Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning

被引:31
作者
Andriopoulos, Nikos [1 ,2 ]
Magklaras, Aristeidis [1 ]
Birbas, Alexios [1 ]
Papalexopoulos, Alex [3 ]
Valouxis, Christos [1 ]
Daskalaki, Sophia [1 ]
Birbas, Michael [1 ]
Housos, Efthymios [1 ]
Papaioannou, George P. [2 ]
机构
[1] Univ Patras, Dept Elect & Comp Engn, Patras 26500, Greece
[2] Independent Power Transmiss Operator IPTO SA, Res Technol & Dev Dept, 89 Dyrrachiou & Kifisou Str Gr, Athens 10443, Greece
[3] Ecco Int Inc, San Francisco, CA 94104 USA
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 01期
关键词
short-term electrical load forecasting; machine learning; deep learning; statistical analysis; parameters tuning; CNN; LSTM; NEURAL-NETWORK; STORAGE DEVICES; MODEL; ALGORITHM; ERROR;
D O I
10.3390/app11010158
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页码:1 / 22
页数:22
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