{"id":8627,"date":"2021-07-30T09:48:13","date_gmt":"2021-07-30T07:48:13","guid":{"rendered":"https:\/\/kinit.sk\/?p=8627"},"modified":"2021-07-30T09:53:19","modified_gmt":"2021-07-30T07:53:19","slug":"application-of-biologically-inspired-algorithms-to-improve-precision-in-power-load-predictions","status":"publish","type":"post","link":"https:\/\/kinit.sk\/sk\/application-of-biologically-inspired-algorithms-to-improve-precision-in-power-load-predictions\/","title":{"rendered":"Aplik\u00e1cia biologicky in\u0161pirovan\u00fdch algoritmov ako pomoc na spresnenie predikci\u00ed v energetike"},"content":{"rendered":"
\n

Marek L\u00f3derer<\/a> je na\u0161\u00edm nov\u00fdm doktorom a vo svojej dizerta\u010dnej pr\u00e1ci sa venoval met\u00f3dam predikt\u00edvnej anal\u00fdzy v oblasti energetiky. Radi by sme v\u00e1s v kr\u00e1tkosti obozn\u00e1mili s jeho v\u00fdskumom a v\u00fdsledkami.<\/p>\n<\/div>\n\n

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V s\u00fa\u010dasnosti doch\u00e1dza k rozsiahlej transform\u00e1cii energetickej siete. Sie\u0165 sa st\u00e1va viac a viac flexibilnej\u0161ou, robustnej\u0161iu, adapt\u00edvnej\u0161ou, in\u00fdmi slovami Smart. Do energetickej siete s\u00fa zav\u00e1dzan\u00e9 nov\u00e9 technol\u00f3gie a komponenty ako obnovite\u013en\u00e9 zdroje energie, ve\u013ekokapacitn\u00e9 bat\u00e9rie \u010di elektromobily (obr. 1.), ktor\u00e9 so sebou prin\u00e1\u0161aj\u00fa mnoh\u00e9 benefity, ale aj v\u00fdzvy. V\u00fdzvy t\u00fdkaj\u00face sa zabezpe\u010denia spo\u013eahlivej dod\u00e1vky energie, n\u00e1vrhu robustnej architekt\u00fary \u010di bezpe\u010dnosti zbieran\u00fdch d\u00e1t.<\/p>\n<\/div>\n\n

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Neoddelite\u013enou s\u00fa\u010das\u0165ou energetickej siete sa st\u00e1vaj\u00fa inteligentn\u00e9 mera\u010de, ktor\u00e9 v pravideln\u00fdch intervaloch zaznamen\u00e1vaj\u00fa mno\u017estvo spotrebovanej a vyrobenej energie. Nev\u00eddan\u00e9 mno\u017estvo z\u00edskavan\u00fdch \u00fadajov poskytuje priestor na v\u00fdvoj sofistikovanej\u0161\u00edch predik\u010dn\u00fdch modelov, ktor\u00e9 dok\u00e1\u017eu presnej\u0161ie predpoveda\u0165 bud\u00faci stav v sieti. V\u010faka presn\u00fdm predpovediam je mo\u017en\u00e9 efekt\u00edvnej\u0161ie vyu\u017ei\u0165 v\u00fdrobn\u00e9 zdroje, minimalizova\u0165 v\u00fddavky spojen\u00e9 s regula\u010dn\u00fdmi poplatkami za sp\u00f4soben\u00e9 v\u00fdkyvy a zni\u017eova\u0165 negat\u00edvny dopad na \u017eivotn\u00e9 prostredie. Presn\u00e9 a spo\u013eahliv\u00e9 kr\u00e1tkodob\u00e9 predikcie s\u00fa pre oblas\u0165 energetiky k\u013e\u00fa\u010dov\u00e9 a preto sa im venuje zv\u00fd\u0161en\u00e1 pozornos\u0165.<\/p>\n<\/div>\n\n

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Model of Smart grig<\/figcaption><\/figure><\/div>\n<\/div>\n\n
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Na rie\u0161enie probl\u00e9mu spo\u013eahlivej kr\u00e1tkodobej predikcie som sa rozhodol pou\u017ei\u0165 pr\u00edstup naz\u00fdvan\u00fd U\u010denie s\u00faborom met\u00f3d (Ensemble Learning), do ktor\u00e9ho som vo svojej dizerta\u010dnej pr\u00e1ci integroval r\u00f4zne vylep\u0161enia.<\/p>\n<\/div>\n\n

