The present study introduces and thoroughly investigates a novel smart heating, ventilation, and air conditioning system with thermal storage in a newly built commercial building in Uppsala, Sweden. The system combines 25 double U-tube borehole thermal energy storage, district heating, and intelligent control strategies to effectively manage heating and cooling demands for offices and restaurants. A novel optimal adaptive control framework dynamically adjusts the radiator supply temperature by accounting for solar radiation, ventilation flow rate, occupancy gains, and outdoor temperature. These modifications are optimized using the particle swarm method to enhance thermal comfort and energy efficiency. The proposed framework is compared with the existing control system based solely on outdoor temperature from techno-economic, environmental, and comfort aspects. According to the results, the outdoor temperature history and wind velocity have minimal effects on heating demand deviations, while solar radiation, occupancy gains, and ventilation performance play significant roles. The results further indicfate that solar radiation is the most influential factor in warmer months, whereas occupancy and ventilation gain are more important in colder months. Results demonstrate substantial enhancements in thermal comfort, with the weighted temperature deviation index reduced by 72.7% and the comfort consistency ratio increased by 54.4%. The designed adaptive controller reduces the annual heating supplied to radiators and the payback period by 13.2% and 9.0%, respectively, and decreases CO2 emissions and the index by 9.4% and 2.6%, respectively. After 20 years, the adaptive controller outperforms the basic model in terms of profit, increasing it by 20.4% to 190,260 USD, proving its economic superiority in the long run. In transitional months like April (14.9 MWh, 56.3% of the total) and May (15.9 MWh, 69.9%), when efficient solar gains reduce heating demands, the suggested adaptive controller also has substantial monthly energy savings.

Global floor area is increasing every year which is subsequently leading to an increase in electricity and heating demand in buildings. Residential buildings have huge potential for energy savings and there is an immediate need to decarbonize them by the end of 2050. Machine learning is finding application across all fields and will thus have an important role to play in the building sector also. One of the important challenges that building owners need to tackle is occupancy detection in residential apartments which can help save considerable amounts of energy and costs. However, occupancy is highly variable, and it is difficult to quantify and predict occupancy because of the random and individualistic nature of humans. In addition, scalable approaches for occupancy detection should prioritize data from common and cost-effective sensors like temperature sensors. In contrast to existing literature which has stated that occupancy detection based on the data from a single environmental sensor is not appropriate for obtaining good results, this paper aims to detect occupancy in a real residential building using only indoor temperature as the feature to train the model. Different machine learning models and techniques are studied and tested to understand how the accuracy of occupancy detection can be increased. With the right techniques, it has been possible to obtain promising results in the form of an accuracy of 95% using machine learning models and only indoor temperature to train it.

Building and construction sector is responsible for 40% of the total energy consumption and 36% of the total greenhouse gas emissions in the European Union. Digital twin is an emerging digital tool that facilitates building management through data interactions using sensor readings between a physical building and its digital model and improves operation and enhances transparency. However, since the digital twin technologies are not mature and has several challenges associated with it, such as need for extensive data, it is necessary to conduct a systematic literature review on its application to buildings and smart grids. The majority of the current studies look into how digital twins can be used for the management of normal residential or commercial buildings that are connected to conventional electricity grids with little scope for bidirectional power flow. This study conducts a systematic literature review to map the current landscape of research on digital twins in grid-interactive buildings, with a focus on identifying the software tools used in the creation of digital twins for improving energy efficiency. The study uses scientific databases like Scopus and Web of Sciences and has been carried out in accordance with PRISMA guidelines that specify the different steps involved in the methodology to conduct systematic reviews. Autodesk Revit and Artificial Neural Networks emerged as the most common software and technique, based on previous works.

Building and construction sector is responsible for 40% of the total energy consumption and 36% of the total greenhouse gas emissions in the European Union. Digital twin is an emerging digital tool that facilitates building management through data interactions using sensor readings between a physical building and its digital model and improves operation and enhances transparency. However, since the digital twin technologies are not mature and has several challenges associated with it, such as need for extensive data, it is necessary to conduct a systematic literature review on its application to buildings and smart grids. The majority of the current studies look into how digital twins can be used for the management of normal residential or commercial buildings that are connected to conventional electricity grids with little scope for bidirectional power flow. This study conducts a systematic literature review to map the current landscape of research on digital twins in grid-interactive buildings, with a focus on identifying the software tools used in the creation of digital twins for improving energy efficiency. The study uses scientific databases like Scopus and Web of Sciences and has been carried out in accordance with PRISMA guidelines that specify the different steps involved in the methodology to conduct systematic reviews. Autodesk Revit and Artificial Neural Networks emerged as the most common software and technique, based on previous works.

