论文标题
短期电价预测的深卷卷卷神经网络模型
Deep Convolutional Neural Network Model for Short-Term Electricity Price Forecasting
论文作者
论文摘要
在现代电力市场中，电力交易是一个极具竞争力的行业。更准确的价格预测对于帮助电力生产商和贸易商做出更好的决策至关重要。在本文中，提出了一种新颖的卷积神经网络（CNN）方法，以迅速在能源市场中提供小时预测。为了提高预测准确性，我们按季节将年度电价数据分为四类，并分别对每个类别进行培训和预测。通过将提出的方法与其他现有方法进行比较，我们发现所提出的模型已取得了出色的结果，每个类别的平均绝对百分比误差（MAPE）和均方根误差（RMSE）分别约为5.5％和3。
In the modern power market, electricity trading is an extremely competitive industry. More accurate price forecast is crucial to help electricity producers and traders make better decisions. In this paper, a novel method of convolutional neural network (CNN) is proposed to rapidly provide hourly forecasting in the energy market. To improve prediction accuracy, we divide the annual electricity price data into four categories by seasons and conduct training and forecasting for each category respectively. By comparing the proposed method with other existing methods, we find that the proposed model has achieved outstanding results, the mean absolute percentage error (MAPE) and root mean square error (RMSE) for each category are about 5.5% and 3, respectively.
