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Daily Peak Load Demand Forecast Considering Weather Conditions | IEEE Conference Publication | IEEE Xplore

Daily Peak Load Demand Forecast Considering Weather Conditions

Publisher: IEEE

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 me...View more

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
Date of Conference: 25-27 February 2022
Date Added to IEEE Xplore: 22 March 2022
ISBN Information:
Publisher: IEEE
Conference Location: Shiga, Japan

I. Introduction

Electricity enriches our lives. The electric power company estimates the demand for the electric power every day. For example, in the case of TEPCO(Tokyo Electric Power Company) Power Grid, the date and time of daily peak load demand are disclosed in Electrical Forecast [1]. Since electric power cannot be stored, it is important to reduce surplus electric power for the sake of the environment problem as prevention from global warming etc.. In previous studies, the authors have confirmed that meteorological conditions are important for load demand forecasting and that determining forecast values with a single forecast model is a risk. In this paper, we have proposed a prediction method combining machine learning methods and statistical methods that is different prediction characteristics. Furthermore, we consider the effective input variables for daily peak load demand forecasting.

References

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