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Nowadays, energy efficiency is a major issue in modern day societies, due to increasing worldwide energy demands. Having this in mind several different solutions are emerging with the purpose of helping the control of energy in all possible ways, whether at its starting pipeline, i.e. where the energy is produced, at the middle pipeline, i.e. where and how the energy is transported, or finally, at the end of the pipeline, where the energy is consumed. At the moment, most solutions are addressing the problem at the end of the pipeline, because it is easier to control the consumption, than it is to alter all of the parts that compose an energy system.Thus, the solution proposed in this paper refers to the development of a platform capable of providing energy prediction on buildings, whether the building is commercial, industrial or residential. The platform will be composed of prediction algorithms, supported by the use of computational intelligence methods such as Artificial Neural Networks (ANN). The main objective of this platform is to use datasets previously recorded of the building energy consumption, along with a number of other parameters, to accurately predict the energy consumption of a given day, so that future, and pondered actions can be taken in order to provide a suitable response for that given day. Technically, the platform itself will be based on standard online remote communication protocols, and this platform is to be integrated with, amongst other equipment, energy meters.

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