6. Conclusion and future work

Householders' profiles and patterns will allow electricity suppliers to understand consumers' behavior, set up more flexible and customized electricity prices to avoid peak consumption. One the other hand, prosumers will benefit from the forecasting solutions that will estimate wind and PV generation, therefore they will schedule their appliances according to electricity prices and their generation resources.

From our experiments, we consider artificial neural networks a good solution for determining the consumption profiles, for short-term load forecasting on each profile and also for shortterm micro-generation forecasting.

A disadvantage of neural networks is that the most appropriate solution in a particular case is found by successive attempts on the number of hidden layers and the number of neurons on each layer, so in the case of another set of data from another geographic area with different characteristics regarding consumption or meteorological conditions that affect the wind or solar generation, it is necessary to re-configure the ANN parameters.

An advantage of artificial neural networks in case of consumption and generation forecasting is that they perform predictions with very good results in a very short time, which makes ANN particularly useful for real time short-term forecasting.
