Energy-efficient Building Operation Research Articles

Towards a Digital Representation of Building Systems Controls

Energy monitoring and performance optimization processes play a significant role in ensuring energy-efficient building operation. However, the current implementation of these processes requires substantial time and effort due to the fragmented and non-digital nature of technical building equipment information. To address these challenges, we demonstrate the application of the PLCont methodology within an ongoing monitoring process in a real building. PLCont utilizes Semantic Web Technologies and established RDF ontologies to describe the control topology. The digital representation of control functions aligns closely with the IEC standard 61131-3, and the PLCont ontology establishes a connection between the topology and the control functions. We compare the conventional approach and the PLCont methodol-ogy using the supply air temperature control of an Air Handling Unit (AHU) as a use case. A graphical user interface built upon the PLCont backend provides a comprehensive represen-tation of the system's functionality. It displays the system topology as a digital HVAC schema, the function code exported from the PLC, time-series data plots, and textual descriptions of implemented functions. We demonstrate how this approach enhances transparency and facil-itates the identification and elimination of faulty system behavior, ultimately contributing to the optimization and energy efficiency of building systems.

Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls

Developing personalised thermal comfort models to inform occupant-centric controls (OCC) in buildings requires collecting large amounts of real-time occupant preference data. This process can be highly intrusive and labour-intensive for large-scale implementations, limiting the practicality of real-world OCC implementations. To address this issue, this study proposes a thermal preference-based HVAC control framework enhanced with Active Learning (AL) to address the data challenges related to real-world implementations of such OCC systems. The proposed AL approach proactively identifies the most informative thermal conditions for human annotation and iteratively updates a supervised thermal comfort model. The resulting model is subsequently used to predict the occupants’ thermal preferences under different thermal conditions, which are integrated into the building’s HVAC controls. The feasibility of our proposed AL-enabled OCC was demonstrated in an EnergyPlus simulation of a real-world testbed supplemented with the thermal preference data of 58 study occupants. The preliminary results indicated a significant reduction in overall labelling effort (i.e., 31.0%) between our AL-enabled OCC and conventional OCC while still achieving a slight increase in energy savings (i.e., 1.3%) and thermal satisfaction levels above 98%. This result demonstrates the potential for deploying such systems in future real-world implementations, enabling personalised comfort and energy-efficient building operations.

Virtual PMV sensor towards smart thermostats: Comparison of modeling approaches using intrusive data

Smart thermostats are considered an effective digital technology for building energy efficiency. This study proposes a virtual predicted mean vote (PMV) sensor for smart thermostats for indoor thermal comfort and energy-efficient building operation and control. The proposed virtual sensor can observe the representative PMV for the occupied zone, considering the systematic errors caused by the spatial difference between the wall-mounted thermostat and the occupied zone. Virtual PMV sensor-based smart thermostat has the potential to improve energy efficiency and thermal comfort by observing unmeasured thermal comfort within the occupied zone based on variables obtained from an existing wall-installed thermostat. To cover systematic errors for general applications, intrusive measurements using a commissioning period are necessary, even in the short term. Thus, conventional modeling approaches can be used with limited intrusive data to develop an accurate in-situ virtual PMV sensor during operation. In the application into a small-sized office building, the real systematic errors showed up to −2.4 °C among ten different thermostat locations in the target office zone. The in-situ virtual sensor could accurately observe the PMV for an occupied zone for different air-conditioning periods (air-conditioned working hours, non-air-conditioned working hours, and closing hours) with the best accuracy of 0.05 mean absolute error (MAE) using one-day intrusive dataset. Based on comparing white-box, black-box, and gray-box virtual sensors in the application, this study recommends using the gray-box modeling approach to leverage the synergetic effect between data and knowledge for better in-situ virtual sensor modeling under a limited intrusive dataset.

Real-world implementation and evaluation of a Model Predictive Control framework in an office space

