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VERY SHORT-TERM LOAD FORECASTING USING EXPONENTIAL SMOOTHING AND ARIMA MODELS

Alexandra Kotillova

Open publisher page 11 citations

Abstract

Autoregressive integrated moving average (ARIMA) model and exponential smoothing are one of the most popular linear models in time series forecasting during the past three decades. This paper uses 30-minutes Australian electricity demand observations to evaluate methods for prediction 30 minutes ahead. For comparison was designed “industry ” model.

About this research paper

What this paper is about

Autoregressive integrated moving average (ARIMA) model and exponential smoothing are one of the most popular linear models in time series forecasting during the past three decades. This paper uses 30-minutes Australian electricity demand observations to evaluate methods for prediction 30 minutes ahead. For comparison was designed “industry ” model.

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OpenAlex reports 11 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Autoregressive integrated moving average (ARIMA) model and exponential smoothing are one of the most popular linear models in time series forecasting during the past three decades. This paper uses 30-minutes Australian electricity demand observations to evaluate methods for prediction 30 minutes ahead. For comparison was designed “industry ” model.

Key concepts: Exponential smoothing, Autoregressive integrated moving average, Autocorrelation, Term (time), Box–Jenkins, Moving average, Partial autocorrelation function, Time series

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