  Short-term load forecasting is one of the crucial sections in smart grid.
Precise forecasting enables system operators to make reliable unit commitment
and power dispatching decisions. With the advent of big data, a number of
artificial intelligence techniques such as back propagation, support vector
machine have been used to predict the load of the next day. Nevertheless, due
to the noise of raw data and the randomness of power load, forecasting errors
of existing approaches are relatively large. In this study, a short-term load
forecasting method is proposed on the basis of empirical mode decomposition and
long short-term memory networks, the parameters of which are optimized by a
particle swarm optimization algorithm. Essentially, empirical mode
decomposition can decompose the original time series of historical data into
relatively stationary components and long short-term memory network is able to
emphasize as well as model the timing of data, the joint use of which is
expected to effectively apply the characteristics of data itself, so as to
improve the predictive accuracy. The effectiveness of this research is
exemplified on a realistic data set, the experimental results of which show
that the proposed method has higher forecasting accuracy and applicability, as
compared with existing methods.
