LSTM Prediction on Sudden Occurrence of Maintenance Operation of Air-Conditioners in Real-Time Pricing Adaptive Control

Abstract

Predicting the occurrence of embedded maintenance operations in building multi-type air-conditioners is desirable during the Real-Time Pricing (RTP) scheme in the future smart grid. The maintenance operation is a kind of a high priority embedded control for complicated refrigerant circuit network in an office building. Since it suddenly operates and consumes large electric power, it becomes a big disturbance from the viewpoint of RTP control system in the cloud. In this research, we propose a model that forecasts the sudden occurrence of the maintenance operation. Since the occurrence of the operation depends on the refrigerant circuit operation history, the model is implemented as a Long Short Term Memory (LSTM) neural network. An accuracy of prediction was evaluated and then simulation experiments showed the improvement by 27% on RTP adaptive control result.