Modern heat pump systems often come equipped with sensors, enabling the collection of substantial operational data. However, many residential heat pumps installed in preceding decades lack pressure sensors, energy meters, or mass flow meters, primarily due to financial limitations. As a result of these incomplete measurements, the direct analysis of the heat pump system’s performance or the leveraging of the amassed data for inventive applications like prognosticating energy consumption, detecting and diagnosing faults, and implementing intelligent control becomes challenging.In existing literature, the focus of soft sensors in heat pump systems has been on estimating a single parameter. This approach, however, overlooks the reality that multiple parameters are often missing due to the lack of all-encompassing physical meters and sensors. Furthermore, current soft sensor models are typically developed using inputs such as compressor power consumption, pressures, evaporation, and condensation temperatures. These inputs, unfortunately, tend to be inaccessible within existing heat pump monitoring installations.In practice, it is a challenge to compensate for several critical measurements, encompassing mass flow rate, pressures, power consumption, and heating capacity, by using only commonly available sensors such as secondary loop temperatures and compressor frequency are available. Currently, there is a notable gap in research concerning this practical issue.To address the problems associated with inadequate measurements, this study presents the development and validation of soft sensors based on a data-driven approach, which can compensate for the parameters often unavailable with data collected from a limited number of commonly used sensors. Each component model employs a multivariate polynomial regression that calculates the evaporation temperature, condensation temperature, mass flow rate, and compressor power consumption, respectively. Subsequently, we present an integrated heat pump model that combines these component models into a comprehensive heat pump model.Finally, we validate the data-driven model against field test installations, demonstrating its accuracy with a relative root mean squared error (RRMSE) ranging from 10% to 20%.

As the transition away from fluorinated refrigerants occurs due to F-gas and PFAS regulations, heat pumps face the challenge of adapting to new non-fluorinated refrigerants. Evaluating heat pump performance during this transition is challenging due to limited operational data on the new refrigerants. Conducting long-term tests to fully understand a heat pump’s performance with all possible refrigerants is labor-intensive and economically burdensome. This study introduces two complementary reduced-parameter models to assess heat pump performance across multiple new natural refrigerants despite limited data. A transfer learning model, leveraging knowledge from existing data-rich refrigerants, has been developed to evaluate the performance of heat pumps using new, data-scarce natural refrigerants. However, due to the lack of transparency in transfer learning models, semi-empirical models are being developed in parallel. The semi-empirical models, across multiple natural refrigerants, are capable of analyzing the thermodynamics and heat transfer processes within the heat pump system by utilizing only limited easy-to-measure variables as inputs. The transfer learning model demonstrates high accuracy for all outputs across seven refrigerants with RRMSE all below 7%. In comparison, the semi-empirical models are less accurate, with RRMSE results under 25% for all parameters except compressor power. By integrating these two models, a comprehensive framework is established for assessing heat pump performance with both high accuracy and a deeper understanding of the system.

The development of smart sensors, low cost communication, and computation technologies enables continuous monitoring and accumulation of tremendous amounts of data for heat pump systems. But the measurements, especially for domestic heat pump, usually suffer from incompleteness given technical and/or economic barriers, which prevents database of measurements from being exploited to its full potential. To this end, this work proposes a data-driven soft sensor approach for compensating multiple missing information. The soft sensors are developed based on an ANN model, an integrated multivariate polynomial regression model and empirical model by considering different constrains like data and information availability during model establishing process. All the three models have been validated against the data from a field test installation, and showed good performance for all the compensated variables. Of the three models, the ANN model shows the best performance for all soft sensors, but it has the highest requirement for additional resources to collect training data. While the integrated multivariate polynomial regression model demonstrates excellent accuracy for the majority of soft sensors with manufacturers' subcomponent data which needs no extra cost. Even though empirical model is not as accurate as the other two models, it still performs good accuracy with limited information from performance map. The methods developed in the present study paves the way for available measured data in thousands of installations to be fully utilized for innovative services including but not limited to: improved heat pump control strategies, fault detection and diagnosis, and communication with local energy grids.

