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It is desirable to predict the occurrence of disturbance during power consumption management such as the Real‐Time Pricing (RTP) adaptive control in the future smart grid. Maintenance operation is one of the most power‐consuming operations of multitype air‐conditioners in office buildings. Because the operation occurs stochastically and abruptly, it is extremely difficult to predict the occurrence timing from the RTP control system's point of view. In this research, we propose a prediction model that forecasts the impending occurrence of the operation. The model was implemented as a Long Short‐Term Memory (LSTM) neural network because the occurrence timing depends on the air‐conditioner's operation history. The prediction accuracy was evaluated and then the contribution of the prediction model to the RTP control goodness score was estimated. In our example case, even though prediction accuracy was not perfect, the improvement of the score was estimated by up to 21%. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Recently, smart grid Real-Time Pricing (RTP), which changes electricity unit price for every several 10 min, has been receiving attention. The RTP control system uses a Neural Network (NN) prediction model on the response of power consumption against the power limitation commands for power saving. In this research, we propose a new NN training method using the data that are observed during short-term.

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キーワード： 地質観察，不連続面，LIDAR，点群，DiAna，可変格子分割 LIDAR (Laser Imaging Detection and Ranging) detects a rock mass surface as a point cloud, and threedimensional configurations of the rock mass can be obtained from the point cloud. In previous studies, algorithms to estimate discontinuities from a point cloud have been developed. In those algorithms, it is necessary to determine geological parameters in advance. DiAna(Discontinuity Analysis) is a Matlab tool which was developed for geostructural analysis of rock mass discontinuities. It is a semi-automatic tool. DiAna segments a point cloud into bounding boxes to estimate the surface of a rock mass. However, an expert's skills necessary to determine the appropriate size of the bounding boxes for DiAna. We developed the VBS (Variable-Box Segmentation) algorithm to determine the appropriate box size depending on the location of the point cloud and to estimate the surface of a rock mass. The VBS algorithm consists principally of three processes: large box segmentation, small box segmentation, and merging. The small boxes are merged to obtain an appropriate box size. The surface of the rock mass is estimated using the point cloud in the box. The performance of the VBS algorithms was evaluated using point clouds obtained by a geological survey. For evaluation, we estimated reference rock mass surfaces manually using the point cloud and geological sketches by geologists. Similarities among the respective reference surfaces and the surfaces estimated using the VBS algorithm were measured. Similarities among the respective reference surfaces and the surfaces estimated using the DiAna algorithm were also measured. The similarities among them were compared using standard competition ranking. The results of comparison showed that the VBS algorithm estimated planes more accurately for the reference planes than the DiAna algorithm. Therefore, the VBS algorithm determines appropriate box sizes automatically depending on the location of the point cloud and estimates the surface appropriately. スキャンライン上天蓋部 (Fig. 4 橙 ) では ……………………………………………… ( .