Detail publikace

Forecasting Electricity Consumption in Czech Republic

Originální název

Forecasting Electricity Consumption in Czech Republic

Anglický název

Forecasting Electricity Consumption in Czech Republic

Jazyk

en

Originální abstrakt

Correct prediction of electricity consumption is important for planning its production in the short term, but also in the long term due to the construction of new power plants and mining planning. Accurate prediction is a challenging task because the consumption changes both in the day and during the whole year. The paper describes a method based only on input data for consumption. No additional influences were included such as temperature, wind, GDP (Gross Domestic Product). Five machine learning algorithms were used to create a predictive model. The best results were achieved with a local polynomial regression algorithm. Daily prediction error was 5.77%, weekly 3.49% and monthly 2.41%.

Anglický abstrakt

Correct prediction of electricity consumption is important for planning its production in the short term, but also in the long term due to the construction of new power plants and mining planning. Accurate prediction is a challenging task because the consumption changes both in the day and during the whole year. The paper describes a method based only on input data for consumption. No additional influences were included such as temperature, wind, GDP (Gross Domestic Product). Five machine learning algorithms were used to create a predictive model. The best results were achieved with a local polynomial regression algorithm. Daily prediction error was 5.77%, weekly 3.49% and monthly 2.41%.

BibTex


@inproceedings{BUT115494,
  author="Václav {Uher} and Radim {Burget} and Malay Kishore {Dutta} and Petr {Mlýnek}",
  title="Forecasting Electricity Consumption in Czech Republic",
  annote="Correct prediction of electricity consumption is important for planning its production in the short term, but also in the long term due to the construction of new power plants and mining planning. Accurate prediction is a challenging task because the consumption changes both in the day and during the whole year. The paper describes a method based only on input data for consumption. No additional influences were included such as temperature, wind, GDP (Gross Domestic Product). Five machine learning algorithms were used to create a predictive model. The best results were achieved with a local polynomial regression algorithm. Daily prediction error was 5.77%, weekly 3.49% and monthly 2.41%.",
  booktitle="Proceedings of the 38th International Conference on Telecommunication and Signal Processing, TSP 2015",
  chapter="115494",
  doi="10.1109/TSP.2015.7296264",
  howpublished="electronic, physical medium",
  year="2015",
  month="july",
  pages="262--265",
  type="conference paper"
}