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Zjednodu\u0161ene sa d\u00e1 u\u010denie s\u00faborom met\u00f3d (obr. 2.) op\u00edsa\u0165 ako proces vyu\u017e\u00edvaj\u00faci sadu viacer\u00fdch modelov, ktor\u00fdch v\u00fdstupy s\u00fa kombinovan\u00e9 tak\u00fdm sp\u00f4sobom, aby vznikol \u010do najpresnej\u0161\u00ed fin\u00e1lny v\u00fdsledok. V na\u0161om pr\u00edpade je to predikcia spotreby elektrickej energie na najbli\u017e\u0161\u00ed de\u0148.<\/p>\n<\/div>\n\n

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Ensemble learning process<\/figcaption><\/figure><\/div>\n<\/div>\n\n
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Vo svojom v\u00fdskume som si zadefinoval uveden\u00fa kombin\u00e1ciu modelov ako optimaliza\u010dn\u00fd probl\u00e9m, ktor\u00fd som sa rozhodol rie\u0161i\u0165 pomocou meta-heuristick\u00fdch optimaliza\u010dn\u00fdch algoritmov, ako napr\u00edklad Genetick\u00fd algoritmus, Optimaliz\u00e1cia svorkou siv\u00fdch vlkov, Umel\u00e1 kol\u00f3nia v\u010diel, \u010di Optimaliz\u00e1cia zalo\u017een\u00e9 na bio-geografii. Tieto algoritmy boli vybran\u00e9 z d\u00f4vodu, \u017ee dosahuj\u00fa vysok\u00fa \u00faspe\u0161nos\u0165 aj pri rie\u0161en\u00ed n\u00e1ro\u010dn\u00fdch optimaliza\u010dn\u00fdch \u00faloh.<\/p>\n<\/div>\n\n

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Navy\u0161e, vyu\u017eitie meta-heuristick\u00fdch optimaliza\u010dn\u00fdch algoritmov v kombin\u00e1cii s u\u010den\u00edm s\u00faborom met\u00f3d nie je v s\u00fa\u010dasnosti plne presk\u00faman\u00e9. Okrem spresnenia predikcii sa n\u00e1m v\u010faka v\u00fdsledkom experiment\u00e1lneho overenia podarilo odpoveda\u0165 na viacero otvoren\u00fdch ot\u00e1zok, \u010d\u00edm sme posunuli poznanie v tejto oblasti. Na z\u00e1klade n\u00e1\u0161ho v\u00fdskumu u\u017e v s\u00fa\u010dasnosti pozn\u00e1me odpove\u010f, ktor\u00e1 skupina optimaliza\u010dn\u00fdch algoritmov v kombin\u00e1cii s u\u010den\u00edm s\u00faborom met\u00f3d poskytuje najlep\u0161ie v\u00fdsledky, alebo do akej miery vpl\u00fdva orez\u00e1vanie s\u00faboru met\u00f3d na celkov\u00fa presnos\u0165 predikcie.<\/p>\n<\/div>\n\n

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V z\u00e1vere\u010dnej etape dizert\u00e1cie som sa venoval aj kombin\u00e1cii modelov zalo\u017eenej na met\u00f3de dynamick\u00fdch v\u00e1h (DWM) a rozklade \u010dasov\u00fdch radov na viacer\u00e9 zlo\u017eky. Uveden\u00fd pr\u00edstup dosahuje vysok\u00fa presnos\u0165 a r\u00fdchlu adaptivitu na neo\u010dak\u00e1van\u00e9 zmeny v spotrebe energie.<\/p>\n<\/div>\n\n

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V\u00fdsledky experimentov, vykonan\u00fdch na re\u00e1lnych datasetoch spotreby elektrickej energie, ukazuj\u00fa \u00faspe\u0161n\u00e9 zn\u00ed\u017eenie chyby predikcie. Na obr\u00e1zku 3 sa nach\u00e1dza uk\u00e1\u017eka v\u00fdsledkov jedn\u00e9ho z viacer\u00fdch vykonan\u00fdch experimentov, kde je mo\u017en\u00e9 porovna\u0165 presnos\u0165 navrhnutej met\u00f3dy DWM a in\u00fdch predik\u010dn\u00fdch met\u00f3d.<\/p>\n<\/div>\n\n

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\"\"
Comparison of the prediction error of the proposed DWM method and other commonly used methods.<\/figcaption><\/figure><\/div>\n<\/div>\n\n
\n

Poznatky, ktor\u00e9 som nadobudol po\u010das rie\u0161enia dizerta\u010dnej pr\u00e1ce, s\u00fa uplatnite\u013en\u00e9 v praxi, najm\u00e4 pri rie\u0161en\u00ed \u00faloh vy\u017eaduj\u00facich predikciu spotreby elektrickej energie alebo pri detekcii anom\u00e1lnych stavov v sieti. Z\u00edskan\u00e9 poznatky bud\u00fa ur\u010dite vyu\u017eit\u00e9 v \u010fal\u0161\u00edch energetick\u00fdch projektoch, ktor\u00fdm sa v KInITe venujeme. <\/p>\n<\/div>\n\n

