An optimized model of electricity price forecasting in the electricity market based on fuzzy timeseries

Electricity price forecasting in the electricity market is one of the important purposes for improving the performance of market players and increasing their profits in a competitive electricity market. Since the system load is one of the important factors affecting electricity price changes, a two-factorial model based on fuzzy time series is presented in this paper for electricity price forecasting using the electricity prices of the previous days and the system load. In the proposed method, price and system load time series are fuzzified by fuzzy sets created based on the fuzzy C-means clustering algorithm. After determining proposed model coefficients by the Teaching–Learning-Based Optimization algorithm, this model is used for forecasting the next day electricity price. The promising performance of the proposed model is examined using Australia and Singapore electricity markets data.