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Short-term load forecasting is one of the crucial sections in smart grid.Precise forecasting enables system operators to make reliable unit commitmentand power dispatching decisions. With the advent of big data, a number ofartificial intelligence techniques such as back propagation, support vectormachine have been used to predict the load of the next day. Nevertheless, dueto the noise of raw data and the randomness of power load, forecasting errorsof existing approaches are relatively large. In this study, a short-term loadforecasting method is proposed on the basis of empirical mode decomposition andlong short-term memory networks, the parameters of which are optimized by aparticle swarm optimization algorithm. Essentially, empirical modedecomposition can decompose the original time series of historical data intorelatively stationary components and long short-term memory network is able toemphasize as well as model the timing of data, the joint use of which isexpected to effectively apply the characteristics of data itself, so as toimprove the predictive accuracy. The effectiveness of this research isexemplified on a realistic data set, the experimental results of which showthat the proposed method has higher forecasting accuracy and applicability, ascompared with existing methods.

Short-term load forecasting using optimized LSTM networks based on EMD