PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHMS IN ELECTRIC LOAD FORECASTING
Keywords:
Classification, Load Forecasting, Naïve Bayes, Random Forest, ID3, Machine Learning, PerformanceAbstract
Nowadays, we have data in abundant from numerous sources. Retrieving useful information from this
data is very tedious task. Hence, to address the complex nature of various real world data problems, specialized
machine learning algorithms have been developed that solve these problems perfectly. Machine learning is a type of
artificial intelligence that allows software applications to become more accurate in predicting outcomes without
being explicitly programmed and has gained much popularity in recent years. This paper explores evaluation
performance of Naïve Bayes, ID3, and Random Forest on electricity consumption or load datasets. Naïve Bayes
algorithm is depending upon likelihood and probability; it is fast and stable to data changes. In decision tree
learning, ID3 (Iterative Dichotomiser 3) algorithm is used to generate a decision tree from a dataset and is typically
used in the machine learning and natural language processing domains. Random forest algorithm is an ensemble
algorithm that fits multiple trees with subset of data and averages tree result to improve performance and control
over-fitting. This paper concludes with a comparative evaluation of Naïve Bayes, ID3 and Random Forest in the
context of Electric Load Dataset in order to forecast the electricity load or consumption.
