. Comput. Simul. 2020, 177, 232–243. [Google Scholar] [CrossRef]
- Chen, Y.; Kang, Y.; Chen, Y.; Wang, Z. Probabilistic forecasting with temporal convolutional neural network. Neurocomputing 2020, 399, 491–501. [Google Scholar] [CrossRef] [Green Version]
- Mittelman, R. Time-series modeling with undecimated fully convolutional neural networks. arXiv 2015, arXiv:1508.00317. [Google Scholar]
- Gers, F.A.; Schmidhuber, J.; Cummins, F. Learning to forget: Continual prediction with LSTM. Neural Comput. 2000, 12, 2451–2471. [Google Scholar] [CrossRef] [PubMed]
- Schuster, M.; Paliwal, K.K. Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 1997, 45, 2673–2681. [Google Scholar] [CrossRef] [Green Version]
- Khirirat, S.; Feyzmahdavian, H.R.; Johansson, M. Mini-batch gradient descent: Faster convergence under data sparsity. In Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, Australia, 12–15 December 2017; pp. 2880–2887. [Google Scholar]
- Hinton, G.; Srivastava, N.; Swersky, K. Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Cited 2012, 14, 2. [Google Scholar]
- Al-Mohy, A.H.; Higham, N.J. Improved inverse scaling and squaring algorithms for the matrix logarithm. SIAM J. Sci. Comput. 2012, 34, 153–169. [Google Scholar] [CrossRef] [Green Version]
- Agresti, A. Categorical Data Analysis; John Wiley & Sons: Hoboken, NJ, USA, 2003; Volume 482. [Google Scholar]
- Hochreiter, S. The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 1998, 6, 107–116. [Google Scholar] [CrossRef] [Green Version]
- Hochreiter, S. Recurrent neural net learning and vanishing gradient. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 1998, 6, 107–116. [Google Scholar]
- Hu, Y.; Huber, A.; Anumula, J.; Liu, S.-C. Overcoming the vanishing gradient problem in plain recurrent networks. arXiv 2018, arXiv:1801.06105. [Google Scholar]
- Chai, T.; Draxler, R.R. Root mean square error (RMSE) or mean absolute error (MAE). Geosci. Model Dev. Discuss. 2014, 7, 1525–1534. [Google Scholar]
- Willmott, C.J.; Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 2005, 30, 79–82. [Google Scholar] [CrossRef]
Item | Unit | Remark |
---|---|---|
Electric Load | kW | 800–40,000 |
Propulsion Load | HP | 0–65,536 |
Heading Angle | Degree | 0–360° |
Rudder Angle | Degree | −35–35° |
Water Depth | M | 0–836 |
Water Speed | m/s | −4–6 |
Wind Angle | Degree | 0–360° |
Wind Speed | m/s | 0–47 |
Vessel Speed | knot | 0–25 |
M/E RPM | Rpm | 0–76.3 |
Draft (after) | m | 0–15.4 |
Draft (forward) | m | 0–18.8 |
Draft (Port) | m | 0–15.6 |
Draft (Starboard) | m | 0–16.38 |
Classification | SEA Going | Port in/out | Cargo Unload |
---|---|---|---|
Continuous Load (kW) | 2299.9 | 2660.4 | 1916 |
Reffer Container Load (kW) | 7440 | 7440 | 7440 |
Intermittent Load (kW) | 337.5 | 375.2 | 440.9 |
Group Diversity Factor (-) | 0.4 | 0.4 | 0.4 |
Actual Intermittent Load (kW) | 135 | 150.1 | 176.4 |
Deck Machinery Load (kW) | 0 | 3,637.2 | 286 |
Model Structure | Combination Method | Detail |
---|---|---|
CNN–LSTM | Direct | CNN: (5 × 128) − (2 × 128) − (2 × 64) − (64) LSTM: (1 × 512) − (1 × 256) − (128) |
CNN–bidirectional LSTM | Direct | CNN: (5 × 128) − (2 × 128) − (2 × 64) − (64) bidirectional LSTM: (1 × 512) − (1 × 256) − (128) |
CNN–LSTM | Parallel | CNN: (5 × 128) − (2 × 128) − (2 × 64) − (64) LSTM: (1 × 512) − (1 × 256) − (128) |
CNN–bidirectional LSTM | Parallel | CNN: (5 × 128) − (2 × 128) − (2 × 64) − (64) bidirectional LSTM: (1 × 512) − (1 × 256) − (128) |
Model | Combination Method | 1st | 2nd | 3rd | 4th | 5th |
---|---|---|---|---|---|---|
CNN–LSTM | Direct | 1752.0 | 1679.3 | 1869.2 | 1806.6 | 1523.6 |
CNN–bidirectional LSTM | Direct | 1670.7 | 1534.4 | 1501.7 | 1536.3 | 1520.2 |
CNN–LSTM | Parallel | 1461.0 | 1552.0 | 1476.6 | 1512.9 | 1507.6 |
CNN–bidirectional LSTM | Parallel | 1579.1 | 1514.4 | 1501.9 | 1509.1 | 1484.0 |
Model | Combination Method | Average | Standard Deviation | Min Value | 4-Quantiles | Max Value | ||
---|---|---|---|---|---|---|---|---|
25% | 50% | 75% | ||||||
CNN–LSTM | Direct | 1726.1 | 133.0 | 1523.6 | 1679 | 1752 | 1806 | 1869.2 |
CNN–bidirectional LSTM | Direct | 1552.7 | 67.4 | 1501.7 | 1520 | 1534 | 1536 | 1670.7 |
CNN–LSTM | Parallel | 1502.0 | 35.3 | 1461 | 1476 | 1507 | 1512 | 1552 |
CNN–bidirectional LSTM | Parallel | 1517.7 | 36.2 | 1484 | 1501 | 1509 | 1514 | 1579.1 |
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Kim, J.-Y.; Lee, J.-H.; Oh, J.-H.; Oh, J.-S. A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship. J. Mar. Sci. Eng. 2022, 10, 32. https://doi.org/10.3390/jmse10010032
Kim J-Y, Lee J-H, Oh J-H, Oh J-S. A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship. Journal of Marine Science and Engineering. 2022; 10(1):32. https://doi.org/10.3390/jmse10010032
Chicago/Turabian StyleKim, Ji-Yoon, Jong-Hak Lee, Ji-Hyun Oh, and Jin-Seok Oh. 2022. "A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship" Journal of Marine Science and Engineering 10, no. 1: 32. https://doi.org/10.3390/jmse10010032
APA StyleKim, J.-Y., Lee, J.-H., Oh, J.-H., & Oh, J.-S. (2022). A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship. Journal of Marine Science and Engineering, 10(1), 32. https://doi.org/10.3390/jmse10010032