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There has been general recognition within the construction industry that there is a discrepancy between the amount of energy that buildings actually use and what designers considered that they should use. This phenomenon is termed “The Performance Gap” and is normally associated with new buildings. However, existing and older buildings contribute a greater amount of operational carbon. In response to the Performance Gap, CIBSE have developed the TM54 process which is aimed at improving energy estimates at design stage. This paper considers how the TM54 process can also be used to develop energy management procedures for existing buildings. The paper describes an exercise carried out for a university workshop building in which design energy use has been compared with the actual building energy use and standard benchmarks. Moreover, a sensitivity assessment has been carried out using different scenarios based on operation hours of building/equipment, boiler efficiency and impact of climate change. The analysis of these results showed high uncertainty in estimates of energy consumption. If carbon challenges are to be met then improved energy management techniques will require a more systematic approach so that facilities managers can identify energy streams and pinpoint problems, particularly where they have assumed responsibility for existing buildings which often have a legacy of poorly metered fuel consumption. [ABSTRACT FROM AUTHOR]

This paper investigates factors associated with variation in daily total (electricity and gas) energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for two nested sub-samples of 1418 and 682 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel. Linear mixed effects modelling resulted in adjusted R2 between 63% and 80% depending on sample size and combinations of contextual data used. Increased daily energy consumption was significantly associated (p -value < 0.05, VIF < 5) with: households living in buildings with more rooms and bedrooms, that are older, more detached, have air-conditioning, and experience colder (more heating degree days) or less sunny weather; households with more adult occupants, more children, older adult occupants, higher heating temperature setpoints, and that do not try to save energy. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data, which has recently been updated to include over 13,000 households from across Great Britain. This paper provides guidance on appropriate methods to use when analysing the data. [ABSTRACT FROM AUTHOR]

Short-term building energy consumption forecasting is vital for energy conservation and emission reduction. However, it is challenging to achieve accurate short-term forecasting of building energy consumption due to its nonlinear and non-stationary characteristics. This paper proposes a novel hybrid short-term building energy consumption forecasting model, SSA-CNNBiGRU, which is the integration of SSA (singular spectrum analysis), a CNN (convolutional neural network), and a BiGRU (bidirectional gated recurrent unit) neural network. In the proposed SSA-CNNBiGRU model, SSA is used to decompose trend and periodic components from the original building energy consumption data to reconstruct subsequences, the CNN is used to extract deep characteristic information from each subsequence, and the BiGRU network is used to model the dynamic features extracted by the CNN for time series forecasting. The subsequence forecasting results are superimposed to obtain the predicted building energy consumption results. Real-world electricity and natural gas consumption datasets of office buildings in the UK were studied, and the multi-step ahead forecasting was carried out under three different scenarios. The simulation results indicate that the proposed model can improve building energy consumption forecasting accuracy and stability. [ABSTRACT FROM AUTHOR]

• New building development projects underperform often. • Underperformance leads to increased energy consumption and CO 2 emissions in building operation. • The paper links building project development and building operational performance. • Two simulation methods are combined in a multi methodology framework in the paper. • The paper explores the implications of project partner collaboration for operational building performance in the case of a recent building project. The "performance gap" in the United Kingdom construction industry is a persistent problem as new building development projects underperform more often than not. The "performance gap" is partially attributed to the number of stages involved in building project development and the coordination difficulties of partners with different incentives. The project outcome is important for energy consumption, carbon emissions and occupant well-being. Thus, it is important to study the project management process in terms of the standard time, cost and quality parameters, but also in terms of project partner incentives and coordination, and the subsequent energy performance and resultant indoor environmental conditions. A system dynamics model of project management processes is developed to explore the implications of partner coordination for building quality. The system dynamics model is coupled to a building performance simulation model to explore building energy consumption and Indoor Environmental Quality, and apply this on a recent building project case study. Results show that greater project partner alignment can reduce annual energy consumption up to 12% and CO 2 emissions up to 37%, with greater emphasis in the design stage of the project subject to resource availability. The trade-offs involved on value appropriation are considered and discussion of results points to possible ways for improvement. [ABSTRACT FROM AUTHOR]

• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

• A switchable photovoltaic window based on thermotropic hydrogel is developed. • Selected hydrogel can reversibly switch between highly transmissive and scattering states. • The window performance is evaluated by indoor and outdoor experiments. • This window can passively control daylighting and heat gain during hot sunny days. • This window offers better electrical performance than conventional PV glazing. Daylighting control technologies have become an essential part of sustainable building design to reduce overheating, glare and energy consumption in buildings. In this paper, a smart glazing system where a Building Integrated Photovoltaic (BIPV) glazing is coupled with an optically switchable thermotropic hydrogel layer is proposed to improve the daylighting control and electricity generation performance of traditional BIPV glazings. Thermotropic (TT) hydrogels made of various weight percentage combinations of hydroxypropyl cellulose (HPC) polymer, gellan gum and sodium chloride (NaCl) salt were synthesised and first evaluated by visible-near-infrared spectroscopy. Subsequently, small-scale prototypes of the proposed BIPV thermotropic (BIPV-TT) laminated glazing based on these TT hydrogels were fabricated and characterised experimentally under controlled laboratory conditions. The TT hydrogel, which was synthesised of 6 wt% HPC, 0.5 wt% gellan gum and 4.5 wt% NaCl, was selected for further experimental characterisations in a dynamic outdoor environment, due to its appropriate transition temperature of 30.7 °C for use in mild climates with a wide modulation range of solar transmittance from 85.8% (in the transparent state) to 9.6% (in the light-scattering state). The outdoor tests were conducted in Nottingham, the UK, on typical summer days with sunny and partial cloudy conditions. The results showed that using the prototype BIPV-TT laminated glazing can reduce up to 80% of the solar radiation transmitted into the outdoor test cell, while providing up to 12% higher electrical power outputs, compared to its counterpart system with no thermotropic hydrogel applied. [ABSTRACT FROM AUTHOR]

Multisource energy data, including from distributed energy resources and its multivariate nature, necessitate the integration of robust data predictive frameworks to minimise prediction error. This work presents a hybrid deep learning framework to accurately predict the energy consumption of different building types, both commercial and domestic, spanning different countries, including Canada and the UK. Specifically, we propose architectures comprising convolutional neural network (CNN), an autoencoder (AE) with bidirectional long short-term memory (LSTM), and bidirectional LSTM BLSTM). The CNN layer extracts important features from the dataset and the AE-BLSTM and LSTM layers are used for prediction. We use the individual household electric power consumption dataset from the University of California, Irvine to compare the skillfulness of the proposed framework to the state-of-the-art frameworks. Results show performance improvement in computation time of 56% and 75.2%, and mean squared error (MSE) of 80% and 98.7% in comparison with a CNN BLSTM-based framework (EECP-CBL) and vanilla LSTM, respectively. In addition, we use various datasets from Canada and the UK to further validate the generalisation ability of the proposed framework to underfitting and overfitting, which was tested on real consumers' smart boxes. The results show that the framework generalises well to varying data and constraints, giving an average MSE of ∼0.09 across all datasets, demonstrating its robustness to different building types, locations, weather, and load distributions. [ABSTRACT FROM AUTHOR]

Energy usage in buildings is coming increasingly under the spotlight as carbon policy focus shifts towards the utilization of thermal energy. In the UK, heating and hot water accounts for around 40% of energy consumption and 20% of greenhouse gas emissions. Heating is typically produced onsite, making widescale carbon or energetic improvements challenging. District heating networks (DHNs) can offer significant carbon reduction for many users but can only be implemented if the end user buildings have good thermal energy efficiency. This greatly limits the ability to implement advancing 4th and 5th generation DHNs, which are the most advanced systems available. We elucidate the current state of thermal efficiency in buildings in the UK and provide recommendations for necessary building requirements and modifications in order to accommodate 4th and 5th generation district heating. We conclude that key sectors must be addressed including creating a skilled workforce, producing relevant metrics and benchmarks, and providing financial support for early stage design exploration. [ABSTRACT FROM AUTHOR]