2008•RelayRequires access

Electricity price forecasting solution based on time series models

Peng Li

Open publisher page 1 citations

Abstract

Based on the day-ahead price research on the United States PJM electricity market from August to November in 2006,this paper proposes a ARIMA,ARCH and combination model of neural network based on the time series to predict the day-ahead price in future 24 hours of United States PJM electricity market,seasonal ARIMA model reflects the price trend,and seasonal,ARCH models reflect the price’s heteroscedasticity,therefore the model can reflect the characteristics of electricity price better,predicted results is excellent and there is broader application prospects.

About this research paper

What this paper is about

Based on the day-ahead price research on the United States PJM electricity market from August to November in 2006,this paper proposes a ARIMA,ARCH and combination model of neural network based on the time series to predict the day-ahead price in future 24 hours of United States PJM electricity market,seasonal ARIMA model reflects the price trend,and seasonal,ARCH models reflect the price’s heteroscedasticity,therefore the model can reflect the characteristics of electricity price better,predicted results is excellent and there is broader application prospects.

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Based on the day-ahead price research on the United States PJM electricity market from August to November in 2006,this paper proposes a ARIMA,ARCH and combination model of neural network based on the time series to predict the day-ahead price in future 24 hours of United States PJM electricity market,seasonal ARIMA model reflects the price trend,and seasonal,ARCH models reflect the price’s heteroscedasticity,therefore the model can reflect the characteristics of electricity price better,predicted results is excellent and there is broader application prospects.

Key concepts: Autoregressive integrated moving average, Electricity price forecasting, Electricity, Electricity market, Electricity price, Heteroscedasticity, Econometrics, Time series

Related papers

Back to paper searchBrowse research topicsOriginal source
Electricity price forecasting solution based on time series models — Research Paper | ScholarLens