Classification of Data Mining Techniques for Electricity Load Forecasting

The global requirement for electricity is increasing daily with the expansion of infrastructure and the addition of new appliances. Precise power consumption forecasting is of major importance for government to define the future power requirement of a given region. This is one of the most scientifically and technologically challenging problem. In this paper, we present the review of available literatures of some data mining techniques and algorithms used by other researchers for short term and long term electric load forecasting. Data mining operations are performed on historical and secondary data to develop a prediction model which is capable of predicting electricity consumption. The work that has been done by various researchers in this field has been reviewed. The review has outlined a number of promising techniques like ARIMA, MLR, ANN, FLN, SVM, Gradient boosted tree, K-NN, Holt-winter, Very Fast Decision Tree, Fuzzy logic and Genetic algorithms etc that have been used to understand the relationships of various factors on electric power prediction