论文标题
基于模式的长期记忆中期电气负载预测
Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting
论文作者
论文摘要
这项工作提出了一个长期的短期内存（LSTM）网络，用于预测每月的电力需求时间序列，并以一年的范围。这项工作的新颖性是将季节性时间序列的模式表示用作分解的替代方案。模式表示简化了复杂的非线性和非组织时间序列，滤除了趋势和均等方差。定义了两种类型的模式：X-Pattern和Y-Pattern。前者需要对编码变量进行其他预测。后者确定过程历史记录中的编码变量。基于X-Patterns的混合方法比基于原始时间序列的标准LSTM方法更准确。在这种组合方法中，使用序列到序列LSTM网络预测X-Pattern，并使用指数平滑预测编码变量。对35个欧洲国家 /地区的每月电力需求时间序列进行的一项模拟研究证实了该模型的高性能及其对古典模型（例如Arima和指数平滑以及MLP神经网络模型）的竞争力。
This work presents a Long Short-Term Memory (LSTM) network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition. Pattern representation simplifies the complex nonlinear and nonstationary time series, filtering out the trend and equalizing variance. Two types of patterns are defined: x-pattern and y-pattern. The former requires additional forecasting for the coding variables. The latter determines the coding variables from the process history. A hybrid approach based on x-patterns turned out to be more accurate than the standard LSTM approach based on a raw time series. In this combined approach an x-pattern is forecasted using a sequence-to-sequence LSTM network and the coding variables are forecasted using exponential smoothing. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model and its competitiveness to classical models such as ARIMA and exponential smoothing as well as the MLP neural network model.
