Heat Pump Control in Urban Buildings: Methods and Deployment Barriers

Abstract

Purpose Heat pumps are central to the decarbonisation of urban building stock, yet their real-world performance in European cities often falls short of nominal efficiency. A major limitation is often not the heat-pump unit itself, but system-level control and integration, including supervisory control, hydraulic integration, commissioning quality, and adaptation to user behaviour, weather conditions, and grid signals. This review examines why advanced controls that perform well in laboratories and simulations have not translated into widespread urban deployment. Methodology This paper reviews 34 peer-reviewed publications, one preprint, and seven authoritative technical, market, and regulatory sources published between 2012 and 2026. The review covers classical control, model predictive control, reinforcement learning, physics-informed and data-driven hybrid methods, smart-grid flexibility schemes, and hardware trends shaping heat-pump integration in existing urban buildings. Sources were grouped by methodological family and application context, with particular attention to experimentally validated and field-deployed studies. Main findings Across the reviewed advanced control approaches, the gap between laboratory performance and practical deployment is mainly driven by four recurring barriers: (i) the cost of building-specific model identification, which scales poorly across the hydraulic and architectural diversity of urban housing stock; (ii) safety and fallback requirements that learning-based controllers cannot yet reliably satisfy in occupied dwellings; (iii) limited generalisation across installations; and (iv) data inefficiency, which conflicts with the slow seasonal dynamics of real heating systems. Emerging hydraulic and sensing technologies address some physical preconditions but are not yet systematically combined with adaptive control. Recommendations Overcoming these barriers requires progress in five connected areas: (i) standardised benchmarking and field validation, (ii) scalable modelling and transfer learning, (iii) safe and data-efficient control with reliable fallback, (iv) hydraulic-control co-design, and (v) smart-grid integration and economic evaluation. Together, these measures can help move advanced heat-pump control from laboratory studies to wider deployment in urban buildings.