Electrical Load Forecasting Based on LSTM Neural Networks
- DOI
- 10.2991/acsr.k.191223.024How to use a DOI?
- Keywords
- electrical load forecasting, time series, recursive neural network, long short-term memory
- Abstract
The accurate prediction of electrical load is vital to the safety of power grid and the efficiency of energy. The development history of electrical load forecasting is introduced briefly in this paper. Then the relationship between electricity, environment and economy is also analyzed. Ljubljana’s load data and environmental meteorological data of the previous time are applied to train LSTM networks to make prediction of the electrical load. The experimental results show that, in the case of abundant data with good quality, the LSTM network model can make quite acceptable short-term predictions of the power load based on previous energy consumption and environmental weather data.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Lei Guo AU - Linyu Wang AU - Hao Chen PY - 2019 DA - 2019/12/24 TI - Electrical Load Forecasting Based on LSTM Neural Networks BT - Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019) PB - Atlantis Press SP - 107 EP - 111 SN - 2352-538X UR - https://doi.org/10.2991/acsr.k.191223.024 DO - 10.2991/acsr.k.191223.024 ID - Guo2019 ER -