Villas are a very common building typology in Abu Dhabi. Due to its preponderance in residential areas, studying how to effectively reduce energy demand for this type of building is critical for Abu Dhabi, and many similar cities in the region. This study aims to show the impact of proposed energy efficiency measures on a villa using a calibrated model and to demonstrate that to be accurate, the model must be driven using urban weather data instead of rural weather data due to the significance of the urban heat island effect. Available data for this case study includes construction properties, on-site (urban) weather data, occupancy-related loads and schedules and rural weather data. Four main steps were followed, weather data customisation combining urban and rural weather variables, model calibration using a genetic algorithm-based tool and simulating retrofit strategies. We created a calibrated model for electricity demand during 2016–2017 with a 4% normalized mean bias error and an 11% coefficient of variation of the mean square error. Changing from none to all retrofit strategies results in a 34% reduction in annual energy consumption. According to the calibrated model, increased urban temperatures cause a 7.1% increase in total energy consumption.

Potential solutions for unlocking operational flexibility from data centres are investigated. Two types of data centres, in terms of their cooling facilities, are modelled in order to provide an in-depth assessment of potential variations in data centre performance based on their energy consumption and server temperatures. Using synthetic datasets generated from year-long building dynamic simulations under distinct system conditions, four machine learning (ML) models are designed and trained to predict data centre energy consumption and server temperatures. Three different ML algorithms are considered, including random forest, XGBOOST, and multiple linear regression, which all achieve high accuracy ranging from 93.8% to 98.1% for the mean absolute percentage error. The resulting ML models are then utilised to represent a system-wide fleet of data centres, which are integrated within a power system unit scheduling framework for potential demand shifting based on (system) carbon intensity. Five alternative flexibility scenarios are introduced and compared, with the objective of determining the achievable flexibility from a system-wide portfolio of data centres, focusing on concerns relating to server temperatures. On average, a $6.5\%$ load reduction is seen to be achievable for a winter day during periods of high CO ₂ intensity.

IMPRESS is a H2020 funded project that has developed prefabricated façade panels to reduce building energy demand. In order to accelerate and optimise the retrofit process, IMPRESS has developed an iterative design methodology (IDM), which incorporates all stages of the design-construct-install-operate process and aims to bring energy efficiency as early as possible in the design process. Three software tools have been developed for this purpose: (1) an online decision support software (DSS), to inform decision-making on which panel type is suitable for the building; (2) an interoperable data exchange server (IDES) to allow exchange of information related to the design, construction, installation and operation of the facade; and (3) an online management platform (OMP) for coordination through all stages. The merger of the design methodology and software tools is called IMPRESS BIM methodology (iBIMm) which enables design teams to make informed decisions based on building information models and provides a platform to monitor its performance during the operation stage.

Measurement and verification (M&V) is the process of quantifying energy savings originated by one or several energy conservation measures (ECM) in an existing building. The estimation of the savings consist of comparing actual energy consumption to the adjusted baseline model. This paper focuses on comparing three approaches for creating baseline models: linear, symbolic regression (SR) and extreme gradient boost (XGBoost); and discusses the advantages and drawbacks on each of them from a practitioner’s perspective. In this paper, these approaches are assessed qualitatively and quantitatively. The qualitative assessment compares the type of model output, interpretability and calibration time. Linear model excels in all three criteria whereas the XGBoost is the worst option for model output and interpretability. SR model is the worst performing in terms of calibration time, but intermediate model for output and interpretability. Quantitative assessment is done through the quantification of prediction errors in 367 buildings after being calibrated with hourly data for a 12-month period. The XGBoost model has the highest prediction accuracy in terms of CVRMSE. The linear model performs significantly well in terms of NMBE. SR preforms well in terms of CVRMSE and has the best median NMBE overall remaining as the most accurate interpretable option. The results show different benefits and drawbacks of each approach and the implementation of SR model for this application is the main innovation of this paper.

Implementation of cost-effective energy conservation measures (ECMs) is expected to generate up to 18% of carbon emissions reductions in office buildings. In order to determine adequate ECMs for a specific building, operational data is required. However, buildings generally lack operational data in the form of time series that can limit a breath of analysis required for determining adequate ECMs. Energy time-series data is commonly lacking in the UK due to uneven availability of smart meters (heat, gas, water), security restrictions in Energy Information Systems (EIS) and building management systems (BMS), restrictions and costs associated for automated reporting from utility companies, etc. This work presents a non-intrusive computer vision-based reader to generate energy readings at 10-minute resolution using a Raspberry-Pi, a traditional webcam and an LED light. OpenCV, an open source computer vision library, is used to detect and interpret numeric values from a heat meter, which are in turn uploaded to a cloud-based energy platform to create a complete operational data set enabling detailed analytics, fault detection and diagnostics (FDD) and model calibration. A case study of an office building in Scotland is presented. The building has a heat meter with no remote access capabilities. The accuracy of the method, i.e. the ability of the script to accurately derive the rate of change between readings, resulted on a 92% percent during a test done for 100 samples. Recommendations for accuracy improvements are included in the conclusions.