Most of the positioning systems nowadays require GPS. In indoor environments, however, GPS signals cannot be received properly due to the obstructions of the walls in the building. Therefore, other techniques such as RFID, Zigbee, WIFI or Bluetooth, are used based on the power loss, cost, or transmission range considerations. Our study, conducted at the AI+ Experience Center of Chung Hua University, adopts Bluetooth 5.0-based equipment and uses the received signal strength indication (RSSI) and triangulation method to perform indoor positioning. Our results show that Bluetooth positioning is more accurate in the sense that when the distance is within 1∼3 meters, the error with the actual position is about 1 meter, which is more accurate than the RFID or WIFI positioning with the error of 2∼3 meters.
With the large-scale deployment of solar PV installations, managing the efficiency of the generation system became essential. Generally, the power output is heavily influenced by solar irradiance and sky conditions which are consistently changing. Thus, the ability to accurately forecast the solar PV power is critical for optimizing the generation system, estimating revenue, sustaining profits, and ensuring the quality of service. In this paper, the authors propose a solar PV forecasting model using multiple blocks of GRUs and RNN in a cascade model combined with hierarchical clustering to improve the overall prediction accuracy of solar PV forecast. This proposed model is a combination of hierarchical clustering, the Pearson correlation coefficient for feature selection, and the cascade model with GRU layer from k-means clustering and hierarchical clustering. These results, which are evaluated using NRMSE, show that hierarchical clustering is more suitable for solar PV forecast than k-means clustering.
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