Data Science and Artificial Intelligence for Sustainability MSc

Cranfield University

The sustainability industry has been experiencing growing challenges driven by decarbonisation, examples include the increasing difficulties in balancing energy systems caused by the penetration of uncertain and less controllable renewable generation. Nevertheless, the widespread installation of measurement and control units have enabled innovations in data analytics, especially is using AI to support planning and operation in sustainability industry, which effectively addresses the challenges. The scientific and technical concepts of machine learning and AI methods/tools and their potential advantages in the sustainability sector will be taught in this module. The module aims to provide the students with data analytical skills from machine learning and AI technology, and evaluate the advantages/disadvantages of their applications in the sustainability industry. Additionally, the module aims to provide students with essential skills (e.g. computer programming and coding in Python) for applying machine learning in resolving practical problems.

This module will introduce you to data analytics, overview challenges and solutions in using data analytical tools to solve sustainability problems. The module will present the principles of data ethics, data governance and regulation required to ensure societal good. The lectures and practical sessions will present approaches for knowledge discovery, covering exploratory data analysis, data cleaning, visualisation, statistical methods, and optimisation methods. Students may benefit from knowledge of basic programming and concepts of statistics methods.

In the context of rising household energy demands, concerns for energy security, threat of climate change, and uncertainties in the price of energy (the so-called ‘energy trilemma’) require transformation of the ways in which energy is produced, delivered and consumed. Both for the developed and developing economies challenges stem from meeting increasing electricity demands from more intermittent renewable resources. This module covers a comprehensive overview of crucial aspects within energy policy and the economic principles that underpin energy markets. Our approach is holistic, examining the objectives of energy policy, regulatory aspects of energy markets, and a wide array of policy strategies and tools. We also delve into significant facets of the energy sector, placing a particular emphasis on renewable energy and the economic dynamics of energy supply.

Geographical information is increasingly prevalent in our daily life, affecting personal leisure activities as much as business services and the workplace. Geographical information represents a key theme in environmental management and it has been estimated that some 80% of the data used for environmental, business and policy-oriented decision-making is geographical in nature. Such spatial data requires a structured approach in its management if the maximum benefit is to be derived from analysis and dissemination. This module provides a solid introduction to the issues concerning the management of spatial information and the tools to do so, with a predominant focus on ESRI’s software solutions.

Writing code opens-up new approaches to creative problem solving and allows us to move beyond the limitations of any particular software. This module aims to develop your skills and confidence to write your own code in the Python programming language and access a broad ecosystem of packages and tools that comprise the foundations of Data Science.

Environmental impact assessment and life cycle analysis are important tools for evaluating the sustainability of complex renewable energy technologies and industrial processes or products. The tools and concepts taught in this module will enable you to assess the sustainability of a case study from an environmental standpoint. Analysis of relevant case studies to demonstrate the assessment process, including how to account for uncertainty and sensitivity analysis.