Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction
Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction
复制标题
基于双深度学习网络的部分实时信息负荷预测及其在系统边际价格预测中的应用
DOI：
10.3390/en13010148
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发表时间：
2019
期刊：
影响因子：
3.2
通讯作者：
Hwachang Song
中科院分区：
文献类型：
--
作者：
Khikmafaris Yudantaka;Jung;Hwachang Song
负荷功率预测是电力系统运行和维护中最重要的任务之一。提高其准确性有助于电力系统调度。本文介绍了如何使用部分实时温度信息来预测负载功率，这通常是使用过去的负载功率和温度数据来完成的。部分实时温度信息是指整个预测时间间隔中仅部分时间的温度信息。为此，使用过去的温度和负载功率数据训练长短期记忆（LSTM）网络，以预测负载功率，其中预测的负载功率隐式取决于温度预测。然后，为了处理发生重大温度预测错误的情况，使用描述温度变化和负载功率变化之间关系的过去数据来训练多层感知器（MLP）网络。然后，在预测时间间隔开始时测量温度，并通过将 LSTM 的输出与输入为温度预测误差的 MLP 的输出相加来计算补偿负载预测。结果表明，所提出的使用实时温度信息的补偿确实提高了负载功率预测的性能。这种改进的负载预测用于预测系统边际价格（SMP）。使用韩国的真实温度和负载功率数据对所提出的方法进行了验证。
Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done using past load power and temperature data. The partial real-time temperature information means temperature information for only part of the entire prediction time interval. To this end, a long short-term memory (LSTM) network is trained using past temperature and load power data in order to forecast load power, where forecasted load power depends on the temperature prediction implicitly. Then, in order to deal with the case where nontrivial temperature prediction errors happen, a multi-layer perceptron (MLP) network is trained using the past data describing the relation between temperature variation and load power variation. Then, the temperature is measured at the beginning of the prediction time-interval and compensated load forecast is computed by adding the output of the LSTM and that of the MLP whose input is the temperature prediction error. It is shown that the proposed compensation using the real-time temperature information indeed improves performance of load power forecast. This improved load forecast is used to predict system marginal price (SMP). The proposed method is validated using the real temperature and load power data of South Korea.