Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms.

In recent years, new technologies, such as Artificial Intelligence, are emerging to improve decision making based on learning. Their use applied to the Architectural, Engineering and Construction (AEC) sector, together with the increased use of Building Information Modeling (BIM) methodology in all phases of a building’s life cycle, is opening up a wide range of opportunities in the sector. At the same time, the need to reduce CO${}_2$ emissions in cities is focusing on the energy renovation of existing buildings, thus tackling one of the main causes of these emissions. This paper shows the potentials, constraints and viable solutions of the use of Machine Learning/Artificial Intelligence approaches at the design stage of deep renovation building projects using As-Built BIM models as input to improve the decision-making process towards the uptake of energy efficiency measures. First, existing databases on buildings pathologies have been studied. Second, a Machine Learning based algorithm has been designed as a prototype diagnosis tool. It determines the critical areas to be solved through deep renovation projects by analysing BIM data according to the Industry Foundation Classes (IFC4) standard and proposing the most convenient renovation alternative (based on a catalogue of Energy Conservation Measures). Finally, the proposed diagnosis tool has been applied to a reference test building for different locations. The comparison shows how significant differences appear in the results depending on the situation of the building and the regulatory requirements to which it must be subjected.

Lightweight construction is experiencing a significant market implementation with sustained growth both for new buildings and retrofitting purposes. Despite the acknowledged advantages of this type of construction, their reduced thermal inertia can jeopardize indoor thermal comfort levels while leading to higher energy consumption due to high indoor temperature fluctuations and overheating rates. The incorporation of phase change materials (PCMs) into constructive solutions for lightweight buildings is a promising strategy to guarantee adequate thermal comfort conditions. Particularly, the utilization of mortars embedding PCMs as an indoor wall coating for new and existing buildings represents a solution that has not been widely explored in the past and needs further development and validation efforts. This work pursues the analysis of the thermal regulation effects generated by two thermally-enhanced mortars incorporating microencapsulated PCMs with different operating temperature ranges. To that end, an experimental campaign was conducted in Valladolid (Spain) to address the investigation of the proposed solution under a real-scale relevant environment. The proposed mortars were applied as an indoor coating to the envelope of a single-zone lightweight construction that was monitored (under different weather conditions along 1-year monitoring campaign) together with an identical building unit where the mortar was not added to the constructive base layer. The analysis of indoor temperature fluctuations under free-floating operating mode as well as the energy consumption of HVAC equipment under controlled-temperature operation was specifically targeted. Results derived from the continuous monitoring campaign revealed lower temperature fluctuations during summer and shoulder seasons, reducing indoor temperature peaks by 1–2 °C, and producing a time delay of 1–1.5 h into the temperature wave. A clear reduction in energy use due to the incorporation of the PCM-based indoor coating panels is also observed. Thus, this experimental research contributes to proving that the use of innovative mortars incorporating embedded PCMs enables the development of high-end efficient building solutions with innovative materials towards a sustainable built environment.

In the current context of a world energy situation that demands high-quality indoor environments compatible with rational energy use, the purpose of this work is to present a successful case study of a comfortable low-energy office building in Madrid, Spain. An exhaustive indoor air quality and thermal comfort field measurement survey was carried out during the summer period. The main global and local thermal comfort parameters in several workplaces, as well as CO₂ concentration in supply air, exhaust air, and occupied space, were registered. Correct operation of the demand-controlled ventilation system and air distribution is observed. Also, thermally activated building systems and shading design make it possible to get high levels of indoor thermal comfort, as derived from measurements. This proves that the integration of novel energy-efficient concepts in office building design can not only contribute to a minimization of the energy use but can simultaneously favor the achievement of high indoor environmental quality.