Name
Title | ||
|---|---|---|
Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network |
Abstract | ||
|---|---|---|
Electric load forecasting plays a vital role in smart grids. Short term electric load forecasting forecasts the load that is several hours to several weeks ahead. Due to the nonlinear, non-stationary and nonseasonal nature of the short term electric load time series in small scale power systems, accurate forecasting is challenging. This paper explores Long-Short-Term-Memory (LSTM) based Recurrent Neural Network (RNN) to deal with this challenge. LSTM-based RNN is able to exploit the long term dependencies in the electric load time series for more accurate forecasting. Experiments are conducted to demonstrate that LSTM-based RNN is capable of forecasting accurately the complex electric load time series with a long forecasting horizon. Its performance compares favorably to many other forecasting methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CISS.2017.7926112 | 2017 51st Annual Conference on Information Sciences and Systems (CISS) |
Keywords | Field | DocType |
Electric load forecasting,univariate time series,smart grids,recurrent neural network (RNN),long-short-term-memory (LSTM) | Time series,Nonlinear system,Electrical load,Smart grid,Computer science,Recurrent neural network,Long short term memory,Electric power system,Load forecasting,Real-time computing | Conference |
ISBN | Citations | PageRank |
978-1-5090-2697-5 | 3 | 0.42 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Jian | 1 | 16 | 5.31 |
| Cencen Xu | 2 | 3 | 0.42 |
| Ziang Zhang | 3 | 17 | 5.14 |
| Xiaohua Li | 4 | 354 | 38.22 |