Short term electricity load forecasting using a hybrid model

By Jinliang Zhang, Yi-Ming Wei, Dezhi Li, Z. Tan, Jianhua Zhou. Energy, 2018

309 citations recorded by the source corpus.

Abstract Short term electricity load forecasting is one of the most important issue for all market participants. Short term electricity load is affected by natural and social factors, which makes load forecasting more difficult. To improve the forecasting accuracy, a new hybrid model based on improved empirical mode decomposition (IEMD), autoregressive integrated moving average (ARIMA) and wavelet neural network (WNN) optimized by fruit fly optimization algorithm (FOA) is proposed and compared with some other models. Simulation results illustrate that the proposed model performs well in electricity load forecasting than other comparison models.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2b01bb43f0f9c291a1ef766e9b4eea3df04ac6a1