Abstract
Energy consumption in a pilgrim city belonging to a Gulf Cooperation Council (GCC) country exhibits strong seasonal pattern due to higher demand in summer season and additional load during the pilgrimage months. The pilgrimage month's timing is not fixed in the Gregorian calendar. The event varies according to the lunar calendar called the Hegira calendar, which lags behind the former by approximately 14 days in a year. Ten seasonal demand models are developed to model energy estimate for a GCC pilgrimage city. Among the long-range forecast models, three trigonometric models, a multiplicative model, and a multivariate model using categorical variables are considered. Further, a composite nonlinear model whose coefficients are nonlinear is suggested. This model combines the seasonality extracted from a multivariate regression model and a model that represents the peak electric load pattern. Adopting least square fit of a chi-square error function expanded by parabolic expansion, the parameters of the nonlinear model are identified. Moreover, smoothing-based techniques, such as moving average, double exponential smoothing, Winter's, and a multiplicative seasonal model, are suggested. The peak electric load model on lunar and solar calendars is closely related, and the deference in fitting error can be attributed to the magnitude of data. Computational results and statistical tests are presented to analyze the models. It is observed that the multiplicative model performs better to predict the peak electric load demand.