## 5 Conclusions  
Among the various renewable energy sources, solar energy is being increasingly supported by the Korean government. Solar power systems have been installed in numerous locations such as homes, buildings, and factories, and electricity has been supplied accordingly. Forecasting solar power is essential for a smooth, reliable power supply; however, these forecasts are strongly influenced by meteorological factors. This study analyzed six meteorological factors with the goal of accurately predicting solar power. A total of 22 multivariate models were proposed to assess various combinations of the six meteorological factors: solar radiation (SR), sunlight, humidity, temperature, cloud  
Figure 11: Comparison of the best and worst models for the proposed and traditional LSTM by season. Figure 12: Comparisons between the monthly and yearly averages according to the RMSE values and differences determined by the models. Figure 13: Comparison between the monthly and yearly averages according to the computation time and differences determined by the models. cover (CC), and wind speed (WS). Analysis of the multivariate numerical models included modifications of the traditional long short-term memory (LSTM) method and application of the proposed LSTM. The main results are as follows. (1) The six meteorological factors affect the solar power forecast in the following descending order: SR, sunlight, WS, temperature, CC, and humidity. SR has the greatest influence on solar power forecasting, while humidity has effectively no influence on the solar power forecast. (2) Among the 22 models, model 16 is superior, regardless of the characteristics of abnormal weather in Korea. The meteorological factors incorporated in model 16 are SP, SR, sunlight, and WS. (3) The remaining models were ranked according to medium- and long-term solar power forecasting accuracy. Models 6, 7, 9, 13, and 19 showed reasonable performance for long-term forecasting, whereas models 1, 5, 8, and 10 were not suitable. All models except 1, 5, 8, and 10 are appropriate for medium-term solar power forecasting. (4) The root mean square error and the mean absolute difference of the proposed LSTM are superior to those of the traditional LSTM. (5) The calculation time increases as the number of variables in a model increases. (6) The proposed medium- to long-term photovoltaic (PV) solar power forecasting contributes to efficient power consumption, demand response, and a smooth power supply for buildings. The limitations of the article are as follows. (1) Attaching the weather sensors (temperature, humidity, WS, SR, etc.) to the solar installation site to acquire meteorological and solar power data simultaneously will improve the accuracy of the solar power forecasts. However, this research was limited to collecting meteorological data at an established government weather station some distance from the site. (2) The proposed algorithm has been applied to the various sites for less than a year; however, seasonal measurements could not be repeated. Future work will collect meteorological data simultaneously at the site where PV is installed, and measurements will extend for more than a year. The methodology will also be applied to other solar power plants to verify the proposed model. [PERSON] supervised and wrote the article and implemented the algorithm. [PERSON] surveyed related research and background. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This article was supported by research fund from Honam University, 2020. The authors declare no conflict of interest.