Electricity is one of the mandatory commodities for mankind today.
Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids.
In the first stage, disaggregation of the load into fixed and shiftable components is accomplished by means of a hybrid algorithm consisting of non-negative matrix factorization (NMF) and Gaussian mixture models (GMM), with the latter trained by an expectation-maximization (EM) algorithm.
Due to uncertain demand of electricity at receiving end of station causes several challenges such as: reduction in performance parameters of generating and receiving end stations, minimization in revenue, increases the jeopardize for the utility to predict the future energy need for a company etc.
Time series forecasting remains a central challenge problem in almost all scientific disciplines, including load modeling in power systems engineering.
One of the consequences of this policy is a significant change in electricity consumption patterns.
We also present the challenges associated with the real-time deployment of such systems as well as the research opportunities presented by a fully functional forecasting system that has been developed within the National Renewable Energy Laboratory's Intelligent Campus program.