Tarkavaralahendus elektrienergia börsihinna ja ilmastikuandmete salvestamiseks ja töötlemiseks
Laen...
Kuupäev
2024
Kättesaadav alates
10.09.2024
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Eesti Maaülikool
Abstrakt
Tulevase elektrienergia börsihinna teadmine aitab planeerida elektrienergia tarbimist. Eestis on Nord Pool börsi andmetest võimalik elektrienergia hinda vaadata ette ühe päeva. Töö eesmärk oli luua tarkvaralahendus, mis salvestab elektrienergia börsihinna ja ilmastikuandmed ning võimaldab nende andmete töötlemist, et ennustada tulevasi börsihindasid. Tarkvaralahendus võimaldab varasemate elektrienergia börsihinna, ilmastikuandmete ning ilmaprognoosi põhjal ennustada ette rohkem kui ühe päeva elektrienergia börsihinda, kasutades selleks statistilisi mudeleid. Töös antakse ülevaade tarkvaralahenduse loomiseks vajalikest komponentidest. Tarkvaralahenduse loomiseks kasutatakse Python programmeerimiskeelt. Elektrienergia börsihinda ja ilmastikuandmeid kogutakse internetist kasutades rakendusliideseid. Kogutud andmed salvestatakse andmebaasi. Andmete töötlemiseks loodi kasutajaliides. Töö käigus katsetati loodud tarkvaralahendust ühe kuu jooksul andmeid kogudes ja andmete põhjal elektrienergia börsihinda ennustades. Loodud tarkvaralahendusega saab salvestada elektrienergia börsihinda ja ilmastikuandmeid. Tarkvaralahendusega saab ka ennustada tulevast börsihinda aga katse käigus kogutud ühe kuu andmete ja ilmaprognoosi põhjal ennustatud hind ei olnud väga täpne. Tarkvaralahendust saaks täiendada lisades juurde mudeleid börsihinna ennustamiseks.
Knowing the future electricity exchange price helps consumers plan their electricity consumption. In Estonia, it is possible to view the electricity price one day in advance using data from the Nord Pool exchange. The aim of this work was to create a software solution that stores electricity exchange prices and weather data and enables the processing of this data to predict future exchange prices. The software solution allows predicting electricity exchange prices more than one day in advance using historical electricity exchange prices, weather data, and weather forecasts, using statistical models. This work provides an overview of the components necessary to create the software solution. The Python programming language is used to create the software solution. Electricity exchange prices and weather data are collected from the internet using application programming interfaces. The collected data is stored in a database. A user interface was created for data processing. The created software solution was tested over one month by collecting data and predicting electricity exchange prices based on the data. With the created software solution, it is possible to store electricity exchange prices and weather data. The software solution can also predict future exchange prices, but the price predicted using the one-month data and weather forecast collected during the test was not very accurate. The software solution could be improved by adding additional models for predicting the exchange prices.
Knowing the future electricity exchange price helps consumers plan their electricity consumption. In Estonia, it is possible to view the electricity price one day in advance using data from the Nord Pool exchange. The aim of this work was to create a software solution that stores electricity exchange prices and weather data and enables the processing of this data to predict future exchange prices. The software solution allows predicting electricity exchange prices more than one day in advance using historical electricity exchange prices, weather data, and weather forecasts, using statistical models. This work provides an overview of the components necessary to create the software solution. The Python programming language is used to create the software solution. Electricity exchange prices and weather data are collected from the internet using application programming interfaces. The collected data is stored in a database. A user interface was created for data processing. The created software solution was tested over one month by collecting data and predicting electricity exchange prices based on the data. With the created software solution, it is possible to store electricity exchange prices and weather data. The software solution can also predict future exchange prices, but the price predicted using the one-month data and weather forecast collected during the test was not very accurate. The software solution could be improved by adding additional models for predicting the exchange prices.
Kirjeldus
Bakalaureusetöö
Tehnika ja tehnoloogia õppekaval
Märksõnad
bakalaureusetööd, elektrienergia, tarkvaralahendus, programmeerimine, andmetöötlus