Load balancing is an important topic in smart grid systems. Dynamic pricing is a common approach to achieve a better balance between renewable energy production and energy usage. This assumes that individual households adapt their energy usage patterns based on energy prices. However, the actual behaviour of consumers in a household is an uncertain factor that might influence the effectiveness of pricing strategies. In this paper, we investigate to what extent knowledge about actual user behaviour can contribute to local optimization of energy usage. We use simulations to study whether a smart heating system that applies a pre-heating strategy for domestic water during periods of low prices can benefit from good predictions of the user behaviour, in financial terms or in terms of energy saving. Also, we use the simulations to investigate the effect of different goal temperatures for the pre-heating strategy. The results show that pre-heating does not make a difference with respect to the energy efficiency, but that during cold months, pre-heating can result in a financial benefit. In addition, we calculate what certainty about the user behaviour is needed to be able to effectively use pre-heating during the warmer summer month. These results can help to design residential energy optimization systems.