The market of energy services for the residential sector in Europe is very limited at present. Various reasons can be argued such as the high transaction costs in a highly fragmented market and the low energy consumption per dwelling. The rather long payback time for investments render Energy Services Companies’ (ESCOs) services financially unattractive for many ESCOs and building residents, thus hindering a large potential of energy savings in a sector that is responsible of almost half of Europe’s energy consumption. If the ambitious 2030 and 2050’s decarbonisation targets are to be met, the EU’s residential sector must be part of the solution. This paper offers insights about novel ESCO business models based on intensive data-driven Artificial Intelligence algorithms and analytics that enable the deployment of smart energy services in the domestic sector under a Pay-for-Performance (P4P) approach. The combination of different sources of energy efficiency services and the optimal participation of domestic consumers in aggregated demand response (DR) schemes open the door to new revenue streams for energy service providers and building residents and reduce the hitherto long payback periods of ESCOs services in the sector. Innovative business models for ESCOs and demand flexibility Aggregators are thoroughly described. Especially customised Performance Measurement and Verification protocols enable fair and transparent P4P ESCO contracts. The new human-centric energy and non-energy services increase the energy consumption awareness of building users and deploy behavioural and automated responses to both environmental and market signals to maximise the economic benefit for both energy service providers and consumers, always respecting data protection rules and the consumers’ comfort preferences. The new hybrid business models of P4P energy services make traditional EPC more attractive to energy service providers, with low cost data collection and treatment systems to bring payback periods below 10 years in the residential building sector.

Ahead of the energy crisis, several countermeasures to reduce the energy demand require an active change in the end users’ energy patterns. There are strategies known as demand response (DR) programs that have been tested in recent years, and they have showed us that users’ behaviours can considerably reduce their effectiveness. This is due to a lack of sufficient knowledge, which leads to a lack of acceptance and participation. The key aim of this research is to detect which aspects influence acceptance of energy flexibility strategies the most. Through a series of tools, such as direct questionnaires, brief and user-friendly explanations, and analysis of variance, these aspects are studied by delving into specific topics such as smart home technologies, change in habits and patterns of energy use, and DR programs. In the literature, these topics have been studied separately, but they all contribute to the global acceptance: a comprehensive vision of the matter is the novelty of this work. The key findings are encouraging: 72% of the respondents demonstrated their willingness to enrol in a DR program. A reluctance to change habits was shown, in particular, among the age range 26–40, while women were more environmentally aware and more likely to participate in energy flexibility strategies. The modality of direct load control (which gives control to the utility company) is confirmed to be harder to be accepted (from 13 to 27% less acceptance depending on the category), with people who share a flat being the most likely to try it, and people who live with their parents being the less inclined ones. Acceptance increased when we provided plain language explanations, as seen in the case of smart home technologies: 97% of people who never tried them were declared to be willing to test smart technologies after a simple elucidation that was included in the questionnaire, showing that a right approach to the users led to a greater show of interest. This research highlights that the users’ background and demographics characteristics (namely age, gender, educational level, home situation, home tenure, presence of children, and average income) should be taken into account when it comes to designing new energy flexibility strategies, since differences in the acceptance among groups have been found. The work also presents insights on the payback periods of legacy equipment in the EU energy context, demonstrating that a timely intervention can require half the time compared to that of the period prior to the crisis.