Abstract
Short-term load forecasting is an important part of load forecasting, which is of great significance to the optimal power flow and power supply guarantee of the power system. In this paper, we proposed the load series reconstruction method combined improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with sample entropy (SE). The load series is decomposed by ICEEMDAN and is reconstructed into a trend component, periodic component, and random component by comparing with the sample entropy of the original series. Extreme learning machine optimized by salp swarm algorithm (SSA-ELM) is used to predict respectively, and the final prediction value is obtained by superposition of the prediction results of the three components. Then, the prediction error of the training set is divided into four load intervals according to the predicted value, and the kernel probability density is estimated to obtain the error distribution of the training set. Combining the predicted value of the prediction set with the error distribution of the corresponding load interval, the prediction load interval can be obtained. The prediction method is verified by taking the hourly load data of a region in Denmark in 2019 as an example. The final experimental results show that the proposed method has a high prediction accuracy for short-term load forecasting.
Highlights
With the development of industry and the economy, the conflict between supply and demand for energy is becoming increasingly acute
By analyzing the above experiments, we can draw the following conclusions: (1) Compared with ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) decomposition models, we find that ICEMDAN
To avoid the impact of the highest point of annual load value (4952) on prediction intervals normalized averaged width (PINAW), we select the second highest point of forecast set value 3416 as the upper limit of load change, and the final PINAW is 0.112. This shows that the width of the prediction interval is within a reasonable range, and the model used in this paper does not obtain high coverage by unlimited increase of the width of the error interval
Summary
With the development of industry and the economy, the conflict between supply and demand for energy is becoming increasingly acute. In order to solve this problem, we use improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with sample entropy to reconstruct the load series into three parts: random component, periodic component, and trend component, which reduces the number of models. In this way, the number of prediction models can be reduced to three and the training time can be shortened.
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