Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory
With the continuous development of power industry, the importance of load forecasting is becoming more and more obvious. As an important part of load forecasting, short-term load forecasting is of great significance to the dispatching and operation of power system and market transactions. Accurate l...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2020-06-01
|
| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201910012 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850030122218815488 |
|---|---|
| author | Huiru ZHAO Yihang ZHAO Sen GUO |
| author_facet | Huiru ZHAO Yihang ZHAO Sen GUO |
| author_sort | Huiru ZHAO |
| collection | DOAJ |
| description | With the continuous development of power industry, the importance of load forecasting is becoming more and more obvious. As an important part of load forecasting, short-term load forecasting is of great significance to the dispatching and operation of power system and market transactions. Accurate load forecasting is helpful to improve the utilization rate of power generation equipment and the effectiveness of economic dispatching. Because load data are affected by many random factors and have strong nonlinear characteristics, a short-term power load forecasting method is proposed based on complementary ensemble empirical mode decomposition and long short-term memory. A simulation is made of a city’s power load data using the proposed method, and the simulation results are compared with those of other traditional forecasting methods. It is proved that the long short-term memory model has lower error and higher prediction accuracy. At the same time, the prediction results of complementary ensemble empirical mode decomposition and long short-term memory are compared with those of long short-term memory model under other decomposition methods, which has verified that the complementary ensemble empirical mode decomposition method is effective in improving the prediction accuracy. |
| format | Article |
| id | doaj-art-afb8e220ffe74b6a8a1579d1cfa669e6 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2020-06-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-afb8e220ffe74b6a8a1579d1cfa669e62025-08-20T02:59:18ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492020-06-01536485510.11930/j.issn.1004-9649.201910012zgdl-53-6-zhaoyihangShort-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term MemoryHuiru ZHAO0Yihang ZHAO1Sen GUO2School of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaWith the continuous development of power industry, the importance of load forecasting is becoming more and more obvious. As an important part of load forecasting, short-term load forecasting is of great significance to the dispatching and operation of power system and market transactions. Accurate load forecasting is helpful to improve the utilization rate of power generation equipment and the effectiveness of economic dispatching. Because load data are affected by many random factors and have strong nonlinear characteristics, a short-term power load forecasting method is proposed based on complementary ensemble empirical mode decomposition and long short-term memory. A simulation is made of a city’s power load data using the proposed method, and the simulation results are compared with those of other traditional forecasting methods. It is proved that the long short-term memory model has lower error and higher prediction accuracy. At the same time, the prediction results of complementary ensemble empirical mode decomposition and long short-term memory are compared with those of long short-term memory model under other decomposition methods, which has verified that the complementary ensemble empirical mode decomposition method is effective in improving the prediction accuracy.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201910012short-term load forecastinglong short-term memorycomplementary ensemble empirical mode decompositiondeep learning |
| spellingShingle | Huiru ZHAO Yihang ZHAO Sen GUO Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory Zhongguo dianli short-term load forecasting long short-term memory complementary ensemble empirical mode decomposition deep learning |
| title | Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory |
| title_full | Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory |
| title_fullStr | Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory |
| title_full_unstemmed | Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory |
| title_short | Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory |
| title_sort | short term load forecasting based on complementary ensemble empirical mode decomposition and long short term memory |
| topic | short-term load forecasting long short-term memory complementary ensemble empirical mode decomposition deep learning |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201910012 |
| work_keys_str_mv | AT huiruzhao shorttermloadforecastingbasedoncomplementaryensembleempiricalmodedecompositionandlongshorttermmemory AT yihangzhao shorttermloadforecastingbasedoncomplementaryensembleempiricalmodedecompositionandlongshorttermmemory AT senguo shorttermloadforecastingbasedoncomplementaryensembleempiricalmodedecompositionandlongshorttermmemory |