| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
|
|---|
| Outline of Final Research Achievements |
n this research, new models for GP of Kernel Machine have been proposed to deal with electricity price forecasting in power markets. So, intelligent systems are the mainstream of the methods due to the good nonlinear approximation. However, there is still room for improvement of intelligent systems for electricity price forecasting. This research proposes three strategies for GP, Use of Mahalanobis Functions in GP, Application of EPSO to determination of parameters, and Precondition of learning data by crisp and fuzzy clustering. In addition, the effectiveness of the strategies was investigated for an alternative model of GRBFN (Generalized Radial Basis Function Network) of ANN (Artificial Neural Network).
|
