Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei - Alireza , Pourdaryaei (2020)
Electricity price forecasting is considered as one of prime factors for operation, planning and scheduling of price-setter market participants. However, possessing time variant, non-linear and non-stationary behaviors make the electricity price a complex signal. The main challenge in this area is providing highly accurate and efficient day-ahead price forecasting. A suitable feature selection technique, which is able to model the interacting features and nonlinearities of the forecast processes, is still required although researches have been performed for day-ahead forecasting. In this research, a hybrid electricity price forecasting methodology is proposed using two-stage feature selection method and optimization using adaptive neuro-fuzzy inference system (ANFIS) technique as a forecasting engine. An important contribution of the proposed method is modeling of interaction in addition to relevancy and redundancy based on information-theoretic criteria for the feature selection. A multi-objective feature technique is developed in this study to extract the most influential subsets of input variables with the maximum relevancy and minimum redundancy. The proposed feature selection technique comprises of Multi-objective Binary-valued Backtracking Search Algorithm (MOBBSA). It is used to search within a number of input variables combinations and to select the feature subsets, which minimizes simultaneously vice-versa the estimation error and the feature numbers. In the developed method of multi-objective feature determination, MOBBSA is used to search within different combinations of input variables and to select the non-dominated feature subsets. ANFIS is applied as an evaluation metric to determine the performance of every feature subset. The other foremost contribution of the work is proposing a hybrid electricity price forecasting technique to provide more accurate forecasts. This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through the use of Backtracking Search Algorithm (BSA) as an efficient optimization algorithm in learning process of ANFIS approach. Real-world electricity demand and price dataset from Ontario and Australia power markets, which are reported as among the most volatile market worldwide, have been used to validate the performance of the proposed approach. Finally, the obtained results corroborate the premise of the proposed method through the enhanced accuracy compared to the existing artificial intelligence-based models.