In a changing global energy market where the decarbonization of the economy and the demand growth are pushing to look for new models away from the existing centralized non-renewable based grid. To do so, households have to take a ‘prosumer’ role; to help them take optimal actions is needed a multi-step ahead forecast of their expected energy production and consumption. In multi-step ahead forecasting there are different strategies to perform the forecast. The single-output: Recursive, Direct, DirRec, and the multi-output: MIMO and DIRMO. This thesis performs a comparison between the performance of the differents strategies in a ‘prosumer’ household; using Artificial Neural Networks, Random Forest and K-Nearest Neighbours Regression to forecast both solar energy production and grid input. The results of this thesis indicates that the methodology proposed performs better than state of the art models in a more detailed household energy consumption dataset. They also indicate that the strategy and model of choice is problem dependent and a strategy selection step should be added to the forecasting methodology. Additionally, the performance of the Recursive strategy is always far from the best while the DIRMO strategy performs similarly. This makes the latter a suitable option for exploratory analysis.