A Novel Combined Electricity Price Forecasting Method Based on Data Driven Algorithms - Zhnag, Liang; Zou, Bin; Wang, Hongtao (2019 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), 2019)
In the deregulated electricity market, accurate knowledge of electricity price tend helps maximize the profitability of the participants in the electricity market, so electricity price forecasting becomes extremely important. On the basis of not considering the situation of the electricity market itself and many factors affecting the electricity price, the historical load and electricity price are used as inputs to predict the electricity price from the perspective of data driven. The Lasso, Random Forest, Support Vector Machine and BP Neural Network methods are used to establish a single algorithmic electricity price model respectively, and then the linear Lasso and nonlinear BP neural network are used to make combined the prediction results of four single algorithmic electricity price models. Finally, the actual electricity price and load data from Queensland are used for simulation. The simulation results show that: (i) Among the four electricity price models, BP neural network model has the highest accuracy, and the average absolute error is 6.034. The Random Forests model has the worst accuracy, with an average absolute error of 9.669. (ii) The combined nonlinear BP neural network model can predict the electricity price more accurately with an average absolute error of 4.641.