Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building’s long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively.

The energy transition is a complex problem that requires a comprehensive and structured approach to policymaking. Such an approach is needed to ensure that transition pathways and policies enable greener energy alternatives whilst ensuring prosperity for people living in the region and limiting environmental degradation to the local ecosystem. This paper applies a qualitative approach based on systematic literature research and review analysis to identify and analyse previous work within this interdisciplinary field in order to understand the complexity of energy transitions and identify key variables and sub-sectors that need to be addressed by policymaking. The paper then looks at the problem from a regional level and uses the Tees Valley region in North East England as a reference case for the energy system and potential proposed policies for the energy transition. A system dynamics methodology was employed to help visualise and emphasise the major complexity of the energy transition and the challenges that policymaking needs to tackle for the successfully enable implementation and application of the energy transition policies. The results of this study identified that in relation to the Tees Valley energy system, its development and transition towards decarbonisation, the major challenge for the policymakers is to ensure that proposed policies foster growth in job creation without leading to job losses within the local employment market.

The issue of interoperability in the Architecture, Engineering, and Construction (AEC) industry represents a challenge on a scale that spans across the project life cycle. This is predominant in the infrastructure sector that usually comprises a more versatile Operations and Maintenance (O&M) phase in comparison with the buildings sector. To this end, an important stage in the information life cycle is the asset information capture and validation during product procurement at the O&M phase. The water industry in the United Kingdom relies on Product Data Templates (PDTs) to fulfil such task, which is usually an error prone manual process. This paper presents an ongoing research, which investigates the application of Semantic Web Technologies (SWT) for improving product data exchange during product procurement at the O&M phase for the water industry in the United Kingdom (UK). Therefore, focus group sessions with industry experts were held to discuss current inefficiencies and solution requirements. Based on these results, a semantic common model named Asset Specification Ontology (ASO) was developed to capture and validate asset information during product procurement at the O&M phase. The common model (ontology) is based on available technologies, namely Web Ontology Language (OWL) and Shapes Constraint Language (SHACL). This gives the advantage of semantically rich data which can be linked and queried in a meaningful way to facilitate the exchange and validation of water industry assets’ data. The uniqueness of this paper is manifested in the issue it tackles, as efficient product procurement, and hence, data exchange in the water industry is an industrial challenge that is seldom researched. Results from the focus group sessions showed that information exchange within the UK water industry is impeded due to the lack of structured and semantic data. However, for a robust semantic interoperability, there needs to be a robust semantic data infrastructure, which would require semantic mappings from standards to product properties, from standards to other standards, and from standards to dictionaries. These conclusions were further supported by the common model, which was created from existing schemas, standards, and dictionaries. Generally, this paper recommends a common model/product library for phase-specific product data exchange in the water industry.

The management and the identification of design changes constitute an essential part of the of a design flow within the architecture, engineering and construction (AEC) industry, requiring the formalisation of a multi-disciplinary collaborative information modelling environment. Construction projects generate substantial amount of change information, which needs to be updated continuously throughout the process, from initial feasibility study to the decommissioning of facilities. Complications arise from the information storage in multiple incompatible file formats that can lead to the loss or omission of details. In addition to any unexpected changes, the mismanagement of information is another factor leading to delays and costly errors. In order to mitigate such issues, this paper proposes to integrate the Industry Foundation Classes (IFC) data model and Natural Language Processing (NLP) to validate and visually identify the result of change requests. The system is developed using C# by 1) integrating angular framework with ASP.NET (Active Server Pages) to create a dynamic single web page and 2). Using X-BIM toolkit that supports IFC format to read, create and visualise the BuildingSmart Data Models (aka IFC Models). This approach enables a web-based platform capable of generating reports and visual previews, highlighting the differences between IFC files throughout the design processes. A web-based system prototype allows users to compare subsequent versions of IFC design models in terms of additions, modifications and deletions. The prototype uses NLP to intelligently identify the changes that have been made as it compares newer and older versions of the same model, making this information available to designers and 3D modellers. Prospective work will focus on the application of artificial intelligence (AI) to automate the implementation of changes within the construction models.