Development of advanced Gaussian Process for LMP forecasting
- Resource Type
- Conference
- Authors
- Mori, Hiroyuki; Nakano, Kaoru
- Source
- 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP) Intelligent System Application to Power Systems (ISAP), 2015 18th International Conference on. :1-6 Sep, 2015
- Subject
- Computing and Processing
Power, Energy and Industry Applications
Kernel
Forecasting
Power markets
Estimation
Bayes methods
Covariance matrices
Real-time systems
Electricity Price Forecasting
Gaussian Process
Mahalanobis Kernel
Deterministic Annealing
Hierarchical Bayesian Model
Evolutionary Particle Swarm Optimization
- Language
In this paper, an advanced Gaussian Process (GP) model is proposed for electricity price forecasting. This paper focuses on forecasting of LMP (Locational Marginal Price) that maintains the efficiency in power markets in a sense that the transmission congestion is alleviated. The power market players are interested in maximizing the profits and minimizing the risks by selling and purchasing electricity. As a result, an efficient method is required to forecast LMP and evaluate the uncertainties effectively The proposed method makes use of the hybridization of GP, DA clustering and EPSO. GP is an extension of SVM in which hierarchical Bayesian estimation is used to deal with the uncertainties of electricity price forecasting through the error bar. DA clustering of global optimization is used as the prefiltering of GP in a way that GP is constructed at clusters obtained by the clustering method. EPSO is employed to improve the accuracy in MAP estimation for GP. In addition, the Mahalanobis kernel is introduced into GP to enhance the model generalization ability. The proposed method is successfully applied to real data of hourly LMP.