Domestic microgeneration is the onsite generation of lowand zero-carbon heat and electricity by private households to meet their own needs. In this paper we explore how an everyday household routine -that of doing laundry -can be augmented by digital technologies to help households with photovoltaic solar energy generation to make better use of self-generated energy. This paper presents an 8-month inthe-wild study that involved 18 UK households in longitudinal energy data collection, prototype deployment and participatory data analysis. Through a series of technology interventions mixing energy feedback, proactive suggestions and direct control the study uncovered opportunities, potential rewards and barriers for families to shift energy consuming household activities and highlights how digital technology can act as mediator between household laundry routines and energy demand-shifting behaviors. Finally, the study provides insights into how a "smart" energy-aware washing machine shapes organization of domestic life and how people "communicate" with their washing machine.

Abstract-This paper reports results from an empirical study that explored the potential of interactive electricity DemandShifting -a particular form of behavior change where electricity consumption is shifted towards times of the day when production is at its highest -in the context of residential solar electricity generation. We conducted an in-the-wild user study with eighteen households over a period of six months, combining electricity data from smart meters and smart plugs with in-depth interviews to explore laundry routines and washing machine usage for each household. The study highlights that, although washing machines can be considered to be "shiftable appliances", the specific ways in which each household goes about shifting varies considerably. Furthermore, there is a clear need for ICTs to support people by analyzing current context and future plans to automate electricity demand-shifting and synchronization of both laundry and other appliances. Such support will help them maximize the self-consumption of micro-generated electricity while balancing this with environmental concerns, financial benefits and day-today issues relating to convenience.

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With the advance of the Internet and the Internet of Things, an abundance of 'big' data becomes available. Data science can be incorporated in design, which brings forward various opportunities for designers to benefit from this new material. However, the designer's perspective and their role remains unclear. How do they think about and approach data? What do they want to achieve with this data? What do they need to take ownership of designing with data? In this paper we take a design perspective to map the opportunities and challenges of leveraging large data-sets as part of the design process. We rely on a survey with 75 participants across a Faculty of Industrial Design Engineering and in-depth reflective interviews with a subset of 9 participants. We discuss the impact of data on the roles designers can adopt as well as an approach to designing with data. This work aims to inform on educational support, data literacy and tools needed for designers to take advantage of this new era of design digitalisation.

Recent advances in sensing and networking technologies, namely the Internet of Things (IoT), have become key enablers of data-intensive design processes. However, the recent introduction of the General Data Protection Regulation (GDPR) in Europe has raised concerns that the GDPR might hamper data- intensive design processes. In this paper, we map the challenges of enabling ethical and compliant design of product-service systems with personal IoT data. Specifically, we present a 4-year project led by EON, an international energy provider, to design innovative home energy systems that leverage emerging technologies such as solar panels, electric vehicles and home batteries. We present our 6-stage approach to design, centred on IoT data. We highlight the barriers of responsible design with data and identify three novel trust principles for compliant use of personal IoT data in design (private-by-default, analytics transparency and Accountable analytics).