Electricity demand forecasting is a major problem for the electricity sector, because the energy market players use the results of the electricity demand forecasting to make the right decisions for their work. This article presents an analysis of models and techniques used in the electricity demand forecasting and explain the problems or difficulties that researchers have when making a forecast. Our analysis shows that the most used techniques are the ARIMA model and artificial neural networks. However, it appears unclear evidence on which model is most appropriate and in what cases, in addition, the studies do not present a specific recommendation to develop models for forecasting demand, specifically in the Colombian case. Finally, we propose to make a systematic study to determine the most appropriate models for forecasting demand for the Colombian case.