Load prediction model based on LSTM and attention mechanism

Abstract

load forecasting is an important research direction, which has always been the concern of academia and industry. Accurate prediction results can provide effective decisions for resource allocation of the system. However, the change of application load is very complex. How to accurately predict the change trend of load is a challenging task. Traditional prediction algorithms such as Arima algorithm based on statistical theory and neural network algorithm predict the target load only through the historical sequence of a single load index, ignoring the interaction between different load indexes. Therefore, this paper proposes a load prediction model based on long-term and short-term memory network and attention mechanism lstmda. The model successively uses convolutional neural network and channel attention mechanism to extract the local dependence characteristics between loads. The bidirectional LSTM network with attention mechanism is used to predict the load, and the data at different times are given different degrees of importance. The model proposed in this paper achieves better performance than existing prediction algorithms on real load data sets.