Ultra short term power load forecasting based on the fusion of Seq2Seq BiLSTM and multi head attention mechanism.

ABSTRACT: Ultra-short-term power load forecasting is beneficial to improve the economic efficiency of power systems and ensure the safe and stable operation of power grids. As the volatility and randomness of loads in power systems, make it difficult to achieve accurate and reliable power load forecasting, a sequence-to-sequence based learning framework is proposed to learn feature information in different dimensions synchronously. Convolutional Neural Networks(CNN) Combined with Bidirectional Long Short Term Memory(BiLSTM) Networks is constructed in the encoder to extract the correlated timing features embedded in external factors affecting power loads. The parallel BiLSTM network is constructed in the decoder to mine the power load timing information in different regions separately. The multi-head

SUBMITTER: Gou Y 

PROVIDER: S-EPMC10959395 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature