The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water (DHW) production in residential buildings. This is done by deriving data-driven optimal heating schedules (used interchangeably with policies) automatically. The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism -an air-source heat pump (ASHP) in this case -at the highest possible efficiency. The proposed algorithm, while tested on an ASHP, is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well. Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings (NZEB) in the Netherlands are presented. These show a reduction of energy consumption for DHW by 20% using a computationally inexpensive heuristic approach, and 27% when using a more intensive hybrid ant colony optimization based method. The energy savings are strongly dependent on occupant comfort. This is demonstrated in real world settings for a low-consumption house where active control was performed using heuristics for 3.5 months and resulted in energy savings of 27% (61 kWh). It is straightforward to extend the same models to perform automatic demand side management (ADSM) by treating the DHW vessel as a flexibility bearing device.

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In the past, to make the city liveable, the urban morphology has always be considered taking into account the climate, the buildings' density and characteristics, the type of inhabitants and their social condition. On the contrary, recently in the urban planning process the morphological aspects are no more included even if they influence the energy consumption, the thermal comfort of the urban spaces and the district air quality. Moreover, the socio-economic conditions of inhabitants might strongly affect the lifestyle choice and behavior of building occupants and thus, the probability of success of urban planning measures for energy conservation. The present study aims to: 1) identify the correlation between thermal energy consumption for space heating and urban variables and 2) investigate the role of socio-economic variables in energy savings potential. The city of Turin is suitable for these analyses because it is characterized by different urban forms and urban spaces and by various characteristics of the population. By using a GIS tool, the district 3, chosen as a case study, has been divided into different urban textures considering their urban and socio-economic characteristics. The results of this study show that the measured energy consumption of single building depends on the physical building features (f.i. thermal insulation level, the compactness, the energy system efficiency etc.) but also on the urban form and the streets' orientation. Another important result is that the social and economic situation of inhabitants has a relevant role in the success of sustainable policies. These conclusions may support urban planners in the definition of new urban areas with some "preliminary" energy savings measures at no cost and in formulating tailored policies according to socio-economic conditions from district to district.