期刊
APPLIED SCIENCES-BASEL
卷 11, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/app11010158
关键词
short-term electrical load forecasting; machine learning; deep learning; statistical analysis; parameters tuning; CNN; LSTM
类别
资金
- EU [773430]
This paper introduces a new methodology for electric load forecasting using deep learning algorithms, which optimizes neural network hyperparameters and transforms dataset form to maximize the advantages of convolutional neural networks. Experimental results show the effectiveness of this method, performing well compared to the current state-of-the-art LSTM technique.
The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm's advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据