Peak load estimation is a critical element in utility decision making. An electric utility must construct, maintain, and operate equipment sufficient to meet the peak, or maximum demand level. Electric utilities have utilized several methods of analyzing energy consumption which have yielded various levels of accuracy for estimating maximum demand. In this research, a new method, Nonlinear Load Research Estimation (NLRE), is proposed to estimate the peak load and load characteristics of individual and group of different customer classes. Load research data is used to develop KWHr-to-KW conversion factors, diversity factors, and average time-varying load data as a function of customer class, month, and type of day. Load research data is used to derive monthly load shapes by customer class. These profiles are used to apportion customer KWHr usage among billing cycles according to the split indicated by test data. This parsed KWHr can be further allocated based on type of day to estimate weekday and weekend peak values. / Ph. D.