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This paper proposes a neural 5G traffic generation model and a methodology for calculating spectrum requirements of private 5G networks to provide various industrial communication services. To accurately calculate the spectral requirements, it is necessary to analyze the actual data volume and traffic type of industrial cases. However, since there is currently no suitable traffic model to test loads in private 5G networks, we have developed a generative adversarial network (GAN)-based traffic generator that can generate realistic traffic by learning actual traffic traces collected by mobile network operators. In addition, in the case of industrial applications, probability-based traffic models were also used in parallel because there were not enough real data to be learned. The proposed 5G traffic generation model is combined with the proposed 5G spectrum calculation methodology, enabling more accurate spectrum requirements calculation through traffic simulation similar to the real-life environment. In this paper, spectrum requirements are calculated differently according to the two duplexing types of frequency division duplexing (FDD) and time division duplexing (TDD). As a guide for companies that will provide advanced wireless connectivity for a wide variety of vertical industries using 5G networks, we simulated eight use cases defined in the 5G Alliance for Connected Industries and Automation (ACIA) white paper. Spectrum requirements were calculated under the various simulation conditions considering varying traffic loads, deployment scenarios, and duplexing schemes. Various simulation results have confirmed that a bandwidth of at least 22.0 MHz to a maximum of 397.8 MHz is required depending on the deployment scenario.

To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS.

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