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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.

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.

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.

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