In forecast modelling, the feature selection techniques are a significant step of data preprocessing prior to creating the prediction model. Selecting the most significant input features is important to increase the prediction accuracy and minimize the data and training time. In this paper, some feature selection techniques are compared and analyzed. Next, the feature selection techniques are used as a filter prior to electricity price forecasting and their influence on prediction accuracy and mean absolute percentage error (MAPE) of each selected subset are compared.