Abstract
The paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the proposed forecaster is compared with forecasters based on classification tree and regression tree. A hybrid FS method based on the elitist genetic algorithm (GA) and a tree-based method is applied for FS. Making use of selected features, aperformance test of the forecaster was carried out to establish the usefulness of the proposed approach. By way of analyzing and forecasts for day-ahead electricity prices in the Australian electricity markets, the proposed approach is evaluated and it has been established that, with the selected feature, the proposed forecaster consistently outperforms the forecaster with a larger feature set. The proposed method is simulated in MATLAB and WEKA software.
Highlights
Efficient and consistent electricity price forecasting is vital for market participants in the preparation of appropriate risk management plans in an electricity market
The research for developing price forecasting tools in non-intervention markets is at an intermediary stage and a variety of forecasting models covering many free trade markets have emerged in recent years [1,2,3,4,5,6]
Electricity price forecasting has been focussed of several researchers in the field of electricity and various authors and researchers have published on this topic
Summary
Efficient and consistent electricity price forecasting is vital for market participants in the preparation of appropriate risk management plans in an electricity market. Anamika et al [21] presented, a combination of various methodology like feed forward neural networks (FFNN), wavelet transform (WT), fuzzy adaptive particle swarm optimization (FA-PSO) for price forecasting on Spanish electricity markets for the year 2002. K. Wang et al [22] suggested a novel approach for electricity price forecasting which consists of three models, first they combined the relief-F algorithm and random forest (RF) and proposed a hybrid FS method based on GCA to remove the feature redundancy. The method consists of two steps, in the initial step, a new hybrid method is introduced to predict point forecasts which is a combination of ELM, WT, FS based on mutual information (MI), and bootstrap approaches and in second step, PSO algorithm is used for improving the forecast accuracy and evaluating the variance of the model.
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