Market-based Short-term Electricity Price Forecast Based on EEMD and ARIMA
Jun Dong, Xihao Dou, Dongran Liu, Dongxue Wang
Available Online September 2018.
- https://doi.org/10.2991/icsshe-18.2018.105How to use a DOI?
- Electricity price forecast, EEMD, ARIMA, Mixed model, Electricity market
- With the development and implementation of pilots in the Chinese spot market, the importance of electricity price forecasting to various entities in the electricity market is also constantly emerging. At the same time, with the continuous deepening of China's electricity market construction, the electricity market structure of each province gradually tends to be market-oriented, and the original electricity price forecasting method has been unable to adapt to the future changes in the structure of the electricity market. Based on the characteristics of real-time electricity prices in the spot market, this study proposes a short-term electricity price mixed forecasting model based on ARIMA and EEMD to predict the 24-hour electricity price. This hybrid model uses the ARIMA model to predict the time series decomposed by EEMD, which improves the stationarity of the electricity price series and improves the accuracy. Using MATLAB and SPSS tools, reference the electricity price data of the America PJM day-ahead market to verify the proposed prediction model. The results show that the hybrid model can significantly improve the forecast accuracy of electricity prices compared with the traditional ARIMA method.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jun Dong AU - Xihao Dou AU - Dongran Liu AU - Dongxue Wang PY - 2018/09 DA - 2018/09 TI - Market-based Short-term Electricity Price Forecast Based on EEMD and ARIMA BT - 2018 4th International Conference on Social Science and Higher Education (ICSSHE 2018) PB - Atlantis Press SP - 425 EP - 429 SN - 2352-5398 UR - https://doi.org/10.2991/icsshe-18.2018.105 DO - https://doi.org/10.2991/icsshe-18.2018.105 ID - Dong2018/09 ER -