Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
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.
2017.
Multi-step, forecast, strategies, Recursive, Direct, DirRec, DIRMO, MIMO, Artificial Neural Networks, Random Forest, K-Nearest Neighbours Regression, MAPE, MAE