International Energy Agency predicts that the global number of installed heat pumps (HP) will increase from 180 million in 2020 to approximately 600 million by 2030, covering 20% of buildings heating needs. Electric power consumption is one of the main key performance indicators for the heat pump systems from techno-economic perspective. However a common issue prevalent in many existing heat pumps is the lack of electric power measurement. The modern installations might be equipped with electric power measurement sensors but this comes at a higher system cost for the manufacturers and end-users. The primary objective of this work is to propose a virtual measurement for estimating power consumption, thereby eliminating the need for field measurement of power for heat pumps. To achieve the objective, a data-driven approach is proposed. Firstly, the in-situ data is preprocessed through data merging, cleaning, and normalization. Then, input features are pre-selected using Spearman correlation coefficients, and further refined by addressing multicollinearity problem. Following this, Extreme Gradient Boosting (XGBoost) models and polynomial models are developed by considering different features as inputs. All models are finally validated against the in-situ data from multi-units of ground source heat pump (GSHP) and air source heat pump (ASHP) installations. The results showed that the electric power consumption of GSHP can be estimated with high accuracy (99% for R2, 10 W for MAE, and 1% for MAPE) through generic data-driven models using only four easy-to-measure input features. Taking three input features as inputs for ASHP generic model, the accuracy can be reached to 83% for R2, 125 W for MAE, and 9% for MAPE. The method presented in this paper can be applied to estimate power consumption of millions of heat pumps and consequently add a significant value as well as provide different types of services, such as cost-saving benefits for manufacturers and end-users, flexibility services for aggregators and electricity grids.

The adoption of innovation in the building sector is currently too slow for the ambitious sustainability goals thatour societies have agreed upon. Living labs are open innovation ecosystems in real-life environments usingiterative feedback processes throughout a lifecycle approach of an innovation to create sustainable impact. In thecontext of the built environment, such co-creative innovation and demonstration platforms are needed tofacilitate the adoption of innovative technologies and concepts for more energy-efficient and sustainablebuildings. However, their feasibility is not extensively proven. This paper illustrates the implementation anddemonstrates the feasibility of the Living Labs Triangle Framework for buildings living labs. This conceptualframework has been used to conceive the KTH Live-In Lab, a living lab for buildings. The goal of the Live-In Labwas to create a co-creative open platform for research and education bridging the gap between industry andacademia, featuring smart building demonstrators. The Living Lab Triangle Framework has been deployed tomeet the goals of the Live-in Lab, and the resulting concept is described. This paper then analyses the meth-odological and operational results introducing performance metrics to measure the economic sustainability, thepromotion of multidisciplinary research and development projects, dissemination and impact. The results arecompleted with a SWOT analysis identifying its current strengths and weaknesses. The results collected in thiswork fill a missing gap in the scientific literature on the performance of living labs and provide empirical evi-dence on the sustainability and impact of living labs.

Digitalization offers new, unprecedented possibilities to increase the energy efficiencyand improve the indoor conditions in buildings in a cost-efficient way. Smart buildings are seen bymany stakeholders as the way forward. Smart buildings feature advanced monitoring and controlsystems that allow a better control of the buildings’ indoor spaces, but it is becoming evident that themassive amount of data produced in smart buildings is rarely used. This work presents a long-termevaluation of a smart building testbed for one year; the building features state-of-the-art monitoringcapability and local energy generation (PV). The analysis shows room for improving energy efficiencyand indoor comfort due to non-optimal control settings; for instance, average indoor temperaturesin all winter months were above 24 ◦C. The analysis of electricity and domestic hot water use hasshown a relevant spread in average use, with single users consuming approximately four times morethan the average users. The combination of CO2 and temperature sensor was sufficient to pinpointthe anomalous operation of windows in wintertime, which has an impact on energy use for spaceheating. Although the quantification of the impact of users on the overall energy performance ofthe building was beyond the scope of this paper, this study showcases that modern commercialmonitoring systems for buildings have the potential to identify anomalies. The evidence collectedin the paper suggests that this data could be used to promote energy-efficient behaviors amongbuilding occupants and shows that cost-effective actions could be carried out if data generated by themonitoring and control systems were used more extensively.