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Proposal of New Hybrid Short-term Weather Data Forcasting Method combining Existing SARIMA and GRU
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As the annual average temperature continues to rise due to climate change caused by global warming, the incidence of heat diseases and the number of deaths are also increasing, which is expected to require various alternatives and research. In this study, the average temperature rise-related variables are extracted through statistical analysis for Wonju City, where the average temperature increase rate and change are high, and the average temperature is predicted by utilizing deep learning-based LSTM and GRU based on the extracted variables. Three models were extracted through correlation and regression analysis for 26 variables collected based on prior research consideration, based on which LSTM and GRU analysis were conducted. The analysis showed the lowest MSE of LSTM – 0.4399(2.94°C), GRU – 0.4444(2.97°C) in the third model with 12 variables, with little MAE difference between validation and test data. This study is significant in that it extracted variables through statistical analysis and predicted average temperature rise using deep learning as a data acquisition method for adapting the annual average temperature rise problem. In addition, it is expected that urban space factors that affect the average temperature rise in Wonju City will be extracted along with predicting the trend of average temperature change, and appropriate measures will be prepared to take into account regional impact factors, not uniform climate change adaptation.
Solar power generation influenced by the weather. Using the weather forecast information, it is possible to predict the short-term solar power generation in the future. However, in limited circumstances such as islands or mountains, it can not be use weather forecast information by the disconnection of the network, it is impossible to use solar power generation prediction model using weather forecast. Therefore, in this paper, we propose a system that can predict the short-term solar power generation by using the information that can be collected by the system itself. We developed a short-term prediction model using the prior information of temperature and power generation amount to improve the accuracy of the prediction. We showed the usefulness of proposed prediction model by applying to actual solar power generation data.
This research is to find out the method appropriate for the forecasting of passennger demand using seasonal ARIMA model and efficient operation in Jeju National Airport. Time series monthly data for the investigation were collected ranging from January 2003 to December 2011. A total of 108 observations were used for data analysis. Research findings showed that the multiplicative seasonal ARIMA(0.1.2)(0.1.1)12 model is appropriate model. The number of passengers in Jeju National Airport will continue to rise, it was expected to surpass 20 million people.
Self-study machine learning + deep learning”, Hanbit Media
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Performance Evaluation of LSTM and GRU using TensorFlow
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Time-series data processing and analysis used in practice
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Subway Demand Forecast using Seasonal Autoregressive integrated Moving Average”, Korean Society Of Transportation
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Provide public data integration system
- Data Portal