Provincial Grid Investment Scale Forecasting Based on MLR and RBF Neural Network

Table 1

Researches on investment forecasting.

Authors	Forecast object	Method
Yu Hao, Ling'ou Wang, Lingyun Zhu, Minjie Ye [1]	Rural investment	Error correction model
Kaiji Motegi, Akira Sadahiro [2]	Private investment	Mixed frequency vector auto-regression approach
Coskun Hamzacebi, Coskun Hamzacebi [3]	Annual electricity consumption	Optimized grey model
Taegu Kim, Deok-Joo Lee, Sanghoi Koo [4]	R&D investment	Comparative analogy approach
WANG Dongjin, LI Xiusheng, ZHANG Haiying, WANG Zhen [5]	drilling and completion investment	BP neural network
Sheng-Tun Li, Shu-Ching Kuo [6]	Financial investment	Wavelet-based SOM networks
Xiuquan Li, Zhidong Deng, Jing Luo [7]	Financial investment	Ensemble artificial neural network
Kurt G. Lunsford [8]	Residential investment	Multi Co-integration
Yuan-Yeuan Tai, Jenn-Yang Lin, Ming-Shi Chen, Ming-Chyuan Lin [9]	Investment in the core competitiveness of product development	Analytic hierarchy process and grey model GM(1,1)
Stephan Leitner, Alexandra Rausch, DorisA. Behrens [10]	Distributed investment decisions	Multi-agent simulation
Zhao, H.; Yang, L.; Li, C.; Ma, X [11]	Investment demand of power grid	Co-integration theory and error correction model
Hu, B.C.; Hu, G.; Hu, C.H.; Qin, S.; Li, M.W.; Peng, C. [12]	Grid Infrastructure Investment	Gray Prediction and analytic hierarchy process
Fang, J.L.; Xie, Y.J.; Jin, Y.; Feng, S. [13]	Power Grid Investment	Grey prediction method and hierarchical analysis method
Shuyu Dai, Dongxiao Niu and Yaru Han [14]	Power Grid Investment	Support Vector Machine
Hong, W.-C. [15]	Traffic flow forecasting	Seasonal SVR with chaotic immune algorithm
Geng, J., Huang, M.-L., Li, M.-W., Hong, W.-C [16]	Load forecasting	Seasonal chaotic cloud simulated annealing algorithm and SVR