Work

Predicting 24-Hour Nationwide Electrical Energy Consumption Based on Regression Techniques

Year: 2024

Type: conference-paper

Abstract: This paper applies standard regression techniques to forecast the country-wide consumption of electrical energy. All considered machine learning algorithms are available as a part of the Scikit-learn library. Besides the fine-tuning of regression hyperparameters, several data preparation techniques are employed to improve the forecasting accuracy. It is demonstrated that forecasting for 24 hours ahead is possible with good accuracy and has practical significance.

Source: International Scientific and Practical Programming Conference (UkrPROG)

DOI: 10.55056/ceur-ws.org/vol-3806/s_4_doroshenko_zhora_haidukevych_yatsenko.pdf

Authors: Anatoliy Doroshenko, Dmytro Zhora, Vladyslav Haidukevych, Yaroslav Haidukevych, Olena Yatsenko

Institutions: National Academy of Sciences of Ukraine, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Institute of Software Systems

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W7166319572

FWCI: 0.668

Cites: 2

Cited by: 1

Related to: 10