Short-term Power Load Forecasting Based on Balanced KNN

To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.

[1]  Fushuan Wen,et al.  From Smart Grid to Energy Internet:Basic Concept and Research Framework , 2014 .

[2]  Boming Zhang,et al.  Ultra-short-term load forecasting using robust exponentially weighted method in distribution networks , 2015, 2015 IEEE Power & Energy Society General Meeting.

[3]  Zhang Suxian.  Short-term Power Load Forecasting Based on Big Data , 2015 .

[4]  Yang Ming-hao.  Periodic autoregressive short-term forecasting method based on the linear correlation analysis , 2010 .

[5]  Zhu Yongli.  Present Status and Challenges of Big Data Processing in Smart Grid , 2013 .

[6]  Ngo Vietcuon.  Ultra-Short Term Load Forecasting Using Robust Holt-Winter in Distribution Network , 2014 .

[7]  Nikos D. Hatziargyriou,et al.  Short-Term Load Forecasting Using Radial Basis Function Networks , 2004, SETN.

[8]  Zhao Chuanhui,et al.  Energy Internet system based on distributed renewable energy generation , 2010 .

[9]  Lei Shao-lan.  SHORT-TERM LOAD FORECASTING METHOD BASED ON RBF NEURAL NETWORK AND ANFIS SYSTEM , 2005 .

[10]  Hu Zhi-hong,et al.  Review of the short-term load forecasting methods of electric power system , 2011 .

[11]  Zeng,et al.  Least Squares-support Vector Machine Load Forecasting Approach Optimized by Bacterial Colony Chemotaxis Method , 2011 .

[12]  Huang Renl,et al.  Design of Hierarchy and Functions of Regional Energy Internet and Its Demonstration Applications , 2015 .

[13]  Luo Junzhou,et al.  Cloud computing:architecture and key technologies , 2011 .

[14]  Lin Zhenzhi,et al.  Cloud Computing:Implementing an Essential Computing Platform for Future Power Systems , 2010 .

[15]  Zhang Zhen-gao.  Load Derivation in Short Term Forecasting Using Weather Factor , 2006 .

[16]  Lin Jiang,et al.  Annotation for FEP and DSP in Terms of Trajectory Section Eigenvalues , 2010 .