Daily Peak Load Demand Forecast Considering Weather Conditions

Hideaki Sasaki, Shoichi Urano

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The authors have proposed a prediction method that combines multiple regression analysis, which is a statistical method, and random forest, which is a machine learning method. The proposed method has applied to the prediction of daily peak load demand. In this paper, we compare the meteorological data using not only the past temperature, humidity, and solar radiation but also wind direction/volume, weather, etc

Original languageEnglish
Title of host publicationProceedings of 2022 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages195-200
Number of pages6
ISBN (Electronic)9781665420495
DOIs
Publication statusPublished - 2022
Event12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022 - Shiga, Japan
Duration: 25 Feb 202227 Feb 2022

Publication series

NameProceedings of 2022 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022

Conference

Conference12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022
Country/TerritoryJapan
CityShiga
Period25/02/2227/02/22

Keywords

  • load demand forecast
  • Multiple regression model
  • Random forest

Fingerprint

Dive into the research topics of 'Daily Peak Load Demand Forecast Considering Weather Conditions'. Together they form a unique fingerprint.

Cite this