In Lebanon, electric power is becoming the main energy form relied upon in all economic sectors of the country. Also, the time series of electrical energy consumption in Lebanon is unique due to intermittent power outages and increasing demand. Given these facts, it is critical to model and forecast electrical energy consumption. The aim of this study is to investigate different univariate-modeling methodologies and try, at least, a one-step ahead forecast for monthly electric energy consumption in Lebanon. Three univariate models are used, namely, the autoregressive, the autoregressive integrated moving average (ARIMA) and a novel configuration combining an AR(1) with a highpass filter. The forecasting performance of each model is assessed using different measures. The AR(1)/highpass filter model yields the best forecast for this peculiar energy data. (C) 2001 Elsevier Science Ltd. All rights reserved.