{
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  "description": "The present disclosure relates to the technical field of electric power engineering, in particular to a two-stream Long Short-Term Memory (LSTM) method for predicting power load of port shore power. Loads. The method entails collecting longitudinal data to identify factors that affect power load…",
  "path": "/patents/1423667",
  "publishedAt": "2026-06-18T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "H02J3/003",
    "China Three Gorges University"
  ],
  "textContent": "The present disclosure relates to the technical field of electric power engineering, in particular to a two-stream Long Short-Term Memory (LSTM) method for predicting power load of port shore power. Loads. The method entails collecting longitudinal data to identify factors that affect power load data, performing correlation analysis to classify dominant and auxiliary features power loads; separately modeling the dominant and auxiliary features and generating a fusion feature map; constructing a Bayesian Optimization-Long Short-Term Memory (BO-LSTM) neural network, and inputting a fusion feature map into a two-stream time series learning module, extracting a deep representation of the dominant and auxiliary features, then introducing a channel attention mechanism is to weight a fusion feature vector, and outputting a power load prediction value by a residual correction module. The present disclosure significantly improves the prediction accuracy and robustness, and supports the real-time scheduling of the port shore power system.",
  "title": "TWO-STREAM LSTM METHOD FOR PREDICTING POWER LOAD OF PORT SHORE"
}