An Improved Prediction Model Based on Grey Clustering Analysis Method and its Application in Power Load Forecasting

Current grey clustering analysis methods have some defects. So, this paper proposes a prediction model based on improved grey clustering analysis. Firstly, it constructs the grey classical domain and the grey sector domain based on prediction subjects and data and according to relevant theory about grey clustering analysis. Secondly, it categorizes samples according to features of prediction subjects and confirms the analysis categories corresponding to the classical domain. Thirdly, based on the grey system theory, it constructs the grey correlation coefficient model and grey correlation degree model so as to obtain the weighed grey correlation degree. Thus, prediction subjects can be divided into proper category. Finally, power load forecasting in the power industry is taken as a case to prove that the model is reliable and has efficacy.