Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon

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 en ergy 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 dif ferent univariate-modeling methodologies and try, at least, a one-step ahea d forecast for monthly electric energy consumption in Lebanon. Three univar iate models are used, namely, the autoregressive, the autoregressive integr ated moving average (ARIMA) and a novel configuration combining an AR(1) wi th 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.