A statistical model for predicting power demand peaks in power systems

The total commodity cost for electricity also includes the cost of building new electricity infrastructure and the expenses of providing conservation and demand response programs. This is called the Global Adjustment (GA) by the Independent Electricity System Operator (IESO), a corporate entity in Ontario, Canada working at the heart of Ontario´s power system to ensure there is enough power to meet the province´s energy needs in real-time and to plan and secure energy for the future. In Ontario, approximately 300 Class A customers representing Ontario´s largest electricity consumers pay GA based on how much they contribute to the 5 highest peak hour demands in a fiscal year. Accurately predicting the top 5 peak hours in a fiscal year may help such a customer minimize its energy consumption in such periods and save it tens of millions of dollars in adjustment cost. In the meantime, this will reduce the size of demand peaks and the need of new electricity infrastructure for exceptionally high peak demands. This paper proposes to learn a statistical model for predicting in real-time the top 5 peak demand hours in a fiscal year, where feature selection is discussed. Preliminary experimental studies indicate the proposed model can effectively help locate the potential peak hours. This work is conducted for an application project, so we also discuss the implementation and deployment details of this model, where a client-server architecture with Ajax is adopted to ensure the updated peak hour information is delivered to all customers in real-time.