In this work, Model Predictive Control (MPC) is experimentally implemented on a Heating, Ventilation and Air Conditioning (HVAC) system of a large-scale office space. As controller model, a physics-based Modelica building model is calibrated based on historic data pursuing an iterative, nonlinear optimization approach. For the calibration period with a horizon of seven weeks, the calibrated model exhibits a high model accuracy with a Root Mean Square Error (RMSE) of 0.49 °C between the measured and estimated room temperature. The MPC toolchain includes modules for state estimation and forecasts of disturbances quantities (such as outdoor temperature, solar radiation including calculation of the direct and diffuse fraction, supply temperatures and occupancy). The MPC execution comprises the operation of heating based on radiators and floor heating (via regulation of valve openings) as well as shading of three Venetian blind systems (via regulation of vertical position and slat inclination angle). The experiment is conducted during a heating period with a duration of three weeks from October 21 to November 11, 2022. The heating actuators are controlled considering their typical dynamics and take into account the night setback during unoccupied office periods. User acceptance of the automated shading control is included through additional cost function terms for the shading operation. The field test reveals the predictive control capabilities of the proposed MPC toolchain in a real-life scenario, demonstrating energy-efficient building operation and total average discomfort of 0.53 Kh/d. The MPC formulation provides flexibility regarding adjustability of the control towards energy efficiency, thermal comfort, daylight transmission and non-oscillating shading control. Finally, the disturbance forecast accuracies for outdoor temperature and the solar radiation quantities are evaluated and the MPC control performance is compared against a conventional control approach.

In situ model fusion for building digital twinning

Building digital twins are crucial in holistic, synthetic, and supervisory digital environments for intelligent and energy-efficient building operations. As buildings are designed, constructed, and operated for long-term periods with uncertainties, it is essential to model, verify, extend, and calibrate the digital twin models in situ over the building life cycle, unlike digital twin modeling in the manufacturing industries. Therefore, this study proposes in situ model fusion techniques for building digital twinning, which include (1) model coupling and (2) model assembly. The first model coupling technique enables nonintrusive in situ verification and calibration for prediction models without the model observations (Y). The second model assembly enables in situ modeling or a more accurate model construction by connecting the verified models through the model coupling technique to the input layer of the target model. In the case studies conducted in a central heating system, the prediction model for the secondary-side return water temperature was modeled and calibrated without relying on the model observation data (Y), thanks to the model coupling technique. The nonintrusive model showed an accuracy with a root mean square error (RMSE) of 0.53 °C, which was comparable to the intrusive gray-box models (around 0.5 °C RMSE) calibrated using intrusive datasets (Y). Additionally, the prediction model could improve the benchmark model's performance from 0.44 °C to 0.26 °C (RMSE) using the model assembly technique. The in situ model fusion would enable and enhance nonintrusive modeling approaches, providing a reliable and extensive model environment for digital twins in the building sector.

Fault data seasonal imbalance and insufficiency impacts on data-driven heating, ventilation and air-conditioning fault detection and diagnosis performances for energy-efficient building operations

The heating, ventilation and air-conditioning fault impacts vary with different seasonal climatic conditions, but the fault data may not be available under some seasons in real buildings due to the frequency and span of fault occurrences. This study evaluates the fault detection and diagnosis (FDD) performance differences of the proposed convolutional and recurrent neural networks under limited seasonal fault data scenarios and an ideal scenario covering climatic conditions from multiple seasons. The fault and normal data were gathered from fault simulations using a verified prototype building EnergyPlus model and two real fault datasets. Four different data experiments based on the simulated dataset were implemented to assess FDD performance differences, and two sets of further experiments based on each real fault dataset were conducted to verify the findings from previous experiments. The results show that the FDD architectures, trained on sufficient fault data under a certain season(s), indicate poor generalization ability to identify faults under unseen seasons. Moreover, the coverage of fault data under different seasons is more crucial in enhancing FDD performances than the amount of fault data under each season. These findings will help researchers consider this practical issue when evaluating new or existing data-driven FDD methods.

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings.

Prediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings’ eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R2) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R2, which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption.

Potentials and limitations of direct air capturing in the built environment

This concept study presents an approach for resolving the trade-off between energy-efficient building operation and the provision of hygienically harmless indoor air quality. A novel coupling of HVAC-systems (heating, ventilation and air conditioning systems) with DAC-technology (direct air capturing technology) is proposed to separate CO2 in the exhaust air of buildings and recirculate the CO2-depleted air back into the building. In a mainly theoretical approach, the corresponding potentials and limitations of the novel HVAC/DAC-coupling in recirculation mode are evaluated. For that purpose, CO2-loads in the feed and exhaust air of four buildings located in Germany were measured using calibrated non-dispersive infrared (NDIR) sensors with pyroelectric detection principle. Subsequent numerical model simulations resort to typical meteorological data as well as building operation parameters grouped in different scenarios. The measurement and simulation results were assessed with regard to: (i) the unique possibilities of a HVAC/DAC-coupling in recirculation mode for the improvement of indoor air quality, (ii) the energy saving potentials through reduced air conditioning requirements enabled by a HVAC/DAC-coupling in recirculation mode, and (iii) the potential allocation of CO2 separated from building exhaust air for energetic and/or material reutilization in decentralized systems. In conclusion, a HVAC/DAC-coupling in recirculation mode can not only reduce the energy demand of buildings but also facilitates access to unutilized CO2-resources transported in the built environment and additionally offers the potential to improve indoor air quality. However, a suitable DAC module for operation in indoor air is not yet commercially available.

