KEYWORDS: Solar energy, Data modeling, System integration, Reliability, Mathematical optimization, Power consumption, Atmospheric modeling, Education and training, Power grids, Wind energy
The multiple loads of the integrated energy system have the characteristics of complex coupling, strong volatility, and strong randomness, and accurate prediction is the foundation and guarantee for optimizing the scheduling of the integrated energy system. An integrated energy system scheduling method based on SSA-BI-GRU multivariate load forecasting is proposed. Firstly, in order to explore the relationships between data more fully, the SSA-BI-GRU algorithm is proposed. Random forest regression model and cross-telecommunication validation are introduced into the data processing module. Then, we construct an integrated energy system optimization scheduling model to incorporate system reliability into the objective function. Finally, the load prediction results of the SSA-BI-GRU model are used as input to solve the IES scheduling problem using the improved MOGWO algorithm. The results show that SSA-BI-GRU improves the accuracy and speed of load forecasting, and the established multi-energy flow coupling optimization scheduling model achieves the economic, efficient, and reliable operation of IES.
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