Electricity Price and Demand Forecasting in Smart Grids

In future smart grids, consumers of electricity will be enabled to react to electricity prices. The aggregate reaction of consumers can potentially shift the demand curve in the market, resulting in prices that may differ from the initial forecasts. In this paper, a hybrid forecasting framework is proposed that takes such dynamics into account when forecasting electricity price and demand. The proposed framework combines a multi-input multioutput (MIMO) forecasting engine for joint price and demand prediction with data association mining (DAM) algorithms. In this framework, a DAM-based rule extraction mechanism is used to determine and extract the patterns in consumers' reaction to price forecasts. The extracted rules are then employed to fine-tune the initially generated demand and price forecasts of a MIMO engine. Simulation results are presented using Australia's and New England's electricity market data.