<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>5972ad9e-0f7b-421e-9981-1fca6fc7b46a</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijitee.F9561.0512623</doi>
<citation_list><citation key="ref0"><doi>10.1186/s41601-016-0016-y</doi><unstructured_citation>Z. Li et al., &quot;Short-term wind power forecast with error correction,&quot; Protection Control Modern Power Syst., vol. 1, no. 1, pp. 1-8, 2016.</unstructured_citation></citation><citation key="ref1"><doi>10.1109/TSTE.2014.2381224</doi><unstructured_citation>J. Liu,W. Fang, X. Zhang, and C. Yang, &quot;An improved photovoltaic power forecasting model with the assistance of aerosol index data,&quot; IEEE Trans.Sustainable Energy, vol. 6, no. 2, pp. 434-442, Apr. 2015. [CrossRef]</unstructured_citation></citation><citation key="ref2"><doi>10.1109/TPWRS.2014.2329489</doi><unstructured_citation>F. Sulla, M. Koivisto, and J. Seppanen, &quot;Statistical study and forecasting of damping in the Nordic power system,&quot; IEEE Transactions on Power Systems, vol. 30, no. 1, Jan. 2015, pp. 306-315. [CrossRef]</unstructured_citation></citation><citation key="ref3"><doi>10.1109/IREP.2013.6629368</doi><unstructured_citation>M. Kezunovic, L. Xie, and S. Grijalva, &quot;The role of big data in improving power system operation and protection,&quot; in Proc. IEEEIREP Symp.Rethymnon Bulk Power Syst. Dyn. Control-IX Optim.Security ControlEmerging Power Grid, 2013, pp. 1-9. [CrossRef]</unstructured_citation></citation><citation key="ref4"><doi>10.1016/j.asoc.2011.07.001</doi><unstructured_citation>D. X. Niu, H. F. Shi, and D. D. Wu, &quot;Short-term load forecasting using Bayesian neural networks learned by hybrid Monte Carlo algorithm,&quot;Appl. Soft Comput., vol. 12, no. 6, pp. 1822-1827, 2012. [CrossRef]</unstructured_citation></citation><citation key="ref5"><doi>10.1109/TPWRS.2010.2096829</doi><unstructured_citation>M. Rejc and M. Panto, &quot;Short-term transmission-loss forecast for the Slovenian transmission power system based on a fuzzy-logic decision technique,&quot; IEEE Trans. Power Syst., vol. 26, no. 3, March 2011, pp. 1511-1521. [CrossRef]</unstructured_citation></citation><citation key="ref6"><doi>10.1109/TPWRS.2010.2052638</doi><unstructured_citation>Y.Wang, Q. Xia, and C. Kang, &quot;Secondary forecasting based on deviation analysis for short-term load forecasting,&quot; IEEE Trans. Power Syst., vol. 26, no. 2, pp. 500-507, May 2011. [CrossRef]</unstructured_citation></citation><citation key="ref7"><doi>10.1016/j.enconman.2008.02.021</doi><unstructured_citation>A. Azadeh, M. Saberi, S. F. Ghaderi, and V. Ebrahimipour, &quot;Improved estimate of power demand function by the integration of fuzzy system and data mining technique,&quot; Energy Convers.Manage., vol. 49, no. 8, pp. 2165-2177, 2008. [CrossRef]</unstructured_citation></citation><citation key="ref8"><doi>10.1109/TPWRS.2006.873099</doi><unstructured_citation>K. B. Song et al., &quot;Hybrid load forecasting method with analysis of temperature sensitivities,&quot; IEEE Trans. Power Syst., vol. 21, no. 2, pp. 869-876, May 2006. [CrossRef]</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
