21st EANN 2020, 5 -7 June 2020, Greece

LSTM Neural Network for Fine-Granularity Estimation on Baseline Load of Fast Demand Response

Shun Matsukawa, Keita Suzuki, Chuzo Ninagawa, Junji Morikawa, Seiji Kondo


  In the future power system called Smart Grid, power generators using renew-able energies will be widely introduced to the power grid and required to bal-ance supply and demand on the grid quickly. Therefore, fast automated de-mand response (FastADR) that contributes to balance the power grid from demand side through electrical facilities like building air conditioner are fo-cused recently. When electric grid operator will pay incentive to aggregators of the demand side, it is important to estimate accurate baseline load. How-ever, the FastADR must returns quick response by unit of seconds or minute (fine-granularity), therefore it is difficult to estimate baseline load accurately using conventional method. In this research, the baseline load estimation model for air-con time-series data is constructed using long short-term memory (LSTM) neural network, and compared with multilayer perceptron (MLP) neural network model for baseline load estimation. The training and evaluating time-series data is generated by air-con simulator (AE) carried out on the virtual building. In the estimation results using data that were simulat-ed for a month, the average estimation error of the LSTM model is 2.7% and of the MLP model is 5.3%. Therefore, the LSTM model is more effective for baseline estimation than the MLP model. However, data in various situations are required.  

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