Investigating the thermal performance and Overall Thermal Transfer Value (OTTV) of air-conditioned buildings under the effect of adjacent shading against solar radiation

Building energy conservation regulations are formulated for governing the design and operation of energy-efficient buildings. Overall Thermal Transfer Value (OTTV) is one of the performance-based methods for governing the design of energy-efficient building envelope. OTTV is a measure of average heat gain transmitted into a building through the building envelope. In the current OTTV regulation, any shading effect cast by adjacent buildings against solar radiation is excluded from the OTTV calculation. This may result in an inaccurate OTTV calculation. The objective of this study is to develop an appropriate approach for calculating the OTTV of buildings under adjacent shading effect against solar radiation. Firstly, 14 multi-block building developments constructed in Hong Kong were used as a case study. Through building energy simulations and analysis, it reveals that the discrepancy in OTTV calculation between the building cases with and without adjacent shading effect taken into consideration is very substantial; and the percentage difference can be up to 53.48 %. Moreover, 491 existing commercial buildings were identified and selected. Through computer modelling and simulations, a correlation between annual heat gain through building envelope and the corresponding OTTV has been developed. With this correlation, the OTTV of a building under shading effect cast by adjacent buildings can be properly evaluated. Moreover, parametric simulations have been conducted to identify the dominant factors that influence the adjacent shading effect of a building. It is envisaged that the methodology developed in this research work can be applied to other cities with OTTV regulation being implemented, for fulfilling the regulatory requirement on energy-efficient building envelope design.

Operational Load Forecasting Using Machine Learning Algorithms

Function of Building control and analytics, as well as grid-interactive and energy-efficient building operations, rely on building load prediction. Furthermore, electric transmission lines must be sized for a specific maximum power, and overloading will lead towards blackouts or electrical mishaps. As we move closer to a smart grid system, short-term load forecasting has become even more important due to the increasing share of temperature dependent renewable energy sources. . In this work, we proposed a load forecast through the decomposition of the time series using python programming. Many computational models were compared with three different regression models, and the results exhibited the proposed work have improved results with enhanced accuracy levels compared with existing works along with way paves to solve peak load record problem.

Grey emission targets for municipalities based on material cadastres

The building stock contributes significantly to greenhouse gas (GHG) emissions. Considerable progress has been made in the energy-efficient operation of buildings, thus the importance of grey emissions is increasing and will become a future field of action for municipalities as well. So far, there is poor knowledge regarding quantities, fields of actions and impacts. This paper is addressing this gap by applying a bottom-up material flow analysis (MFA) approach on the domestic building stock of Hamburg, and by calculating material-induced grey emissions of alternative building stock compositions as well as impacts of technical and social innovations. Overall, the stock induced grey emissions amount to 41.9 million metric tons of CO2 equivalent. Impact with regard to technical innovations concerning GHG reduction is rather limited. In contrast, impact achieved by ambitious social innovation is quite significant. This means that technical innovations must be considered in a more radical way and merged with social innovations.

Impact assessment of air velocity on thermal comfort in composite climate of India

Thermal adaptation plays crucial role in energy efficient operation of buildings without compromising with human thermal comfort. Elevated air velocity is commonly desired to restore comfort requirements at higher temperatures especially in NV and MM buildings located in tropical countries like India. This article is the systematic field study comprising a total of 4872 responses (1874 from NV buildings and 2998 from MM buildings) collected over a period of six years (2011–2017) during summer and moderate seasons under composite climate of Jaipur (India). Subjects’ responses and concurrent field measurements were utilized to investigate the impact of elevated air velocity on indoor thermal comfort. This research is a first attempt of its kind that deals with graphical quantification of air velocity required to offset increased temperature and follows similar approach as presented in ISO 7730. A redefined air velocity offset chart for Indian subjects working in office buildings has been proposed using the evidences (i.e. comfort expectations, preferences and local adaptation) collected from actual field observations. Thus, the offset in comfort operative temperature from base value of 28.04 °C and 26.93 °C for NV and MM buildings were obtained to be 4.78 °C and 4.24 °C, respectively for the elevated air velocity of 1.5 m/s.

