<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.4 20241031//EN"
        "https://jats.nlm.nih.gov/publishing/1.4/JATS-journalpublishing1-4.dtd">
<article  article-type="research-article"        dtd-version="1.4">
            <front>

                <journal-meta>
                                                                <journal-id>tepes</journal-id>
            <journal-title-group>
                                                                                    <journal-title>Turkish Journal of Electrical Power and Energy Systems</journal-title>
            </journal-title-group>
                                        <issn pub-type="epub">2791-6049</issn>
                                                                                            <publisher>
                    <publisher-name>Association of Turkish Electricity Industry</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.5152/tepes.2024.24008</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Electrical Energy Transmission, Networks and Systems</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>Elektrik Enerjisi Taşıma, Şebeke ve Sistemleri</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                        <article-title>Hybrid Artificial Intelligence Techniques for Enhanced Electricity Outage Prediction and Management in Distribution Networks</article-title>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                <name>
                                    <surname>Avcı</surname>
                                    <given-names>Ezgi</given-names>
                                </name>
                                                                    <aff>TED UNIVERSTY, DEPARTMENT OF APPLIED DATA SCIENCE</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20240624">
                    <day>06</day>
                    <month>24</month>
                    <year>2024</year>
                </pub-date>
                                        <volume>4</volume>
                                        <issue>2</issue>
                                        <fpage>63</fpage>
                                        <lpage>73</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20240416">
                        <day>04</day>
                        <month>16</month>
                        <year>2024</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20240516">
                        <day>05</day>
                        <month>16</month>
                        <year>2024</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2021, Turkish Journal of Electrical Power and Energy Systems</copyright-statement>
                    <copyright-year>2021</copyright-year>
                    <copyright-holder>Turkish Journal of Electrical Power and Energy Systems</copyright-holder>
                </permissions>
            
                                                                                                <abstract><p>This paper investigates outage management in electricity distribution networks through the application of artificial intelligence techniques. The core of the system utilizes a diverse dataset compiled from outage management system records, weather forecasts, and geographical data to predict potential electricity outages. The data is rigorously analyzed to determine correlations between various weather conditions and outage occurrences, with particular emphasis on the impact of wind speed and storm conditions. The predictive model, a cornerstone of this research, employs a hybrid artificial intelligence algorithm that integrates outputs from convolutional neural networks, recursive neural networks, and extreme gradient boosting. The predictions are further refined using a feedforward neural network and distributed to specific districts based on historical data trends. Comparative analysis against a naive model based on historical averages highlights the superior performance of the hybrid model, showcasing its reduced error rates and enhanced predictive accuracy. This decision support system not only provides reliable outage predictions but also facilitates more effective management strategies, thus improving operational efficiencies and customer service in electricity distribution. The findings underscore the potential of advanced analytics in transforming utility management and pave the way for further innovations in smart grid technology and outage prevention strategies.</p></abstract>
                                                            
            
                                                            <kwd-group>
                                                    <kwd>Artificial intelligence</kwd>
                                                    <kwd>  decision support systems</kwd>
                                                    <kwd>  electricity distribution</kwd>
                                                    <kwd>  outage management</kwd>
                                            </kwd-group>
                            