\n

Jedn\u00fdm z najnov\u0161\u00edch projektov, na ktorom participujem, je disagreg\u00e1cia (rozklad krivky) celkovej spotreby elektrickej energie na spotrebu jednotliv\u00fdch elektrospotrebi\u010dov v dom\u00e1cnosti, ktor\u00fa realizujeme v spolupr\u00e1ci so spolo\u010dnos\u0165ou ZSE. Rozklad spotreby pom\u00e1ha dod\u00e1vate\u013eom energie lep\u0161ie pochopi\u0165 potreby svojich z\u00e1kazn\u00edkov, v\u010faka \u010domu je mo\u017en\u00e1 efekt\u00edvnej\u0161ia dod\u00e1vka elektriny, nastavovanie z\u00e1kazn\u00edcky v\u00fdhodnej\u0161\u00edch tar\u00edf a presnej\u0161\u00ed mana\u017ement Smart dom\u00e1cnost\u00ed.<\/p>\n<\/div>\n\n

\n

V\u00fdber z Marekov\u00fdch<\/a> publik\u00e1ci\u00ed:<\/h3>\n<\/div>\n\n
\n
  1. M. Cuper, M. L\u00f3derer, and V. Rozinajov\u00e1 (2019). Detection of abnormal load consumption in the power grid using clustering and statistical analysis<\/a>. In Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2019. Springer International Publishing<\/em>. pp. 464\u2013475.
    <\/a><\/li>
  2. P. Laurinec, M. L\u00f3derer, M. Luck\u00e1, and V. Rozinajov\u00e1 (2019). Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption<\/a>. In Journal of Intelligent Information Systems<\/em>. Vol. 53, 219.
    <\/li>
  3. V. Rozinajov\u00e1, A. Bou Ezzeddine, M. L\u00f3derer, J. Loebl, R. Magyar and P. Vrablecov\u00e1 (2018). Computational intelligence in smart grid environment<\/a>. In A. K. Sangaiah, Z. Zhang, & M. Sheng (Eds.), Computational intelligence for multimedia big data on the cloud with engineering applications, Intelligent Data-Centric Systems<\/em>. (pp. 23-59). Cambridge, MA, USA: Elsevier.
    <\/li>
  4. M. L\u00f3derer, P. Pavl\u00edk, and V. Rozinajov\u00e1 (2018). Improving time series prediction via modification of dynamic weighted majority in ensemble learning<\/a>. In Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2018. Springer International Publishing<\/em>. pp. 651\u2013660.
    <\/li>
  5. P. Hala\u0161, M. L\u00f3derer, and V. Rozinajov\u00e1 (2017). Prediction of electricity consumption using biologically inspired algorithms<\/a>. In 2017 IEEE 14th International Scientific Conference on Informatics<\/em> (IEEE). (Poprad, 2017) pp. 98-103.<\/li><\/ol>\n<\/div>","protected":false},"excerpt":{"rendered":"

    Marek L\u00f3derer je na\u0161\u00edm nov\u00fdm doktorom a vo svojej dizerta\u010dnej pr\u00e1ci sa venoval met\u00f3dam predikt\u00edvnej anal\u00fdzy v oblasti energetiky. Radi by sme v\u00e1s v kr\u00e1tkosti obozn\u00e1mili s jeho v\u00fdskumom a…<\/p>\n","protected":false},"author":4,"featured_media":6633,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[81,82,88],"tags":[167],"class_list":["post-8627","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analytics-for-green-energy-sk","category-2021-sk","category-pop-science-sk","tag-data-analytics-sk"],"acf":[],"yoast_head":"\nAplik\u00e1cia biologicky in\u0161pirovan\u00fdch algoritmov ako pomoc na spresnenie predikci\u00ed v energetike - KInIT<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/kinit.sk\/sk\/application-of-biologically-inspired-algorithms-to-improve-precision-in-power-load-predictions\/\" \/>\n<meta property=\"og:locale\" content=\"sk_SK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Aplik\u00e1cia biologicky in\u0161pirovan\u00fdch algoritmov ako pomoc na spresnenie predikci\u00ed v energetike - KInIT\" \/>\n<meta property=\"og:description\" content=\"Marek L\u00f3derer je na\u0161\u00edm nov\u00fdm doktorom a vo svojej dizerta\u010dnej pr\u00e1ci sa venoval met\u00f3dam predikt\u00edvnej anal\u00fdzy v oblasti energetiky. 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