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The k-means clustering algorithm is used to quantify the voyage history data to classify the ship\u2019s navigation conditions into fast-changing conditions and slow-changing conditions. Some characteristic parameters related to working conditions are selected as input. Then, input and the category of working conditions are put into least squares support vector machine to learn and train to get an online working condition classifier. The genetic algorithm is used to optimize the radial-based neural network to predict the load demand under fast-changing conditions, use the Markov chain model to predict the load demand under slow-changing conditions, so as to obtain the most accurate future load demand of the ship. The simulation results show that the proposed prediction models under different conditions have higher precision, which is an effective means of predicting the load demand for hybrid power ships.<\/jats:p>","DOI":"10.1177\/0142331220923767","type":"journal-article","created":{"date-parts":[[2020,6,8]],"date-time":"2020-06-08T12:21:01Z","timestamp":1591618861000},"page":"5-14","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["A novel load prediction method for hybrid electric ship based on working condition classification"],"prefix":"10.1177","volume":"44","author":[{"given":"Diju","family":"Gao","sequence":"first","affiliation":[{"name":"Key Laboratory of Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, 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