Journal
ENERGIES
Volume 10, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/en10081168
Keywords
long short-term memory neural networks; similar day; extreme gradient boosting; k-means; empirical mode decomposition; short-term load forecasting
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Funding
- National Key Research and Development Program of China [2017YFB0802303]
- Application platform and Industrialization for efficient cloud computing for Big data of the Science and Technology Supported Program of Jiangsu Province [BA2015052]
- National Natural Science Foundation of China [61571226]
- Jiangsu Program for the transformation of scientific and technological achievements [BA2015051]
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Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.
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