This module defines the characteristics of infrastructure projects, programmes and plans of various kinds and scales in different sectors, with much emphasis...

Infrastructures as Agents of Change (BPLN0024)

This module defines the characteristics of infrastructure projects, programmes and plans of various kinds and scales in different sectors, with much emphasis on mega investments. The course examines their roles as agents of change and the different decision-making roles of various stakeholders in their planning, appraisal and development. . It encompasses an understanding of past perspectives of the role of such investments in different international contexts, both in the developed and developing world, and investigates 21st Century perspectives in a context of global interdependencies of concerns about equity, economic growth, governance and environmental impacts, as sustainability concerns loom large as key challenges.

The dissertation allows the future lighting specialist to demonstrate a deep understanding of the lighting knowledge base. It involves the preparation and...

MSc Light and Lighting Dissertation (BENV0137)

The dissertation allows the future lighting specialist to demonstrate a deep understanding of the lighting knowledge base. It involves the preparation and submission of a 10,000-word report on a topic associated with sustainability and light and lighting in the built environment.

Applied Statistics|Artificial Intelligence|Building|Energy and Environment

MACHINE LEARNING|SMART BUILDINGS|PERFORMANCE PREDICTION|ENERGY MANAGEMENT|BUILDING SYSTEMS

The module focuses on the applications of Machine Learning towards improving building operation.

Through a series of case studies, this module will introduce...

Machine Learning in Smart Buildings (BENV0119)

The module focuses on the applications of Machine Learning towards improving building operation.

Through a series of case studies, this module will introduce students, to applications of machine learning and the potential of such models to making buildings smarter. The case studies will draw upon applications in areas like occupant modelling, performance prediction, building services and their control.

Through the lens of these case studies relevant machine learning algorithms and tools will be presented to provide grounding on:

Machine learning model-development basics (hyperparameters, validation sets, overfitting, underfitting)
	Regression (e.g. Support Vector Machine, Gaussian Processes)
	Clustering (e.g. k-means clustering)
	Reinforcement learning
	Advanced topics (deep neural networks, convolutional neural networks)