Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach

• Published in: International Conference on Big Data and Smart Computing pp. 119 - 125
• Main Authors: Xishuang Dong, Lijun Qian, Lei Huang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.02.2017
• Subjects: Big Data Analytics; Convolutional Neural Network; Data models; Forecasting; Load forecasting; Load modeling; Machine Learning; Predictive models; Short-term Load Forecasting; Testing; Training
• ISSN: 2375-9356
• DOI: 10.1109/BIGCOMP.2017.7881726

Although many methods are available to forecast short-term electricity load based on small scale data sets, they may not be able to accommodate large data sets as electricity load data becomes bigger and more complex in recent years. In this paper, a novel machine learning model combining convolutional neural network with K-means clustering is proposed for short-term load forecasting with improved scalability. The large data set is clustered into subsets using K-means algorithm, then the obtained subsets are used to train the convolutional neural network. A real-world power industry data set containing more than 1.4 million of load records is used in this study and the experimental results demonstrate the effectiveness of the proposed method.