Kratkoročno napovedovanje porabe električne energije z uporabo mehkih Takagi-Sugeno modelov

ČERNE, GREGOR (author), Škrjanc, Igor (mentor)

Električni distributerji morajo za naslednji dan čim bolje napovedati porabo njihovih strank oziroma naročnikov električne energije, saj tako lahko vnaprej kupijo električno energijo po nižji ceni kot bi jo v trenutku porabe. Problem nastane v primeru napačne napovedi, saj to prinese dodatne stroške bodisi z nakupom dražje manjkajoče energije v trenutku porabe bodisi s kaznimi energetskega regulatorja zaradi preobremenitve omrežja. Opisana situacija je problem kratkoročnega napovedovanja porabe električne energije, ki so se ga z različnimi metodami lotili že mnogi znanstveniki ter gospodarske družbe. Magistrsko delo se problema kratkoročnega napovedovanja porabe električne energije loti z uporabo najnovejših odkritij na področju modeliranja procesov z mehkimi modeli. Ti se v literaturi za napovedovanje porabe električne energije že uporabljajo, vendar v pretežno neprilagodljivih konfiguracijah (ročno vnaprej določeni roji), kjer za dober model potrebujemo dobro znanje o samih podatkih ter dolgotrajen proces uglaševanja parametrov. Magistrsko delo tako razišče možnosti uporabe adaptivnih mehkih modelov v namen poenostavitve načrtovanja modela napovedovanja porabe električne energije. Tekom dela so bile razvite tudi nadgradnje za particioniranje prostora, katere bi lahko bile uporabne tudi na drugih področjih uporabe mehkih modelov. Rezultati razvitega modela so se izkazali kot dobri in obetajoči za nadaljnje raziskovanje, predvsem se pa je model izkazal v primerjavi z že obstoječimi metodami.

• Language: Slovenian
• Keywords: mehko modeliranje, napovedovanje, poraba električne energije, rojenje Gustafson-Kessel, metoda uteženih najmanjših kvadratov, skrivanje rojev, identifikacija
• Work type: Master's thesis/paper
• Organization: FE - Faculty of Electrical Engineering
• Year: 2016
• PID: 20.500.12556/RUL-84151
• Publication date in RUL: 12.07.2016

ČERNE, GREGOR, 2016, Kratkoročno napovedovanje porabe električne energije z uporabo mehkih Takagi-Sugeno modelov [online]. Master’s thesis. [Accessed 28 March 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=84151

Secondary language

• Language: English
• Title: Short-term forecasting of electric energy consumption by using fuzzy Takagi-Sugeno models

Abstract

Because electricity distributors can pre-purchase electric energy in advance at the lower prices than at the time of consumption, they want to forecast their customer's demand for the following day in order to pre-purchase electric energy according to that forecast. A problem arises in the case of an inaccurate forecast, as it brings additional expenses either by buying more expensive energy at the time of consumption either by energy regulator's penalties for overloading the electric network. The described situation is the problem of short-term forecasting of electric energy consumption, which was already addressed by many scientists and corporations using various methods. Master's thesis tackles the mentioned problem using the latest discoveries in the field of fuzzy modeling. In the literature, fuzzy modeling has been already used for forecasting electric energy consumption, but in the predominantly non-flexible configurations (for example manually predefined clusters), where a good knowledge of the given data and a time-consuming tuning of parameters is required. Therefore, Master's thesis investigates the possibility of using adaptive fuzzy models for the purpose of simplifying modeling stage in electric energy consumption forecasting. During the research, we also developed upgrades for space partitioning, which could be effective also in other fields where fuzzy modeling is used. The results of the developed model proved to be good and promising for further research, especially when compared with existing methods.

• Keywords: fuzzy modeling, forecasting, electric energy consumption, Gustafson-Kessel clustering, weighted least mean square method, cluster hiding, identification