深度学习框架下LSTM网络在短期电力负荷预测中的应用

Application of LSTM Networks in Short-Term Power Load Forecasting Under the Deep Learning Framework

准确的电力负荷预测可以保证电力供应的稳定,降低用电成本,提高供电质量。在进行短期电力负荷预测时,考虑到时序数据的时间相关性,应用张量流深度学习框架构建了LSTM神经网络模型,对电力负荷时序数据进行回归预测。使用某省电力公司电力负荷数据进行模拟仿真,结果表明基于长短期记忆人工神经网络(Long-Short Term Memory,LSTM)的深度学习模型在短期电力负荷预测中可以有效地预测负荷变化。

Accurate power load forecasting can ensure the stability of power supply, reduce the cost and improve the quality of power supply. For the short-term power load forecasting, considering the time correlation of time- series data, this paper builds a LSTM neural network model based on the TensorFlow deep learning framework and applies this model in the power load time-series data regression prediction. A simulation using power load data from a province power company proves that the deep learning model based on long-short term memory neural network can effectively predicate the load change in the short-term power load forecasting.