Short-term load interval prediction based on ALO-ML-ELM

Abstract

The short-term interval load forecasting accuracy of the power system has an important impact on the power system operation. A short-term load forecasting model based on ALO-ML-ELM is proposed for short-term load-interval forecasting without considering the load trend and inaccurate interval construction. Firstly, the weather, temperature, and historical load data are processed and normalized by taking the load trend influencing factors into account. Then the load power interval is constructed by fitting a normal distribution to the historical load error data, obtaining the relevant parameters, and generating random noise. Next, the short-term load-interval prediction model is established by optimizing the input layer weights of the multi-layer extreme learning machine (ML-ELM) by Ant Colony Optimization (ALO). Finally, it is validated by the actual load data of a city in China. The simulation results show that the proposed method has higher interval coverage and accuracy.