Multi-energy load forecasting model based on bi-directional gated recurrent unit multi-task neural network

Zhijie Zheng,Liang Feng,Yi Sun,Xian Wang,Rui Liu,Xuan Wang,P Siano,Q Li

doi:10.1051/e3sconf/202125602032

Zhijie Zheng, Liang Feng + Show 6 more

Open Access

https://doi.org/10.1051/e3sconf/202125602032

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Abstract

The complex coupling, coordination and complementarity of different energy in the integrated energy system puts forward higher requirements for the technology of multi-energy load forecasting. To this end, this paper proposes a novel multi-energy load forecasting model based on bi-directional gated recurrent unit (BiGRU) multi-task neural network. Firstly, through the correlation analysis, an effective multi-energy load input data set is constructed. Secondly, the input data set is utilized to train the BiGRU and master the evolution laws of multi-energy loads. Then, multi-task learning (MTL) is used to share the information learned by BiGRU from perspectives of different load forecasting tasks, so as to fully dig the coupling relations among various energy loads. Finally, different types of load forecasting results can be obtained. Simulation results show that BiGRU can simultaneously consider the known data of the past and the future, and it can learn more characteristic information effectively. At the same time, the proposed model utilizes MTL to carry out parallel learning and information sharing for forecasting tasks of various energy loads, which can dig the complex coupling relations among different types of loads more deeply, thus improving the forecasting accuracy of multi-energy loads.

Highlights

Nowadays, there are three major problems in the global economy: energy security, environmental pollution and climate change
In order to illustrate the superiority of the proposed model, the long short-term memory (LSTM)-recurrent neural network (RNN) model proposed in [17], the DBN model proposed in [7], the gated recurrent unit (GRU) model and the bi-directional gated recurrent unit (BiGRU) model are selected to be comparison models
Compared with GRU model, BiGRU model can get smaller values of mean absolute percentage error (MAPE) and root mean square error (RMSE) when forecasting cooling, heating and electricity loads, which indicates that BiGRU can consider both past and future information, and learn the influence of existing information on current state more effectively, obtaining the higher forecasting accuracy of multienergy loads

Summary

Introduction

There are three major problems in the global economy: energy security, environmental pollution and climate change. Further research is still needed to fully dig coupling relations among various energy loads and improve the accuracy of multi-energy load forecasting. On the basis of existing research, this paper proposes a multi-energy load forecasting based on BiGRU multitask neural network. The proposed model utilizes BiGRU as the basic unit to construct a multi-energy load forecasting model, so that it can dynamically model multi-energy loads of forecasted time from the forward and backward directions. Multi-task learning (MTL) is introduced into the proposed model, which makes the tasks of cooling, heating and electricity loads forecasting can be shared and learned in parallel, so as to dig the coupling relations among different types of loads more deeply and improve the forecasting accuracy of multi-energy loads

BiGRU multi-task neural network

Simulation

Processing of input data set

Results

Conclusion