First results of remote building characterisation based on smart meter measurement data

In European households, 79% of the energy is consumed for space heating and cooling. The remote detection of possible retrofitting targets can help to increase the renovation rate and hence contribute to the realization of the 2000 W society. Here, a new method to characterize buildings based on smart meter monitoring data and a simplified physical simulation model is presented. The aim of this method is to estimate the time dependent demand of heating energy based on weather data applying these simplified building models. The method has been successfully applied on simulation and real-world smart meter monitoring data. The annual space energy demand was excellently reproduced with a deviation of less than 1% and 8% for simulation and real-world buildings, respectively. The recovered relevant building parameters deviate less than 1% for the reference case. The successful application of the algorithm on in-silico and real-world data monitoring data indicates the vast potential of this automated modelling technique on heat load prediction and energy-efficient operation of buildings. Furthermore, the derived heat demand profile may help utilities and facility managers in the future to identify better operation schedules of small areas and districts.

Distributed model predictive control of building energy systems coupled to geothermal fields

In building energy systems (BES), operation strategies are a key factor to exploit the full potential of renewable energy sources and thus to reduce the CO2 emission and energy costs. However, advanced operation strategies like model predictive control require high engineering effort, which limits the applicability. To ease the implementation of model predictive control in BES, we present a flexible and robust distributed control framework. The framework supports the use of different model types and timescales for the various subsystems, allowing the user to tailor models from white to black modelling according to the available system knowledge. In a case study, we apply this framework to control a real-life industrial hall. We further simulate a case in which the hall is supplied by a geothermal field to investigate the sustainable long-term control of the field. We vary the temperature setpoint of the hall and adjust the thermostats in the office rooms. Since the set temperature of the hall and offices differs, the model predictive control framework finds an economic trade-off between the requirements of those two zones. Judging from these results, the approach and our implementation could be an important contribution to energy efficient operation of buildings with renewable energy sources.

Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis

Retrofitting building envelopes is regarded as an effective solution that can help commercial and individual investors offset daily power usage. However, it is worthwhile to explore a highly efficient approach to seek optimum retrofitting strategies. A novel hybrid approach that integrates energy simulation, Orthogonal Array Testing (OAT), and Data Envelopment Analysis (DEA) is developed in this research to discover optimal solutions for building retrofit. A commercial high-rise building is chosen as a case study, and five parameters are considered, including the exterior envelope fabric, exterior window type, sunshade type, window-to-wall ratio, and airtightness. The energy consumption is first simulated and verified as a baseline. OAT is then employed to conduct experiments and explore potential solutions to the energy optimisation problem, based on which the most efficient strategy is obtained through DEA benchmarking. The identified optimal solution is able to save an annual operation energy of 7.01 kWh/m2, which is also cost-effective. It is also found that the window type and airtightness are significant factors with regard to the energy performance of building envelope retrofit. The study benefits designers and construction managers in determining the optimal solution of retrofitting building envelope for achieving energy-efficient building operations.

Rapid Internet of Things (IoT) prototype for accurate people counting towards energy efficient buildings

According to the U.S. Department of Energy, a significant portion of energy used in buildings is wasted. If the occupancy quantity in a pre-determined thermal zone is aware, a building automation system (BAS) is able to intelligently adjust the building operation to provide “just-enough” heating, cooling, and ventilation capacities to building users. Therefore, an occupancy counting device that can be widely deployed at low prices with low failure rate, small form-factor, good usability, and conserved user privacy is highly desirable. Existing occupancy detection or recognition sensors (e.g., passive infrared, camera, acoustic, RFID, CO2) cannot meet all these above system requirements. In this work, we present an IoT (Internet of Things) prototype that collects room occupancy information to assist in the operation of energy-efficient buildings. The proposed IoT prototype consists of Lattice iCE40-HX1K stick FPGA boards and Raspberry Pi modules. Two pairs of our prototypes are installed at a door frame. When a person walks through this door frame, blocking of active infrared streams between both pairs of IoT prototypes is detected. The direction of human movement is obtained through comparing occurrence time instances of two obstructive events. Thus, the change in occupancy quantity of a thermal zone is calculated and updated. Besides, an open-source application user interface is developed to allow anonymous users or building automation systems to easily acquire room occupancy information. We carry out a three-month random test of human entry and exit of a thermal zone, and find that the occupancy counting accuracy is 97%. The proposed design is completely made of off-the-shelf electronic components and the estimated cost is less than $160. To investigate the impact on building energy savings, we conduct a building energy simulation using EnergyPlus and find the payback period is approximately 4 months. In summary, the proposed design is miniature, non-intrusive, ease of use, low failure rate, and cost-effective for smart buildings.

