Electricity price and demand forecasting under smart grid environment

In this paper, the development of electricity price and demand forecasting, with the emergence of demand response programs, is investigated. Short Term Load/Price Forecasting (STL/PF) is performed for an electricity market that offers Demand Response (DR) Programs. The change in the forecasting errors, of both electricity price and demand, over years of inactive and active DR is monitored. Commonly used prediction methods, namely; Least Squares-Support Vector Machines (LS-SVM), and Random Forests (RF), are used for forecasting, to ensure the generality of the results. The Australian National Electricity Market (ANEM), specifically Victoria region, is used as a subject case study. It was concluded that adding DR programs decreases the volatility of electricity price, with no validated effect on demand.