A Regional Integrated Energy System Load Prediction Method Based on Bayesian Optimized Long-Short Term Memory Neural Network

In the face of the rapid growth and development of regional integrated energy system (RIES) globally, accurate load prediction technique is increasingly playing a critical role in RIES planning. This paper presents a Bayesian Optimized Long Short-Term Memory (BO-LSTM) neural network to predict the electric, heating and cooling power load for the short and mid-term operation. The Bayesian optimization algorithm is performed to automate hyperparameter tuning to improve results, so avoiding different hyperparameters may lead to considerable differences in the performance of other deep learning network architecture in some sense. The developed model is validated on one actual RIES in China for data collected in a year. The simulation results of the proposed BO-LSTM indicate the effectiveness and excellent prediction accuracy in comparison with other traditional models, such as autoregressive integrated moving average model (ARIMA), long short-term memory (LSTM) and convolutional neural network (CNN).