Forecasting Electricity Consumption in Czech Republic

Forecasting Electricity Consumption in Czech Republic

Paper in proceedings (conference paper)

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%.

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%.

Electricity consumption, forecast, machine learning, optimalization, prediction

Electricity consumption, forecast, machine learning, optimalization, prediction

UHER, V.; BURGET, R.; DUTTA, M.; MLÝNEK, P.

2016

09.07.2015

Prague, Czech Republic

978-1-4799-8497-8

Proceedings of the 38th International Conference on Telecommunication and Signal Processing, TSP 2015

NEUVEDENO

262

265

4

https://ieeexplore.ieee.org/document/7296264

http://hdl.handle.net/