|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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