Journal of The Korean Society of Living Environmental System ISSN:1226-1289(Print) 한국생활환경학회

A. S. H. R. A. E. (2014). Measurement of energy, demand, and water savings. ASHRAE Guideline, 4, 1-150.

Ahn, Y., Lee, Y. J., Oh, E. J., & Kim, B. S. (2021). Prediction of building power consumption in the short-term through solar radiation calculation based on ultra-short weather forecast data. Korean Journal of Air-Conditioning and Refrigeration Engineering, 33(3), 113-121.

Al-Hajj, R., Assi, A., & Fouad, M. M. (2019). Stacking-based ensemble of support vector regressors for one-day ahead solar irradiance prediction. In 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), 428-433.

Chung, M. H. (2020). Estimating solar insolation and power generation of photovoltaic systems using previous day weather data. Advances in Civil Engineering, 2020(1), 8701368.

Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.

Compare the Effect of Different Scalers on Data with Outliers. (n.d.). In Scikit-learn. Retrieved July 21, 2024 from https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html

Ehuia-Oller, P., Martinez-Marino, S., Granada-Alvarez, E., & Febrero-Garrido, L. (2021). Empirical validation of a multizone building model coupled with an air flow network under complex realistic situations. Energy and Buildings, 249, 111197.

Ekici, B. B. (2014). A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems. Measurement, 50, 255-262.

Grossi, E., & Buscema, M. (2007). Introduction to artificial neural networks. European Journal of Gastroenterology & Hepatology, 19(12), 1046-1054.

Jeon, W. Y., Jo, S. M., & Cho, I. H. (2019). Study on estimating the uncertainty of net renewable energy demand in 2030 using probabilistic models for solar and wind energy. Journal of The Korean Society for New and Renewable Energy, 15(4), 28-38.

Korea Climate Characteristics. (n.d.). In Korea Meteorological Administration. Retrieved July 21, 2024 from https://www.weather.go.kr/w/climate/statistics/regional-char.do

Korea Energy Agency. (2022). Production of supply statistics for renewable energy center.

Krenker, A., Bešter, J., & Kos, A. (2011). Introduction to the artificial neural networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, 1-18.

Krishnan, N., & Kumar, K. R. (2024). Solar radiation forecasting using gradient boosting based ensemble learning model for various climatic zones. Sustainable Energy, Grids and Networks, 101312.

Liu, X., Liu, X., Zhang, R., Luo, D., Xu, G., & Chen, X. (2022). Securely computing the manhattan distance under the malicious model and Its applications. Applied Sciences, 12(22), 11705.

Park, J. H., & Ahn, K. M. (2023). Analysis of temperature differences between regions in South Korea using functional data analysis. Journal of the Korea Data & Information Science Society, 34(6), 957-966.

Park, J., Hong, S. H., Yeon, S. H., Seo, B. M., & Lee, K. H. (2023). Predictive model for solar insolation using the deep learning technique. International Journal of Energy Research, 2023(1), 3525651.

Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.

Shadab, A., Ahmad, S., & Said, S. (2020). Spatial forecasting of solar radiation using ARIMA model. Remote Sensing Applications: Society and Environment, 20, 100427.

Sin, J. T., & Hwang, H. K. (2022). Recent trends and implications of AI uncertainty quantification. National Information Society Agency, 14, 1-24.

Son, W. B. (2023). Current status of solar power generation forecasting technology in preparation for the expansion of renewable energy deployment. Information and Communications Magazine, 40(12), 20-26.

Wang, Z., Zhang, Y., Li, G., Zhang, J., Zhou, H., & Wu, J. (2024). A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model. Renewable Energy, 226, 120367.

Woo, T. K., So, M. S., Kang, S. Y., & Sin, J. H. (2024). Development of a solar power generation prediction model utilizing photovoltaics and climate data. Society for Computational Design and Engineering, 29(1), 33-41.

Xia, P., Zhang, L., & Li, F. (2015). Learning similarity with cosine similarity ensemble. Information Sciences, 307, 39-52.

Yu, J. H., Kim, S. J., Jang, B. H., & Woo. S. M. (2022). Renewable energy prediction market participation type photovoltaics prediction model development. Journal of The Korean Solar Energy Society, 42(6), 1-13.