论文标题
G-20成员的经常性神经电力负载预测
Recurrent Neural Based Electricity Load Forecasting of G-20 Members
论文作者
论文摘要
对于每个基于发电厂的发电站来说，预测负载需求/需求的实际电量始终是一项具有挑战性的任务。由于不确定的电力需求在接收末端的电力需求引起了一些挑战，例如：降低生成和接收终端站的性能参数，最小化收入，增加了危险的危险，以预测公司对公司的未来能源的需求等，以解决此问题，在接收站的载荷上的精确预测是在接收站的准确预测以及不足的供应链之间的不足和不足。在本文中，使用复发性神经网络以及滑动窗口方法进行数据生成，已经对G-20成员进行了负载预测。在实验中，我们使用LSTM达到了16.2193 TWH的平均绝对测试误差。
Forecasting the actual amount of electricity with respect to the need/demand of the load is always been a challenging task for each power plants based generating stations. Due to uncertain demand of electricity at receiving end of station causes several challenges such as: reduction in performance parameters of generating and receiving end stations, minimization in revenue, increases the jeopardize for the utility to predict the future energy need for a company etc. With this issues, the precise forecasting of load at the receiving end station is very consequential parameter to establish the impeccable balance between supply and demand chain. In this paper, the load forecasting of G-20 members have been performed utilizing the Recurrent Neural Network coupled with sliding window approach for data generation. During the experimentation we have achieved Mean Absolute Test Error of 16.2193 TWh using LSTM.
