This paper tackles a new challenge in power big data: how to improve the precision of short-term load forecasting with large-scale data set. The proposed load forecasting method is based on Spark platform and “clustering–regression” model, which is implemented by Apache Spark machine learning library (MLlib). Proposed scheme firstly clustering the users… (More)

Cooling load forecasting method based on support vector machine optimized with entropy and variable accuracy roughness set

M. Xie, Ji, D.J.L.X.

Power Syst. Technol. 41(1), 5

2017

1 Excerpt

Empiricalmode decomposition based denoising method with support vector regression for time series prediction: a case study for electricity load forecasting

Y. Yaslan, B. Bican

Measurement 103, 52–61

2017

1 Excerpt

Modeling and impact analysis of interdependent characteristics on cascading failures in smart grids

Y Cai

Int. J. Electr. Power Energy Syst. 89, 106–114

2017

2 Excerpts

Very short term load forecasting of a distribution system with high PV penetration

S. Sepasi

Renew. Energy 106, 142–148

2017

1 Excerpt

A systematic design of interval type-2 fuzzy logic system using extreme learningmachine for electricity load demand forecasting