论文标题
N-Beats神经网络用于中期电力负载预测
N-BEATS neural network for mid-term electricity load forecasting
论文作者
论文摘要
本文解决了中期电力负载预测问题。解决此问题对于电力系统的运营和计划以及在失调的能源市场中谈判远期合同所必需的。我们表明，我们提出的基于深神经结构的深度神经网络建模方法有效地解决了中期电力负载预测问题。提出的神经网络具有高表达能力，可以解决非线性随机预测问题，包括时间序列，包括趋势，季节性和显着的随机波动。同时，实施和训练非常简单，不需要信号预处理，并且配备了预测偏置降低机制。我们将我们的方法与十种基线方法（包括经典统计方法，机器学习和混合方法）与欧洲国家 /地区的35个每月电力需求时间序列进行了比较。实证研究表明，提出的神经网络在准确性和预测偏见方面显然优于所有竞争者。代码可在此处找到：https：//github.com/boreshkinai/nbeats-midterm。
This paper addresses the mid-term electricity load forecasting problem. Solving this problem is necessary for power system operation and planning as well as for negotiating forward contracts in deregulated energy markets. We show that our proposed deep neural network modeling approach based on the deep neural architecture is effective at solving the mid-term electricity load forecasting problem. Proposed neural network has high expressive power to solve non-linear stochastic forecasting problems with time series including trends, seasonality and significant random fluctuations. At the same time, it is simple to implement and train, it does not require signal preprocessing, and it is equipped with a forecast bias reduction mechanism. We compare our approach against ten baseline methods, including classical statistical methods, machine learning and hybrid approaches, on 35 monthly electricity demand time series for European countries. The empirical study shows that proposed neural network clearly outperforms all competitors in terms of both accuracy and forecast bias. Code is available here: https://github.com/boreshkinai/nbeats-midterm.
