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

EasyChair Preprint 6108, version 1

Abstract

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 ShortTerm 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).

Keyphrases: Bayesian optimization, Long Short-Term Memory, deep learning, load prediction, regional integrated energy system

@booklet{EasyChair:6108,
  author    = {Ang Xuan and Shihao Tian},
  title     = {A Regional Integrated Energy System Load Prediction Method Based on Bayesian Optimized Long-Short Term Memory Neural Network},
  howpublished = {EasyChair Preprint 6108},
  year      = {EasyChair, 2021}}