Quality Improvement of Specialists Training for Energy-Efficient Construction

The paper explores the issues of improving the quality of training in the field of energy-efficient construction. The analysis of the development of energy-efficient construction and the regulatory framework of the Russian Federation in this area was carried out. The analysis of the data on the job market in the construction industry, the results of employers’ survey, and the experience in the implementation of educational programs in Russian universities showed the need to improve the quality of training in the field of energy efficient construction of buildings. The findings from the employers’ questionnaires revealed their interest in further cooperation in the implementation of educational programs. The example of the Master’s program “Design, construction and operation of energy efficient buildings” shows the stages of creating an educational program from the perspective of a competence-based approach. The main approaches to the formulation of learning outcomes by combining the requirements of federal state educational standards, professional standards and the questionnaire findings are considered. The methodology for assessing the quality of the educational program in the field of energy efficient construction is proposed.

Distributed exergy-based simulation-assisted control of HVAC supply chains

Generic model-assisted control algorithms for building energy systems achieve an energy-efficient building operation while the implementation effort is lower than in the case of a taylor-made automation system. In this paper, a generic distributed simulation-model-assisted and exergy-based control algorithm is presented, which is applicable to typical building energy systems (BES). It exploits dynamic nonlinear simulation models from an open-source Modelica library and makes use of sequential neighbor communication. In the supply chains of a BES, the algorithm performs multiple optimizations of small subsystems by considering pre-defined values of pre-defined coupling variables. The objective function is the sum of the exergy destruction and loss, which is based only on measurements without additional information such as monetary costs. In a simulation case study, an open-loop and a closed-loop version of the algorithm are compared to a control system based on rules and feedback controllers that was optimized manually for this particular scenario to provide a benchmark. The tracking error of the closed-loop algorithm is close to the benchmark. The open-loop algorithm achieves an energy consumption close to the benchmark. These results show that the algorithm finds suitable solutions automatically whereas the rule-based control system cannot be expected to perform equally well if boundary conditions were changed.

Energy-Efficient Data Collection Scheme for Environmental Quality Management in Buildings

How to efficiently collect sensory data for supporting energy-efficient operation of buildings has become a great challenge, especially for large-scale networks in buildings. In this paper, a spatio-temporal compression-based optimized clustering scheme is proposed for energy-efficient environmental data collection in buildings. In the scheme, first, according to the establishment of a cluster, an adaptive dynamic cluster head selection method for prolonging the lifetime of sensory nodes in buildings is developed. Meanwhile, to further reduce energy consumption, we construct the dynamic optimal quantity of the cluster heads solution method to achieve minimum energy consumption. Moreover, the spatio-temporal correlations of sensory data are explored in the data sampling and transmission processes, which can decrease the amount of data transmission and further extend the lifetime of the entire data collection network. The proposed scheme provides sensing data with high quality for environmental quality management in buildings and supports energy-efficient building operation. Finally, the simulation results show that the proposed scheme obtains obvious advantages in energy consumption compared with other related schemes, and the lifetime of the proposed scheme is also longer than others when it maintains high reconstruction precision.

Realising Operational Energy Performance in Non-Domestic Buildings: Lessons Learnt from Initiatives Applied in Cambridge

The gap between the intended and actual energy performance of buildings is increasingly well documented in the non-domestic building sector. Recognition of this issue has led to the availability of a large range of initiatives that seek to ensure energy efficient building operation. This article reviews the practical implementation of three such initiatives in a case study building at the University of Cambridge. The notionally high-performance office/laboratory building implemented two voluntary design frameworks during building planning and construction: the voluntary rating scheme BREEAM and a bespoke Soft Landings framework called the Cambridge Work Plan. The building additionally meets the energy reporting criteria for the EU Energy Performance of Buildings Directive (EPBD), a legislative requirement for many publicly owned buildings in the UK. The relative impact of these three approaches for optimising building energy performance is reviewed through a mixed methods approach of building occupant and operator interviews, document analysis and energy performance review. The building’s core functions were revealed to consume 140% more energy than the building logbook estimate for the same needs. This difference, referred to widely as the energy performance gap, is larger than the majority of reported UK university buildings in the energy reporting database CarbonBuzz. The three implemented initiatives are demonstrated to be inadequate for reducing the energy performance gap in the case study, thus a number of alternative energy efficiency approaches are additionally reviewed. Common to the three approaches used in the case study is a lack of verification of actual building performance despite ambitious sustainability targets, due to a heavy focus on the design-stage and few follow-up mechanisms. The paper demonstrates the potential of energy efficiency initiatives that are focussed on operational performance as a core criterion (such as the Living Building Challenge) together with those that ensure the creation of realistic energy estimates at the design stage (such as the Chartered Institution of Building Services Engineers (CIBSE) Technical Memorandum 54).