Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

Whilst unavoidable, inspections, progress monitoring, and comparing asplanned with as-built conditions in construction projects do not readily add tangible intrinsic value to the end-users. In large-scale construction projects, the process of monitoring the implementation of every single part of buildings and reflecting them on the BIM models can become highly labour intensive and error-prone, due to the vast amount of data produced in the form of schedules, reports and photo logs. In order to address the mentioned methodological and technical gap, this paper presents a framework and a proof of concept prototype for on-demand automated simulation of construction projects, integrating some cutting edge IT solutions, namely image processing, machine learning, BIM and Virtual Reality. This study utilised the Unity game engine to integrate data from the original BIM models and the as-built images, which were processed via various computer vision techniques. These methods include object recognition and semantic segmentation for identifying different structural elements through supervised training in order to superimpose the real world images on the as-planned model. The proposed framework leads to an automated update of the 3D virtual environment with states of the construction site.

This paper presents a new, multi-objective method of analysing and optimising the energy processes associated with window system design in office buildings. The simultaneous consideration of multiple and conflicting design objectives can make the architectural design process more complicated. This study is based on the fundamental recognition that optimising parameters on the building energy loads via window system design can reduce the quality of the view to outside and the received daylightboth qualities highly valued by building occupants. This paper proposes an approach for quantifying Quality of View in office buildings in balance with energy performance and daylighting, thus enabling an optimisation framework for office window design. The study builds on previous research by developing a multi-objective method of assessment of a reference room which is parametrically modelled using actual climate data. A method of Pareto Frontier and a weighting sum is applied for multi-objective optimisation to determine best outcomes that balance design requirements. The Results reveal the maximum possible window to wall ratio for the reference room. The optimisation model indicates that the room geometry should be altered to achieve the lighting and view requirements set out in building performance standards. The research results emphasise 2 the need for window system configuration to be considered in the early design stages. This exploratory approach to a methodology and framework considers both building parameters and the local climate condition. It has the potential to be adopted and further refined by other researchers and designers to support complex, multi-factorial design decision-making.

There have been numerous simulation tools utilised for calculating building energy loads for efficient design and retrofitting. However, these tools entail a great deal of computational cost and prior knowledge to work with. Machine Learning (ML) techniques can contribute to bridging this gap by taking advantage of existing historical data for forecasting new samples and lead to informed decisions. This study investigated the accuracy of most popular ML models in the prediction of buildings heating and cooling loads carrying out specific tuning for each ML model and using two simulated building energy data generated in EnergyPlus and Ecotect and compared the results. The study used a grid-search coupled with cross-validation method to examine the combinations of model parameters. Furthermore, sensitivity analysis techniques were used to evaluate the importance of input variables on the performance of ML models. The accuracy and time complexity of models in predicting heating and cooling loads are demonstrated. Comparing the accuracy of the tuned models with the original research works reveals the significant role of model optimisation. The outcomes of the sensitivity analysis are demonstrated as relative importance which resulted in the identification of unimportant variables and faster model fitting.

Non-domestic buildings contribute 20% of the UK's annual carbon emissions. A contribution exacerbated by its ageing stock of which only 7% is considered new-build. Consequently, the government has set regulations to decrease the amount of energy take-up by buildings which currently favour deep energy retrofitting analysis for decision-making and demonstrating compliance. Due to the size and complexity of non-domestic buildings, identifying optimal retrofit packages can be very challenging. The need for effective decision-making has led to the wide adoption of artificial intelligence in the retrofit strategy design process. However, the vast retrofit solution space and high time-complexity of energy simulations inhibit artificial intelligence's application. This paper presents an energy performance prediction model for non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic buildings energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using