Smart building technologies and building automation and control systems (BACS) can offer a range of additional benefits beyond energy savings, in particular by improving building responsiveness to user needs. Although in recent years these technologies have gained popularity as a means of reducing energy consumption and improving building performance, a clear picture of the role of BACS in providing a wider range of benefits is still missing. This review identifies and collects BACS impacts in office buildings with a special emphasis on improving indoor environmental quality by adapting building operation to changing conditions and guaranteeing feedback and real-time interaction with occupants. The resulting benefits, such as increased employee productivity, fewer occurrences of sick leave, and lower rates of absenteeism are highlighted. Offices represent an interesting field of application, as small improvements in the built environment can have a significant impact on labour costs which are the predominant share of the total operating costs. Furthermore, quantitative relationships between physical factors of the indoor environment and benefits have been displayed where available. This literature review aims at establishing an approach that comprehensively evaluates BACS across their entire spectrum, leading to the promotion of novel business cases.

Locating and quantifying photovoltaic (PV) installations is a time-consuming and labor-intensive process, but it is necessary for monitoring their distribution. In the absence of existing data, the use of aerial imagery and automated detection algorithms can improve the efficiency and accuracy of the data collection process. This study presents a machine learning approach for the analysis of PV installations in urban areas based on less complex and resource-intensive models to target the challenge of data scarcity. The first objective of this work is to develop a model that can automatically detect PV installations from aerial imagery and test it based on the case study of Crevillent, Spain. Subsequently, the work estimates the PV capacity in Crevillent, and it compares the distribution of PV installations between residential and industrial areas. The analysis utilizes machine learning techniques and existing bottom-up data to assess land use and building typology for PV installations, identifying deployment patterns across the town. The proposed approach achieves an accuracy of 67% in detecting existing PV installations. These findings demonstrate that simple machine learning models still provide a reliable and cost-effective way to obtain data for decision-making in the fields of energy and urban planning, particularly in areas with limited access to existing data. Combining this technology with bottom-up data can lead to more comprehensive insights and better outcomes for urban areas seeking to optimize and decarbonize their energy supply while minimizing economic resources.

The building sector plays an important role in the transition to a climate-neutral society. The international interest in low, zero energy, or zero-emission buildings has grown as a potential means for this transition. A more ambitious step from zero energy buildings is the concept of Positive Energy Buildings (PEB), which are also contributing to the decarbonization of the surrounding built environment, not just minimizing their own carbon footprint. A shared PEB definition is needed in order to enable the design, assessment, and documentation of positive energy buildings. This article describes the ongoing discussions, concentrating on the most essential points.

The building sector contributes significantly to energy consumption and greenhouse gas emissions, with many buildings being energy inefficient. In response, the European Green Deal promotes improving energy efficiency to support decarbonization goals. However, managing energy consumption and integrating data from multiple sources presents challenges, especially for large building portfolios. This study introduces a novel methodology designed to optimize energy renovation strategies, balancing technical, financial, and maintenance considerations. The methodology is implemented in CERPlan 1.0, a web-based decision-support platform that combines data on building energy performance, renovation costs, and maintenance needs. Through simulations, CERPlan 1.0 helps decision-makers prioritize retrofit interventions based on economic criteria while leveraging synergies between energy improvements and regular maintenance. Application of this methodology to real estate portfolios reveals opportunities to enhance cost-effectiveness and energy savings. The results show that integrating maintenance into renovation planning reduces payback times and allows for more comprehensive renovation strategies. The conclusions highlight CERPlan 1.0’s potential to improve decision-making, making building renovations more efficient and sustainable.