Abstract: This article describes the development of a prediction method for the demand for electrical energy of a marketer's customer portfolio. The project is motivated by the economic benefit produced when the entity has accurate estimates of energy demand when buying energy in an electricity auction. The developed system is based on time series analysis and machine learning. As this system was part of a real-world project with data from a real environment, the article focuses on practical aspects of the design and development of system of these characteristics, such as the heterogeneity of data sources, and the delay in data availability. The predictions obtained by the developed system are compared with the results of a simple method used in practice.

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