Abstract
The paper considers the problem of forecasting solar insolation. Due to a large number of factors that are difficult to predict, this problem is complex and difficult like other problems where parameters depend on weather or climate. Despite such factors do not significantly affect the parameters under study, they create an essential bias. The problem is also characterized by a significant amount of information, which should be processed to obtain a reliable forecast. The paper discusses relevant machine learning methods and analyzes methods for reducing the training sample to make quality and reliable predictions of solar insolation under the weather and climatic changes. Reducing the size of the training sample allowed testing a significant number of models and optimizing the hyperparameters of these models, which made it possible to identify the most accurate model, which graph is close to ideal. The effect of reducing the size of the training sample has been measured related to the speed and accuracy of machine learning algorithms. It is shown that the use of clustered data sampling for models that prone to overfitting can improve the accuracy of these models. It is shown that using the method of clustering to reduce data samples can also reduce variance in the training dataset. The reduced data sample helps to perform optimization of hyperparameters because the accuracy of different models is preserved over a wide range of input data. The results obtained can be successfully applied in control systems for large objects of solar energy generation under conditions of fast and frequent weather changes as well as slow climatic changes.
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References
Archives: NSRDB (n.d.). https://nsrdb.nrel.gov/data-sets/archives.html
Abirami, S., Chitra, P.: Energy-efficient edge based real-time healthcare support system. Adv. Comput. 117(1), 339–368 (2020). https://doi.org/10.1016/bs.adcom.2019.09.007
Aggarwal, S.K., Saini, L.M.: Solar energy prediction using linear and non-linear regularization models: a study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest. Energy 78, 247–256 (2014). https://doi.org/10.1016/j.energy.2014.10.012
Ahmad, A., Anderson, T.N., Lie, T.T.: Hourly global solar irradiation forecasting for New Zealand. Solar Energy 122, 1398–1408 (2015). https://doi.org/10.1016/j.solener.2015.10.055
Chung, M.H.: Estimating solar insolation and power generation of photovoltaic systems using previous day weather data. Adv. Civil Eng. 2020, 1–13 (2020). https://doi.org/10.1155/2020/8701368
de Araujo, J.M.S.: Performance comparison of solar radiation forecasting between WRF and LSTM in Gifu. Japan. Environ. Res. Commun. 2(4), 045002 (2020). https://doi.org/10.1088/2515-7620/ab7366
Diez, F.J., Navas-Gracia, L.M., Chico-Santamarta, L., Correa-Guimaraes, A., Martínez-Rodríguez, A.: Prediction of horizontal daily global solar irradiation using artificial neural networks (ANNs) in the castile and León region, Spain. Agronomy 10, 96 (2020). https://doi.org/10.3390/agronomy10010096
Fürnkranz, J.: Decision tree. In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-30164-8_204
Ghojogh, B., Crowley, M.: Principal sample analysis for data reduction. In: IEEE International Conference on Big Knowledge (ICBK). pp. 350–357 (2018). https://doi.org/10.1109/icbk.2018.00054
Gorban, A.N., Tyukin, I.Y.: Blessing of dimensionality: mathematical foundations of the statistical physics of data. Philos. Trans. R. Soc. Ser. A Math. Phys. Eng. Sci. 376(2118), 20170237 (2018). https://doi.org/10.1098/rsta.2017.0237
Harrell, F.E., Jr.: Regression Modeling Strategies. SSS, Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19425-7
Ingrassia, S., Morlini, I.: Neural network modeling for small datasets. Technometrics 47(3), 297–311 (2005). https://doi.org/10.1198/004017005000000058
Jeon, B.K., Kim, E.J.: Next-day prediction of hourly solar irradiance using local weather forecasts and LSTM trained with non-local data. Energies 13, 5258 (2020). https://doi.org/10.3390/en13205258
Jeon, B.K., Kim, E.J., Shin, Y., Lee, K.H.: Learning-based predictive building energy model using weather forecasts for optimal control of domestic energy systems. Sustainability 11, 147 (2019). https://doi.org/10.3390/su11010147
Khanmirza, E., Esmaeilzadeh, A., Markazi, A.H.D.: Predictive control of a building hybrid heating system for energy cost reduction. Appl. Soft Comput. 46, 407–423 (2016). https://doi.org/10.1016/j.asoc.2016.05.005
Pal, R.: Overview of predictive modeling based on genomic characterizations. In: Predictive Modeling of Drug Sensitivity, pp. 121–148 (2017). https://doi.org/10.1016/B978-0-12-805274-7.00006-3
Pestov, V.: Is the k-NN classifier in high dimensions affected by the curse of dimensionality? Comput. Math. App. 65(10), 1427–1737 (2013). https://doi.org/10.1016/j.camwa.2012.09.011
Premalatha, N., Valan Arasu, A.: Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. J. Appl. Res. Technol. 14(3), 206–214 (2016). https://doi.org/10.1016/j.jart.2016.05.001
Qing, X., Niu, Y.: Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148, 461–468 (2018). https://doi.org/10.1016/j.energy.2018.01.177
Samimi, J.: Estimation of height-dependent solar irradiation and application to the solar climate of Iran. Solar Energy 52, 401–409 (1994). https://doi.org/10.1016/0038-092X(94)90117-K
Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1177–1178 (2010). https://doi.org/10.1145/1772690.1772862
Sharma, V., Yang, D., Walsh, W., Reindl, T.: Short term solar irradiance forecasting using a mixed wavelet neural network. Renew. Energy 90, 481–492 (2016). https://doi.org/10.1016/j.renene.2016.01.020
Srivastava, S., Lessmann, S.: A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Solar Energy 162, 232–247 (2018). https://doi.org/10.1016/j.solener.2018.01.005
Varoquaux, G., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., Mueller, A.: Scikit-learn. GetMobile. Mobile Comput. Commun. 19(1), 29–33 (2015). https://doi.org/10.1145/2786984.2786995
Vindel, J.M., Polo, J., Zarzalejo, L.F.: Modeling monthly mean variation of the solar global irradiation. J. Atmos. Solar-Terr. Phys. 122, 108–118 (2015). https://doi.org/10.1016/j.jastp.2014.11.008
Wang, F., Mi, Z., Su, S., Zhao, H.: Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies 5, 1355–1370 (2012). https://doi.org/10.3390/en5051355
Wilcox, S., Marion, W.: User’s manual for TMY3 data sets (revised) (2008). https://doi.org/10.2172/928611
Zollanvari, Amin, Alex Pappachen, J., Sameni, R.: A theoretical analysis of the peaking phenomenon in classification. J. Classif. 37(2), 421–434 (2019). https://doi.org/10.1007/s00357-019-09327-3
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Povod, Y., Sherstjuk, V., Zharikova, M. (2022). Reduction of Training Samples in Solar Insolation Prediction Under Weather and Climatic Changes. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_22
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