Wave Height Forecasting over Ocean of Things Based on Machine Learning Techniques: An Application for Ocean Renewable Energy Generation
- Title
- Wave Height Forecasting over Ocean of Things Based on Machine Learning Techniques: An Application for Ocean Renewable Energy Generation
- Creator
- Upreti K.; Arora S.; Sharma A.K.; Pandey A.K.; Sharma K.K.; Dayal M.
- Description
- With the evolution and integration of information and communication technologies, the marine environment is being converted into a smart ocean of things. The only way to monitor the marine environment is to access marine information through satellites, radar, etc. Recently, many researchers have focused their interest on generating power from renewable energy. Among all the available renewable resources, ocean waves are attracting the interest of researchers for power generation. Therefore, this article focuses on designing a data-driven forecasting model for marine renewable energy generation applications. This article applies a novel Gini-impurity-index-based bidirectional long short-term memory model for selecting the best ocean/marine environmental factors to forecast wave height and ultimately predict power generation using the numerical model. This article presents short- and long-term forecasting results. In the experiment, four stations each are taken for both short- and long-term forecasting. The average root-mean-square error was approximately 0.17 for long-term forecasting and approximately 0.05 for short-term forecasting. 1976-2012 IEEE.
- Source
- IEEE Journal of Oceanic Engineering, Vol-49, No. 2, pp. 430-445.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Data driven; forecasting; ocean environment; renewable energy; wave height
- Coverage
- Upreti K., CHRIST (Deemed to Be University) Delhi NCR, Department of Computer Science, Ghaziabad, 201003, India; Arora S., KIET Group of Institutions, Department of Computer Applications, Ghaziabad, 201206, India; Sharma A.K., Galgotias University, School of Computing Science and Engineering, Greater Noida, 203201, India; Pandey A.K., KIET Group of Institutions, Department of Information Technology, Ghaziabad, 201206, India; Sharma K.K., KIET Group of Institutions, Department of Information Technology, Ghaziabad, 201206, India; Dayal M., Bharati Vidyapeeth's College of Engineering, Department of Computer Science and Engineering, New Delhi, 110063, India
- Rights
- Restricted Access
- Relation
- ISSN: 3649059; CODEN: IJOED
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Upreti K.; Arora S.; Sharma A.K.; Pandey A.K.; Sharma K.K.; Dayal M., “Wave Height Forecasting over Ocean of Things Based on Machine Learning Techniques: An Application for Ocean Renewable Energy Generation,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/13204.