Semantic Scholar Open Access 2018 295 sitasi

Short term electricity load forecasting using a hybrid model

Jinliang Zhang Yi-Ming Wei Dezhi Li Z. Tan Jianhua Zhou

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (5)

Jinliang Zhang

Yi-Ming Wei

Dezhi Li

Z. Tan

Jianhua Zhou

Format Sitasi

APA MLA BibTeX

Zhang, J., Wei, Y., Li, D., Tan, Z., Zhou, J. (2018). Short term electricity load forecasting using a hybrid model. https://doi.org/10.1016/J.ENERGY.2018.06.012

Akses Cepat

Lihat di Sumber doi.org/10.1016/J.ENERGY.2018.06.012

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 295×
Sumber Database: Semantic Scholar
DOI: 10.1016/J.ENERGY.2018.06.012
Akses: Open Access ✓