Fuzzy Methodologies for Forecasting Usage and Wet-Bulb Data Variables

New fuzzy algorithms for predicting unknown usage and wet bulb variables, like temperature and humidity, are developed. The algorithms define fuzzy memberships to usage and wet bulb data variables based on weighted membership function. Fuzzy similarity matrices, using all the possible pairs of memberships, are computed to predict the future values, where Euclidian distance is used to calculate a similarity between a pair of memberships. Experimental results of the proposed methods for prediction of future usage and the wet bulb variables are provided.