Multivariate load forecasting of integrated energy system based on GBLA

There may be complex and strong coupling relationship between various loads in the integrated energy system. Compared with the single and independent forecasting of various loads, the direct development of multivariate load forecasting can further explore the internal relations between loads and improve the forecasting accuracy. This paper presents a prediction model based on GRA-Bi-LSTM-Attention(GBLA). GRA was used to quantitatively analyze the coupling between the influencing factors. Bi-LSTM was used to capture the nonlinear relationship in the multi-load time series and enhance the short-term memory ability. The attention mechanism was introduced to distribute the weight of the prediction results, so as to realize the joint prediction of multi-load. Finally, based on the load data set of typical integrated energy systems, the validation work is carried out, and the comparison analysis is made with other prediction models. The results show that the prediction method proposed in this paper has better performance.