                                                                                                                        </article-meta>
    </front>
    <back>
                            <ref-list>
                                    <ref id="ref1">
                        <label>1</label>
                        <mixed-citation publication-type="journal">1. International Energy Agency, World Energy Outlook 2023, White Paper. IEA, 2023.</mixed-citation>
                    </ref>
                                    <ref id="ref2">
                        <label>2</label>
                        <mixed-citation publication-type="journal">2. Turkish Association for Energy Economics, Available, 2017. Available: http: //www .trae e.org /tr/.</mixed-citation>
                    </ref>
                                    <ref id="ref3">
                        <label>3</label>
                        <mixed-citation publication-type="journal">3. Eaton Corp., Blackout Tracker United States Annual Report 2018, 2018, Power Outage Annual Report.</mixed-citation>
                    </ref>
                                    <ref id="ref4">
                        <label>4</label>
                        <mixed-citation publication-type="journal">4. P. H. Larsen, K. H. LaCommare, J. H. Eto, and J. L. Sweeney, Assessing Changes in the Reliability of the U.S. Electric Power System, Report no. LBNL-188741. Berkeley, CA: Lawrence Berkeley National Laboratory, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref5">
                        <label>5</label>
                        <mixed-citation publication-type="journal">5. R. E. Brown, S. Gupta, R. D. Christie, S. S. Venkata, and R. Fletcher, “Distribution system reliability assessment: Momentary interruptions and storms,” IEEE Trans. Power Deliv., vol. 12, no. 1, pp. 53–63, 1997.</mixed-citation>
                    </ref>
                                    <ref id="ref6">
                        <label>6</label>
                        <mixed-citation publication-type="journal">6. N. Balijepalli, S. S. Venkata, C. W. Richter, R. D. Christie, and V. J. Longo, “Distribution system reliability assessment due to lightning storms,” IEEE Trans. Power Deliv., vol. 20, no. 3, pp. 2153–2159, 2005.</mixed-citation>
                    </ref>
                                    <ref id="ref7">
                        <label>7</label>
                        <mixed-citation publication-type="journal">7. Y. Zhou, A. Pahwa, and S. S. Yang, “Modeling weather-related failures of overhead distribution lines,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1683–1690, 2006.</mixed-citation>
                    </ref>
                                    <ref id="ref8">
                        <label>8</label>
                        <mixed-citation publication-type="journal">8. S. Yang et al., “Failure probability estimation of overhead transmission lines considering the spatial and temporal variation in severe weather,” J. Mod. Power Syst. Clean Energy, vol. 7, no. 1, pp. 131–138, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref9">
                        <label>9</label>
                        <mixed-citation publication-type="journal">9. D. A. Reed, “Electric utility distribution analysis for extreme winds,” J. Wind Eng. Ind. Aerodyn., vol. 96, no. 1, pp. 123–140, 2008.</mixed-citation>
                    </ref>
                                    <ref id="ref10">
                        <label>10</label>
                        <mixed-citation publication-type="journal">10. H. Liu, R. A. Davidson, D. V. Rosowsky, and J. R. Stedinger, “Negative binomial regression of electric power outages in hurricanes,”, J. Infrastruct. Syst., vol. 11, no. 4, 258–267, 2005.</mixed-citation>
                    </ref>
                                    <ref id="ref11">
                        <label>11</label>
                        <mixed-citation publication-type="journal">11. H. Liu, R. A. Davidson, and T. V. Apanasovich, “Statistical forecasting of electric power restoration times in hurricanes and ice storms,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 2270–2279, 2007.</mixed-citation>
                    </ref>
                                    <ref id="ref12">
                        <label>12</label>
                        <mixed-citation publication-type="journal">12. S. D. Guikema, S. M. Quiring, and S. R. Han, “Prestorm estimation of Hurricane Damage to electric power distribution systems,” Risk Anal., vol. 30, no. 12, pp. 1744–1752, 2010.</mixed-citation>
                    </ref>
                                    <ref id="ref13">
                        <label>13</label>
                        <mixed-citation publication-type="journal">13. S. D. Guikema, and S. M. Quiring, “Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data,”, Reliability Engineering &amp; System Safety, vol. 99, 178–182, 2012.</mixed-citation>
                    </ref>
                                    <ref id="ref14">
                        <label>14</label>
                        <mixed-citation publication-type="journal">14. S. D. Guikema, R. Nateghi, S. M. Quiring, A. Staid, A. C. Reilly, and M. Gao, “Predicting Hurricane Power outages to support storm response planning,” IEEE Access, vol. 2, pp. 1364–1373, 2014.</mixed-citation>
                    </ref>
                                    <ref id="ref15">
                        <label>15</label>
                        <mixed-citation publication-type="journal">15. D. B. McRoberts, S. M. Quiring, and S. D. Guikema, “Improving Hurricane Power outage prediction models through the inclusion of local environmental factors,” Risk Anal., vol. 36, no. 4, pp. 740–755, 2016.</mixed-citation>
                    </ref>
                                    <ref id="ref16">
                        <label>16</label>
                        <mixed-citation publication-type="journal">16. J. He, D. W. Wanik, E. N. Anagnostou, M. Astitha, M. E. B. Frediani, and B. M. Hartman, “Nonparametric tree-based predictive modeling of storm outages on an electric distribution network,” Risk Anal., vol. 36, no. 12, pp. 2100–2117, 2016.</mixed-citation>
                    </ref>
                                    <ref id="ref17">
                        <label>17</label>
                        <mixed-citation publication-type="journal">17. P. Kankanala, S. Das, and A. Pahwa, “ADABOOST(+): An ensemble learning approach for estimating weather-related outages in distribution systems,” IEEE Trans. Power Syst., vol. 29, no. 1, pp. 359–367, 2014.</mixed-citation>
                    </ref>
                                    <ref id="ref18">
                        <label>18</label>
                        <mixed-citation publication-type="journal">18. D. Thukaram, H. P. Khincha, and H. P. Vijaynarasimha, “Artificial neural network and support vector machine approach for locating faults in radial distribution systems,” IEEE Trans. Power Deliv., vol. 20, no. 2, pp. 710–721, 2005.</mixed-citation>
                    </ref>
                                    <ref id="ref19">
                        <label>19</label>
                        <mixed-citation publication-type="journal">19. M. Jamil, Md. A. Kalam, A. Q. Ansari, and Rizwan, “Wavelet-FFNN Based fault Location Estimation of a Transmission Line,” Electr. Eng. Res., vol. 1, pp. 77–82, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref20">
                        <label>20</label>
                        <mixed-citation publication-type="journal">20. S. Wang, J. Cao, and P. S. Yu, “Deep learning for spatio-temporal data mining: A survey,”, IEEE Trans. Knowl. Data Eng., vol. 34, No. 8, 3681–3700, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref21">
                        <label>21</label>
                        <mixed-citation publication-type="journal">21. X. Shi et al., Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref22">
                        <label>22</label>
                        <mixed-citation publication-type="journal">22. X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W. K. Wong, and W. WOO, Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, 2015.</mixed-citation>
                    </ref>
                            </ref-list>
                    </back>
    </article>
