The energy drivers use to charge their Electric Vehicles (EVs) comes from various sources. Of those, some are renewable green energy sources such as solar photovoltaic systems (SolarPV) and home storage battery whilst some are called brown sources such as gas turbine, coal and oil. To analyse the behaviour of EV drivers as to how they use household green and brown energy, separating the mix is a necessity. This paper argues that the Internet of Things (IoT) can be helpful in achieving that goal and demonstrates a pilot study showing the process of separating the green and brown energy from the EV charging.

Choosing the right technologies to build an urban-scale IoT system can be challenging. There is often a focus on low-level architectural details such as the scalability of message handling. In our experience building an IoT information system requires a high-level holistic approach that mixes traditional data collection from vendor-specific cloud backends, together with data collected directly from embedded hardware and mobile devices. Supporting this heterogeneous environment can prove challenging and lead to complex systems that are difficult to develop and deploy in a timely fashion. In this paper we describe how we address these challenges by proposing a three-tiered DevOps model which we used to build an information system that is capable of providing real-time analytics of Electric Vehicle (EV) mobility usage and management within a smart city project.

In this paper we address the gap that exists between designer and implementor when prototyping lighting schemes of an Internet-connected light bulb. We achieve this by implementing a simple domain specific language (DSL) which helps abstract the underlying light bulb API which by design is overloaded with functionality resulting in unnecessary complexity. The goal of the language is to help design complex lighting schemes that reflect the levels of solar energy stored within batteries deployed within the homes' of participants of a solar energy study.

This workshop explores how data literacy impacts on learning analytics both for practitioners and for end users. The term data literacy is used to broadly describe the set of abilities around the use of data as part of everyday thinking and reasoning for solving real-world problems. It is a skill required both by learning analytics practitioners to derive actionable insights from data and by the intended end users, such that it affects their ability to accurately interpret and critique presented analysis of data. The latter is particularly important, since learning analytics outcomes can be targeted at a wide range of end users, some of whom will be young students and many of whom are not data specialists.Whilst data literacy is rarely an end goal of learning analytics projects, this workshop aims to find where issues related to data literacy have impacted on project outcomes and where important insights have been gained. This workshop will further encourage the sharing of knowledge and experience through practical activities with datasets and visualisations. This workshop aims to highlight the need for a greater understanding of data literacy as a field of study, especially with regard to communicating around large, complex, data sets.

Roof-mounted photovoltaic (PV) generation is becoming more prevalent within the domestic setting. Recently battery systems have enabled households to store excess self-generated electricity for subsequent use. However the associated user-interfaces and displays can be hard to understand, potentially preventing households from optimizing their solar usage. This paper introduces a known method being deployed in a new context. It reports on on-going research that investigates the effect of in-home ambient light displays linked to the home battery system. The paper covers the design stage and potential feedback solutions to raise awareness and influence consumer behaviour to promote energy conservation. An Ambient Light System is proposed to enable better user feedback. The study outlines the design recommendation for an ambient light display to be used in an energy consumption context. Using such a display, households can optimize use of low Carbon solar energy within the home, thus minimizing grid electricity usage.

In this paper we describe CASIK, a Culturally Adaptive Sustainable Information Kiosk that has been designed for deployment within institutions that have a requirement for kiosks that can cater to users from a range of cultural backgrounds. We show how a ubiquitous technology such as sound can be used as a method of interaction between high and low-end phones and an Internet-connected kiosk based around a Raspberry Pi, monitor, and microphone.