Abstract
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.
Highlights
Short-term load forecasting (STLF), which ranges from one hour to one week ahead, plays an important role in the control, power security, market operation, and scheduling of reasonable dispatching plans for smart grids
LSTM was mainly motivated and designed to overcome the vanishing gradients problem of the standard recurrent neural network (RNN) when dealing with long term dependencies.This section leads to the long short-term memory neural network
We perform simulations of the four examples to verify the predictive ability of the proposed method: Example 1: Through the enumeration method, k ranges from 5 to 12, the run is repeated several times in each k value using the Xgboost-k-means-based similar day (SD)-empirical mode decomposition (EMD)-LSTM model
Summary
Short-term load forecasting (STLF), which ranges from one hour to one week ahead, plays an important role in the control, power security, market operation, and scheduling of reasonable dispatching plans for smart grids. Mu [9] applied a weighted average model for the historical day to determine the influence of most SDs on the forecasted day Using this method solely cannot sufficiently obtain high prediction accuracy. LSTM performs well in long time horizon forecasting than other artificial intelligence methods based on the past load data that determine the effect and relationship among time series. This study presents a generic framework that combines extreme gradient boosting (Xgboost) and k-means on SD selection, empirical mode decomposition (EMD), and LSTM neural networks to forecast short-term load (i.e., SD-EMD-LSTM model). Numerical testing demonstrates that data decomposition-based LSTM neural networks can outperform most of the well-established forecasting methods in the longer-horizon load forecasting problem.
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