Personalised Retrofit Decision Support Tools For Uk Homes Using Smart Home Technology

Summary on Grant Application Form
Thermal efficiency retrofit options, appliance upgrades and on-site renewables represent a significant opportunity to deliver energy demand reductions to UK homes. The potential to reduce thermal heat losses through insulation and airtightness (in particular in pre-1980s housing), upgrade the household appliance stock (using the latest energy saving models) and integrated on-site renewables and microgeneration (developing a 'prosumer' culture and reducing energy bills) still remains largely unrealised. There are a number of challenges in providing advice for retrofit solutions to consumers which will promote behaviour change and influence purchasing decisions. Currently consumer information is based on standardised methodologies for nominal house types and the resulting predictions of energy savings have minimal resemblance to reality where the thermal efficiency of the dwelling, efficiency of heating system and appliances, occupancy, user behaviour and preferences will have a significant impact on the effectiveness and uptake of retrofit measures. One solution is to provide consumers with personalised, accurate and trustworthy predictions of energy saving measures which are calibrated and tailored to their dwelling and living patterns, presented in a format to engage and promote action.

This proposal will facilitate a widespread uptake of retrofit measures in UK homes by implementing a holistic approach to providing consumers with personalised, tailored retrofit advice delivered using methods to maximise consumer engagement. Smart Home technology provides a unique opportunity to use real-time measurements, advanced data analytics, digital signal processing and communications techniques, novel visualisation, semantic web and cloud computing technologies to generate advice at different levels of abstraction for informed and justified decision making. The Smart Home concept is currently gaining significant momentum and new developments in open systems, simple use and installation features (ie plug and play), mobile access (ie Smart Phones) and connectivity have brought the concept to the attention of energy companies, ICT companies and appliance manufacturers. The IBM vision of a Smart(er) Home gives three characteristics: 1) Instrumented (sensors and automation of household activities); 2) Interconnected (communication between devices and wider networks - allowing remote access and control of devices); and 3) Intelligent ('the ability to make decisions based on data, leading to better outcomes'). Smart Homes provide consumers with more control over their homes and energy systems and, importantly, how their energy demand and costs can be reduced through interventions.

This proposal brings together a multi-disciplinary team of building, ICT, energy, design and user experts to develop a personalised decision support platform for building envelope retrofits, heating system and appliance replacement purchases, and on-site renewables integration. This will deliver a step-change in the provision and accuracy of retrofit advice to UK householders leading to a low-energy and low-carbon future housing stock. The outcomes will be of benefit to: energy, ICT, embedded systems and telecommunication companies developing technology and business models for Smart Home services; consumers to lower their energy bills and improve the safety, security and comfort of their homes; building component, boiler and appliance manufacturers developing the next generation of low-energy products; and policy makers for new insights into innovative approaches to meeting the security, affordability and carbon reduction aspirations of the UK energy system.

The role of Strathclyde within this multi-disciplinary consortium is to focus on the residential sector of the smart grid, specifically the role of smart meters in informing and enhancing current/future energy efficiency mechanisms, enabling innovative pricing mechanisms as well as empowering customers with advanced services that facilitate energy savings, energy efficiency and grid friendliness. We attempt to answer questions such as "How can signal information processing turn smart meter data into 'useful' information?", "How much and what kind of data do we need for effective data analytics?" and "How to map technologies to domestic practices to increase the impact of this 'useful information' feedback?"

The research supported by this award led to a unique dataset of time-stamped power load measurements, at household and appliance levels. The dataset contains readings from 20 houses in England monitored for a continuous period of about two years as the households went about their daily lives. This is the only such UK dataset at this longitudinal scale with a sampling rate below 1min, that is, sampling is carried out at 8 second resolution which is similar to the load measurements from the UK smart meters being rolled out, via the Consumer Access Device. The dataset is now publicly available, and the data collection, cleaning of the data and overall description are described in Nature Scientific Data Journal paper. The dataset has already attracted the attention of many academic research groups as well as the energy and smart home industry. Additionally, the dataset, non-intrusive appliance load monitoring (NILM) and appliance mining methods developed have yielded additional research directions, e.g., activity recognition where energy consumption is quantified through the lens of activities, load-shifting (exploiting flexibility in time-of-use of appliances to manage peak demand), retrofit advice and smart home automation.

The team has successfully tackled the challenges of energy disaggregation, termed Non-Intrusive appliance Load Monitoring (NILM) of low-granularity, one dimensional smart-meter data via the development of a range of low complexity algorithms for a large range of appliances. These NILM algorithms formed the basis of further analytical work developed in the group, namely informing appliance retrofit/upgrade decisions, predicting demand from appliances, finding opportunities for load shifting and developing activity recognition algorithms that map appliances or technologies to activities in order to understand households’ daily routines. The latter challenge of how to interpret real-time energy data in terms of activities from smart meter data is tackled in conjunction with our social science partner, showing the effectiveness of jointly considering quantitative and qualitative data through mixed methods. In summary, we have demonstrated that, from smart meter data alone, we can generate itemised billing down to appliance level or down to activity level and extracting the time-use profile and energy-profile of any particular activity, such as cooking, laundering or washing, in a particular household and across households and what are the cost implications.
http://dx.doi.org/10.15129/31da3ece-f902-4e95-a093-e0a9536983c4

Our findings answer the following questions:
• How can signal information processing turn low resolution smart meter data into meaningful information?
• Can we develop consistently accurate and practical non-intrusive disaggregation solutions from low-resolution smart meter one-dimensional data?
• How much do activities, such as cooking and energy services like refrigeration, account for in a household’s total electricity consumption?,
• Can we generalise when energy-intensive activities occur within households of similar occupancy?
• Which are the largest energy-consuming activities, and what are the implications for demand management and feedback?
Load disaggregation is seen as the next step towards providing effective energy feedback. Load disaggregation providers supply energy disaggregation through a combination of hardware submetering and software analysis. However, these solutions are currently limited to disaggregating high loads and industry is keen to adopt approaches that can operate at Smart Meter data rates, are practical, simple, accurate, and robust for a range of training periods. Our new algorithms are good candidates for future industry uptake.
Our activity recognition method provides an alternative, less expensive option that does not depend on the traditional approach of time diaries, for generating daily time-use profiles of a subset of energy using activities, which can then be compared with national time-use statistics to identify variability, or to segment households. This is attractive for service providers in the Smart Home market to provide value-added feedback to users.
Finally, automatic activity recognition is an important enabler of home automation and effective home energy management systems, as well as Assisted Daily Living with further implications for remote healthcare.